a novel hybridization of abc with cbr for pseudoknotted rna structure

8/8/2019 A Novel Hybridization of ABC with CBR for Pseudoknotted RNA Structure

http://slidepdf.com/reader/full/a-novel-hybridization-of-abc-with-cbr-for-pseudoknotted-rna-structure 1/11

(IJCSIS) International Journal of Computer Science and Information Security,

Vol. 8 , No. 8 , 2010

A Novel Hybridization of ABC with CBR for

Pseudoknotted RNA Structure

Ra’ed M. Al-Khatib, Nur’Aini Abdul Rashid and Rosni Abdullah

School of Computer Science

Universiti Sains Malaysia USM

Penang, Malaysia

[email protected], {nuraini, rosni} @ cs.usm.my

Abstract— The RNA molecule is substantiated to play

important functions in living cells. The class of RNA with

pseudoknots, has essential roles in designing remedies for

many virus diseases in therapeutic domain. These various

useful functions can be inferred from RNA secondary

structure with pseudoknots. Many computational intensive

efforts have been emerged with the aim of predicting the

pseudoknotted RNA secondary structure. The computational

approaches are much promising to predict the RNA structure.

The reason behind this is that, the experimental methods for

determining the RNA tertiary structure are difficult, time-

consuming and tedious. In this paper, we introduce ABCRna, a

novel method for predicting RNA secondary structure with

pseudoknots. This method combines heuristic-based

KnotSeeker with a thermodynamic programming model,

UNAFold. ABCRna is a hybrid swarm-based intelligence

method inspired by the secreting honey process in natural

honey-bee colonies. The novel aspect of this method is adapting

Case-Based Reasoning (CBR) and knowledge base, two

prominent Artificial Intelligence techniques. They are

employed particularly to enhance the quality performance of the proposed method. The CBR provides an intelligent

decision, which results more accurate predicted RNA

structure. This modified ABCRna method is tested using

different kinds of RNA sequences to prove and compare its

efficiency against other pseudoknotted RNA predicted methods

in the literature. The proposed ABCRna algorithm performs

faster with significant improvement in accuracy, even for long

RNA sequences.

Keywords- RNA secondary structure; pseudoknots; Case-Bases

Reasoning; Artificial Bee Colony (ABC) algorithm.

I. INTRODUCTION

Ribonucleic acid or (RNA) is one of the nucleic acids,which plays diverse roles and functions. Basically, one kindof RNA is the messenger RNA (mRNA). It works as anintermediary in carrying the genetic information code fromDNA to make proteins [1]. This carried genetic code is usedin the natural process for synthesizing proteins in living cell.However, the recent biological studies confirmed that thereare other kinds of RNAs, which play various useful roles [2].The latest discovered functions of RNA molecule, include:splicing introns, catalyst for reaction and a regular in cellular

activities [3, 4]. Predicting the RNA structure is the key todetermine and scrutinize the active functions of RNAmolecule. This fact is emphasized by central dogma inbiochemistry and biology research domain [5, 6]. The RNAsecondary structural outputs provide the base for shaping the

RNA three-dimension (3D) structure, which is the first stepof the RNA tertiary structure phase.

The importance of the computational methods forpredicting RNA secondary structure has been acknowledgedas a demanding research area, by computer scientists. Also,there are many conditions, facing the experimental methodsthat are used by biologists [7, 8]. The Nuclear MagneticResonance (NMR) and X-ray crystallography are the twopopular experimental purification methods that are used todetermine the RNA 3D spatial structure [9, 10]. Lateststudies confirmed that many classes of RNA moleculebroadly fold in the pseudoknot motif [11, 12]. Whereas, theRNA structural functions of pseudoknot elements, have beenemphasized to be prominent for medical processes and

designing anti-viral treatments, in therapeutic research [13].Consequently, the computational RNA prediction methodsfor predicting the RNA secondary structures are extensivelyutilized with manageable efforts [14].

The RNA molecules come in two main shapes: the Stem-loop and the Pseudoknots, as illustrated in Figure 1 in termsof RNA structure classifiers [15]. The Stem-loop is a non-crossing RNA structure motif. While, the Pseudoknots is acrossing RNA structure, which plausibly has been spotted by[16]. Further, the pseudoknotted RNAs has been proven toplay several vital roles. From complexity points of view, thetop prediction methods of RNA without pseudoknotsfunctional element are MFold [17] and Vienna [18]

algorithms which execute with complexities O(n3

) in timeand O(n

2) in space. PknotsRG [19] is one of the most proper

algorithm for predicting RNA with pseudoknots. It requiresO(n

4) and O(n

2) in time and space complexities, respectively.

Even if the pseudoknotted RNA secondary structureprediction problem has been stated as Non-deterministicPolynomial time (NP)-Complete problem [20, 21], it is aninsisted matter to be solved [22, 23], in recent years.

In order to overcome the prediction problem of RNAsecondary structure with pseudoknots, this article introduces

School of Computer Sciences, University Sains Malaysia Penang, Malaysia.

289 http://sites.google.com/site/ijcsis/ISSN 1947-5500




Vol. 8 , No. 8 , 2010

a nature-inspired hybrid method called “ABCRna”.Innovatively, this approach combines a new derivation fromArtificial Bee Colony (ABC) algorithm with a specialdeterministic constraints [24]. On top of this, it is borrowedfrom the Artificial Intelligence (AI) field, which is a kind of nature swarm-intelligence [25]. The objective of thisproposed method is to build the entire RNA secondarystructure with pseudoknots from a given single-strandedRNA primary sequence. Indeed, this proposed method is acombination of KnotSeeker (heuristic-based method [3])with UNAFold (a dynamic programming method [26]) forsolving the RNA structural related issue. This hybrid methodis a new derivation from ABC algorithm. It adapts theinspired swarm-based intelligence behavior of the honey-bees in collecting nectar and converting that to honey androyal jelly [27]. Naturally, every individual worker bee visitsmany flower patches during the round-trip of collectingnectar and pollen. Then it goes back to the hive to submit themixed nectar to the nurse bee. Finally, the nurse bee startsmaking honey by a natural biological secreting process.

Intuitively, the proposed RNA structural hybrid method

is deployed and built to solve the related pseudoknotted RNAbioinformatics problem. By a deeper understanding of theCBR technique [28], the proposed hybrid model obtains aglobal optima RNA structural assurance results with moreaccuracy and better performance. Finally, the results showthat the ABCRna method significantly improves theexecution time and the accuracy in both sensitivity andspecificity. This improvement when comparing the outputswith the other pseudoknotted RNA prediction methodsexisting in the state-of-the-art like; FlexStem [29], HotKnots[30] and PknotsRG [19].

The remainder of this article is ordered as follows: In thenext section, we start with describing the secondary structure

of the RNA molecule, in computer context representation. Insection 3 background materials, gives a concise expression tothe generic ABC optimization method. Then, a derivation of

Figure 1. A stem-loop and pseudoknots of RNA structures types.

ABC is adapted to generate the proposed method. Next, theCBR as a modern AI technique, is extensively and widelydiscussed, from theoretical concept. Section 4 presents theproposed method with the implemental mapping betweenpseudoknotted RNA secondary structural prediction and thesecreting process of making honey. Subsequently, thefollowing section reports the comparative benchmark of theproposed method. The results of ABCRna is comparingagainst the results of other RNA prediction methods in theliterature. Finally, the article ends with conclusion remarks,in section 6.

II. SECONDARY STRUCTURES OF RNA

A. RNA Stem-Loop (non-pseudoknots)

The single-stranded RNA molecule forms many foldedstructures in hierarchal shape; the primary RNA singlesequence, the secondary structure of RNA molecule, thethree-dimensional (3D) or tertiary RNA functional structureand the quaternary structure for RNA polymerase [31].

Generally, the RNA computational methods predict thesecondary structure of the given RNA primary sequence.Thus, the RNA secondary structure defines: as an RNAstructural motif, which in some parts includes the double-stranded motifs. These parts joined by complementary andcanonical base pairings with the other parts, which are thenon-paired single bases. The double-stranded motif partscoming in several shaped of stem-loops: hairpin, internal (orinterior ), bulge, multi-branch external bases and stacking (orhelices) loops. As explained above and illustrated in Figure2, the RNA primary sequence (RNA bases) folds and joinson itself in real RNA secondary structure by hydrogenchemical bonds for low energy and more stability [15]. Inmathematical and computational representation concept, the

various layers of RNA structures can be defined as follows:• b = b1, b2, …, bi, …, bn, where b is an RNA primary

sequence and bi is the RNA base or nucleotide [32, 33].The element bi is also a member of set which includes{‘ A’,’C ’,’G’,’U ’,’ N ’}. While, the first four alphabets arerepresentation of the original paired bases (paired-nucleotides) of the real RNA molecule: Adenine, Cytosine,Guanine and Uracil, respectively. The last nucleotide ‘ N ’is assigned to the non-paired base. Such that the n is thelength of the given RNA sequence and 1 ≤ ≤ .

• S ={(bi, b j)}, such that (bi, b j) belongs to the canonical basepairs. S is the secondary structure of the given RNAprimary sequence which satisfies the following conditions:

- (bi, b j) ∈ {( A,U ), (U , A), (G,C ), (C ,G), (G,U ) ,(U ,G)},these are the sets of RNA base-pairs. While, the basepairs include in the set { A-U , U-A , G-C , C-G} is aWatson-Crick RNA base-pairs [34], the set { A-U , U-A}is a Wobble RNA base-pair [35].

- Then S = {(bi, b j): 1 ≤ < ≤ and − > },where is a threshold constant number depend onthe limit length of the minimum un-paired bases in astem-loop (hairpin, stem or bulge ... etc). The istypically taken to be equal three.

290 http://sites.google.com/site/ijcsis/

ISSN 1947-5500




Vol. 8 , No. 8 , 2010

- If (bi, b j) ∈ S, (bk , bl) ∈ S and if bi = bk , then b j = bl. Thisimplies (bi, b j) = (bk , bl). In another words, every base(nucleotide) in RNA secondary structure make join byhydrogen bond at most with another one base (non-tripleor only allow one-to-one).

- If (bi, b j) ∈ S, (bk , bl) ∈ S and < , this can include twolocation elements in RNA stem-loop structure (non-

pseudoknots): If < < < , then the two base pairs are form a

type of nested location elements (nested-fashion), asdepictured in Figure 3 a.

If < < < , then the two base pairs are form atype of juxtaposed location elements (juxtaposed-fashion) [36], as shown in Figure 3 b.

B. RNA with Pseudoknots

The majority of RNA molecule classes fold in functionalstructural elements called pseudoknots. Indeed, they belongto the (3D) tertiary structure element and perform an

important useful roles and constructive functions [37].The pseudoknots substructure can theoretically satisfy the

following term. If there are two base pairs (bi, b j) and (bk , bl),then satisfy the conditions: < < < or < < < ,as shown in Figure 3 c and d. These two base paired shapesare represented the pseudoknots RNA structural elements. Inanother word, the pseudoknots is a crossing sub-structuralfunctional element in the RNA molecules. It formsinteraction the unpaired bases part of the stem-loop, whichfolds back and join in a loop region located outside thatstem-loop.

In spite of the prediction algorithms of RNA withpseudoknots structural elements, have been proven to be NP-

complete problem [21]. It is a demanding research areabecause of the pseudoknotted RNAs has importance as keyfunctions. Further it plays essential roles in viral and cellularregulatory [38].

Figure 2. Different RNA element shapes, the image is adapted from [39].

Figure 3. The diagrammatic position relation between different types of RNA base pairs. (a) two base-pair in juxtaposed fashion. (b) two base-pair in

nested fashion. (c)&(d) two base-pair in pseudoknots.

III. BACKGROUND MATERIALS

A. Problem Statement of RNA with Pseudoknot

Pseudoknotted RNA secondary structure is the problemof predicting its secondary structure from a given primarysequence. Particularly, it has recently become attractiveresearch area. Due to that the RNA with pseudoknots, hasmany important and useful roles, which needs to be solvedcomputationally [40]. The existing pseudoknotted RNAprediction algorithms perform in exponential timecomplexity. The best prediction method run, in the worstcase, O(n

4) in time and O(n

2) in space [19]. Thus they run

very slowly and need an ever increasing memory-space,especially for long sequences. Veritably, this means that theprediction solving algorithms of the pseudoknotted RNAsecondary structural problem, suffer from long executiontime and storage complexities. To the best knowledge of the

authors, the final structural results suffer from poor qualityand inaccuracy, for long RNA sequences.

The pseudoknots class of the RNA structural predictionissue, has been proven an NP-complete problem [20].Increasingly, the collecting nectar to make honey is aninspired field for the bioinformatics researchers, which isderived from the original ABC model [24]. In this article, anew hybrid method as a sub-area of swarm intelligenceapproaches for solving the pseudoknotted RNA structuralproblem is adapted. Besides that the CBR as a modern AItechnique highlighted a way to be deployed, in term of enhancement the final results of the proposed hybridABCRna model. From comparison points of view, we find

this method improved the accuracy of the RNA structuraloutputs with good performance.

B. Swarm-Intelligence in AI Technology

Swarm Intelligence (SI): is an emergent and bioinspiredfield of AI, which has been generated from numerousresearches in social insect’s behavioural models [41]. Thephrase swarm comes up to present solution to overcome theoptimization problems. These optima solutions have beensuccessfully got by utilizing the co-operative and


ISSN 1947-5500




Vol. 8 , No. 8 , 2010

coordinative efforts among the worker-insects. Theinspiration of the swarm intelligence is gained from manysocial insects behavioral models like; honey-bees colony andant-colony. For instance in bee-colony, the objective of theswarm is the quantity and quality production of honey by themutual teamwork. It is a key fact that, the amount of honeythat an individual worker-bee harvests is worthless. But, thehoney production by all worker-bees is considerably muchbetter than the crop of an individual one [42].

Lately, swarm intelligence has obtained high interest tobe adapted by many researchers from diverse fields. The listcompromises, but it is not limited of: engineering, scienceand commerce fields. The computer researchers proposeswarm intelligence optimization methods to solve manycomplex problems that suffer from severe drawbacks. Thetypical research domain of the computational swarmintelligence is to solve many real-world problems. Someapplications of swarm intelligence in a development areas asfollows: (i) The routing optimization in differentcommunication network [43]. (ii) The job scheduling [44].(iii) The swarm control in the Unmanned Aerial Vehicles

(UAV) for both civil-military purposes [45, 46].

C. Honey-Bee Colony Structure

Many social insects live in colonies have instinctualability to perform as agents in a group for solving complexproblems and to complete their tasks. The new AIdisciplinary “swarm-intelligence” has been attractivelyproduced by deep knowledge of the biological swarm insolving the problems. This can done by a behavioralinteraction among thousands members of the swarm-insects[47]. Naturally, the social insects have talent to be in self-organized behavioral models for achieving an intelligencesolution of the vital tasks.

Honey-bees live in a well structured social insect’scolony called a hive. The hive typically is a composition of asolo queen, drones and workers [48]. Each one does thefollowing roles: (i) As usual, there is one queen. She is egg-laying, female as a mother for other colony members andmates one time in her lifelong by drones. (ii) There aredrones or male bees as bee-colony fathers. Their mainresponsibility is fertilizing the new queen in a mating flightparty (social gathering) before dying. They live at most sixmonths and reach to hundreds up to several thousands duringthe summer season. (iii) There are around 10,000 in winter to60,000 in summer female worker-bees (foragers) in eachbee-colony. They do many important jobs including:collecting nectar to make food, raising and bringing up thebroods and larvae’s, guarding and ventilating the hive. But,the primary resourceful task of the worker-bees is collectingthe nectars and pollens from the flower patches (foragefield). Later, when they back to the hive the worker beessecret the honey and royal jelly (food).

D. Honey-bee Collecting Nectar (Foraging)

Honey-bees collecting nectar process to make honey is tobe considered as an optimization swarm-based intelligence

approach [49]. The worker-bees perform the collectingnectar and secreting honey process in a well-organizedbehavioral model known as bees foraging process [50]. It isobvious that, this gigantic task is beyond the ability of everyworker-bee individually. Nevertheless, all the groupmembers interact among each other in a fashion to solve thecollective bee-foraging problem.

The main incentive task in bee’s colony is the foraging(collecting nectar to make honey). To investigate the beeforaging process Seeley in [51], introduced a detailedsystematic mechanism. It is about the self organizedhoneybee’s social behavioral model in collecting forage, asshown in Figure 4. In the proposed system, every worker bee(forager) visits many flowers from the same type within 30to 120 minutes of foraging trip. All the collected nectars,from these flower patches, have been stored in the foragerhoney stomach. Besides that, the forager commits severalactions to provide a feedback. Waggle dance is providing theprofitability rating of nectar in the flower patches, the odor,location and other required information [52, 53].Accordingly, the making honey and royal jelly process startswhen the worker-bee back to hive from the foraging round-trip journey.

Soon after reaching the hive from the foraging trip, thefield bee (forager) gears up to submit that nectar, whichalready stored in her honey sac [54]. This process of submission the gathered nectar to the house bee (nurse bee)is accomplished in a regurgitated behavior. The role of thehouse bee is converting that nectar to honey or royal jelly(bee food) in a secreting process. In this synthesizing honeyprocess, the main work is to split the complex sucrose sugarinto fructose and glucose, which are simpler sugars andpredominant in honey. This sucrose-splitting process isperformed by adding the invertase, which is a specialenzyme, to the nectar from the hypopharyngeal gland in thehead of bee. Then, the new synthesized honey or royal jellyis spread out in a honey comb cells. The house bee exposesthis secreted honey as a thin film to aware of the lastfiltration. This final step was done by increasing the surfacearea, to insure the fast evaporation of water in the well-donehoney. Finally, the filled honey comb cells sealed and cappedby propolis (plant gum), which is an adhesive material. Thiswaxy cover prevents the honey from the bacterial attacks orin case of prevention the stored food to avoid thefermentation.

Consequently, here the details of the foraging process arepresented to make a base for our nature-inspired method. It is

a hybrid adaptation from the process of honeybees incollecting nectar to make honey and royal jelly. Theproposed ABCRna method solves the secondary structureprediction problem of RNA with pseudoknots. The idea isstimulating a hybrid novelty swarm-intelligence approachfrom collecting nectar and making honey in the naturalsecretion process. ABCRna as a new optimization algorithmis based on the main features of a hybrid between twoheuristic-based method KnotSeeker [3] and dynamicprogramming algorithms UNAFold [26].


ISSN 1947-5500




Vol. 8 , No. 8 , 2010

Figure 4. A secreting honey process: the simulation of honeybeescollecting nectar to secret honey and royal jelly mapping with a

diagrammatic representation of the proposed method.

E.

CBR and KBIt’s commonly known that the AI research area provides

many methodologies and technologies for solving complexproblems, which the CBR is one of them. Recently, the CBRhas been successfully used to restore solution for a newproblem based on expertise by retrieving the similar maturesolutions of the past problems [55]. Originally, CBR comesup from the cognitive science and the human expertise toretain and retrieve the information. In another word in CBRmethod, the people solve the new problem by recalling howthey solved the past similar problems. The CBR methodincludes a problem solving cycle with four main activities:Retrieve, Reuse, Revise and Retain [56]. According to theFigure 5, in the heart of this four-RE’s cycle there is a case-

library as a Knowledge Base (KB). This KB is used inretrieval action to assess an intelligent decision of the similarcases for revising the final outputs by retrieving the mostcorrect solutions.

By referring to the adhere of exact matching concepts,the CBR is a generic AI methodology in problem solving[57]. In the proposed ABCRna method, the CBR is deployedas a modern AI inspired technique with KB to augment theresult in retrieval steps. The role of CBR is finding thecurrent pseudoknotted RNA sub-structure with the exactmatching from KB. The KB holds and clusters all realpseudoknotted RNA sequences and their known nativestructures. If the retrieval one has pseudoknots in itssecondary structure, then the CBR chooses the current one.This CBR comparing process, enhances the quality of thepredicted pseudoknotted RNA secondary structure.Moreover, it is deployed significantly to be an alternativedevelopment technique for solving the secondary structureprediction problem of RNA with pseudoknots.

F. Preliminarly in Optimization

According to the theoretical viewpoint, the optimizationmethods are branch of the applied mathematics and basically

Figure 5. The Case-Based Reasoning (CBR), a modern ArtificialIntelligence methodology, adapted from [55].

compromise from two main classes of algorithms;deterministic and probabilistic. Figure 6 shows the general

category of the global optimization methods to clear therelation among all their characteristics. Definitely, thedeterministic algorithms are a type of algorithm which take aset of fixed inputs and produce a fixed result. While, theheuristic is a single assumption works as a search strategy ortechnique in problem-solving. It is based on intelligence andexperience, which can be applied loosely in computerimplementation [58]. The meta-heuristic is based-on severalassumptions work as an optimizer to improve a series of candidate solutions to reach to the final problem solving.Also it may use the many trials iteratively. In 2001, Geem et al. introduced the Harmony Search (HS) algorithm, whichwas a new meta-heuristic algorithm based on natural-inspiredphenomena behavioral models [59]. The HS has been

developed from mimicking the natural phenomena of themusicians improvisation (music players). Severalexperiments proved that the HS as a meta-heuristicalgorithm, is capable to solve the optimization problems withmore improved performance. The result makes the HS as adurable meta-heuristic algorithm in solving the NP-completeproblems. The Traveling Salesman Problem (TSP) is anexample of NP-problem which was solved by HS [60].

Now the main question, Is it feasible to develop a hybridmeta-heuristic algorithm for building the pseudoknottedRNA structure with good performance and more accurateresult? To do this an optimized swarm-based intelligencealgorithm would be inspired as a kind of stimulation from the

Artificial Bee Colony (ABC) algorithm [61]. This inspiredproposal utilizes the ABC to solve the related issue of RNAstructure in bioinformatics. Moreover, the Particle SwarmOptimization (PSO) is a distinguished swarm-basedintelligence algorithm that models some animal socialbehavior like fish schooling or swarm of honey-bees [62].PSO has been proposed by Kennedy in 1995 and has reachedto be an interesting area of knowledge to exploit fordeveloping a new meta-heuristic algorithm by mimickingand inspiring the natural phenomena of animals and colonyinsects.


ISSN 1947-5500




Vol. 8 , No. 8 , 2010

Figure 6. A schematic diagram of the global optimization methods.

IV. PROPOSED METHOD

This section explains in details, the new hybrid of derivedABC algorithm to overcome the pseudoknotted RNAsecondary structure prediction problem. The proposed hybridABCRna method is inspired from the swarm-intelligencesocial behavioral model of honey-bees in collecting nectarand secreting honey, as shown in Figure 7. Hence, theauthors develop ABCRna as a hybrid method in a simpleway to build the secondary structure of RNA molecule withpseudoknots. The following sub-sections demonstrateseparately the paradigms of designing the proposed method.These sub-sections describe the mapping of the all featuresbetween the ABC optimized algorithm and the RNAstructural prediction problem. The final computational resultsof ABCRna for RNA structure reveal an optimized better

performance and more accuracy in terms of sensitivity andspecificity. Its computer code implementation shows lessspace and time complexities when comparing with otherstate-of-the-art methods in solving such RNA predictionproblem.

Here, the researchers underline the hybrid adaptionmodel as a new derivation from ABC algorithm to solveRNA prediction problem. It is a first threshold further opensthe door in front of the other bioinformatics researchers tofollow. Furthermore, it gives immense opportunity to expandthis proposed optimizer in solving such kind of complex bio-computing problems. This is why the AI material already haspresented in the background section to be a general guidance.

A. Honey-bee Foraging Algorithm

The innovative ABC as a swarm-based intelligencealgorithm was deployed particularly based on the honeybeenatural social behaviours. A few other algorithms have beenderived by inspiring the honeybees swarm behavioral model,intelligently [61]. Many researchers have been adapted suchthis swarm collective behaviours to solve optimizationcombinatorial problems. Herein, we describe a new hybrid

Figure 7. Workflow of the ABCRna approach for predicting thepseudoknotted RNA secondary structure, some parts adapted [55].

algorithm called “ABCRna”, which is derived from theoriginal ABC algorithm [24]. It is developed as a hybridadaptation between ABC models with deterministicconstraints and inspired by the intelligence social behavioursof bees in collecting nectar to secret honey. The proposedmethod is applied to solve the pseudoknotted RNAsecondary structure prediction problem, which is a kind of combinatorial NP-complete problem [20].

The bees in colony deliberated for collecting nectar andsecreting honey and they compromise in three bee groups:employed bees, unemployed bees (onlookers or scouts) andnurse bees, plus the food sources (flower patchesprofitability). The first two groups of honeybees (employedand unemployed) search for the last part which is the richfood sources. The third bee component takes the collectedforage (nectar) from the first two groups by process of regurgitation. After that, the nurse bee starts making honeyand royal jelly by a popular secretion honey process. Thebehavioral steps of the bees to carry out the forage collectingprocess, has been shown in Figure 4. Naturally, it can bedescribed as follows:

a) Employed bee (Forager): visits several food sources to

collect the harvested crop, in each round-trip foraging journey. Nectar from many flower patches accumulate andstore in the foragers’ honey stomach (honey sac).

b) Nurse bee: working inside the hive and she receive thecollected nectar from the employed bee (forager) byregurgitation process. After that, the nurse bee starting makeshoney or royal jelly from the associated mixed nectar bysecreting invertase enzyme from the hypopharyngeal glandin her head. The corresponding enzyme assists to split the


ISSN 1947-5500




Vol. 8 , No. 8 , 2010

complex sugar (sucrose) to two simplifier sugars (fructoseand glucose), which are principal of new well-done honey.

B. The Classical ABC Algorithm

The ABC algorithm is a new AI model, which has beenstimulated by the collective behavior of the social honeybees

based on swam intelligence. It uses multi-resource and multi-form to perform the job with full optimization [24]. TheABC algorithm originally is divided into three parts:employed bees, onlookers and scouts. The employed bee is ahard-worker part in the colony that responsible to collectfood. Onlookers part is waiting inside the hive to decide on aforage source. Scouts is performed a general search to findthe food resources.

C. Hybrid ABC Algorithm for RNA Structural Prediction

Our proposed ABCRna method is a hybrid model basedon the PSO and it is derived from the original ABC model.This new derivation of the modified ABC is associated with

a specific case corresponding to the pseudoknotted RNAsecondary structure prediction problem. The worker bee(employed bee) works as an agent, visits many rich flowerpatches (artificial food sources) to collect the nectar.Thereafter, all collected nectar from many flowers stored inforagers’ honey stomach, which will be a mixture of nectarfrom several food sources (many flowers). Then, theemployed bee (forager) back to the hive from the foraging journey with the mixed nectar fills her honey stomach. Inthe hive, the forager submits the crop (collected nectar) tothe nurse bee in a regurgitation process. Finally, the nursebee now is ready to make honey from the correspondingmixture of gathered nectar that submitted by employed bee.The nurse bee starts secreting the honey or royal jelly

associated with specific needs of the hive. Here, the finalwell-done honey is represented the good solutions for theRNA structural prediction problem. In another words, theconcluding honey in mapping segment, stands for the moreaccurate pseudoknotted RNA secondary structure for agiving primary sequence.

The central phase of the ABCRna method is a HoneyRnaalgorithm, which is a modified from ABC algorithm to solvethe pseudoknotted RNA structural prediction method. ThisHoneyRna algorithm is illustrated in Figure 7 and computesin steps as follows:

1: Initialize

2: REPEAT

3: Place the employed bee on her food sources (many flowers)

4: Place the nurse bee on hive working to receive mixed nectar

5: Secret enzyme to split the complex nectar to a simpler honey

6: Fill the secreting food (honey & royal jelly) in the honeycomb

7: Filter the well-done honey from extra water by evaporation

8: Cap and seal the filled cell with food by adhesive wax

9: UNTIL (Demanded food is met)

In the modified ABC algorithm, each cycle of thecollecting nectar and secreting honey process includes three

steps: (i) the employed bee visits many flower patches ineach round-trip of collecting nectar journey. All gatherednectar is stored in her honey stomach. The employed beebacks to the hive with holding the mixed nectar. Then, shewill submit this mixture to the nurse bee.

Moreover, the secretion process of the honey by nursebee performs in many steps, as follows:

a) The harvest of the forage (the mixed nectar), has beencollected from many flowers. By mapping this phase withthe RNA related issue, the predicted RNA secondarystructure is collected from many existed RNA predictedmethods, as illustrated in Figure 7.

b) The nurse bee starts make honey by secreting invertaseenzyme from the gland in her head. This enzyme simplifiesthe sucrose which is a complex sugar in the nectar to twotypes (fructose and glucose) of simpler sugars, which arecomposed the well done honey. By mapping this with RNAstructural problem, there is an agent program, which isworking like that enzyme. This function re-constructs theentire secondary structure of RNA sequence with

pseudoknots from many parts.

V. BENCHMARK TESTS AND RESULTS

We evaluated ABCRna on different types of pseudoknotted RNA classes. The proposed method is built topredict the RNA secondary structure with pseudoknots. Thecomparisons of the ABCRna results have been performed bymeasuring the accuracy of its outputs to the outputs that hasbeen achieved from FlexStem [29], HotKnots [30] andpknotsRG [19]. These accuracy measurements compromisethree statistical notations: (Sensitivity S, Selectivity P and F-measure). They can be calculated by applying the following

formulas, which derived from [63]:

Sensitivity =TP

TP + FN× 100 1

Speciicity =TP

TP + FP× 100 2

F − measure = 2 × ×

+ × 100, 3

where TP is represented the True Positive, which denotes the

number of base pairs that are predicted correctly andpresented in the known native structure. FN is representedthe False Negative, which counts the base pairs that arepresented in the known native structure, but they are notreported in the predicted structure. FP is represented theFalse positive, which denotes the number of base pairs,presented in the native known structure, but they are not inpredicted structure. F-measure is a single measure thatcombines both sensitivity and specificity of the predictoralgorithm in a unique performance measure.


ISSN 1947-5500




Vol. 8 , No. 8 , 2010

(a) Native structure of TMV of length 412 nt.

(c) Structure of TMV predicted by FlexStem, 2008 [29].

(b) Structure of TMV predicted by our ABCRna method.

(d) Structure of TMV predicted by HotKnots, 2005 [30].

(e) Structure of TMV predicted by PknotsRG,2004 [19].

Figure 8. Plots of qualitative comparison analysis of TMV structures: (a) The known native secondary structure of TMV molecule. (b) Secondary structure

predicted by our proposed ABCRna method, with highest excellent sensitivity of (92.9%) and specificity (95.6%). (c) Secondary structure predicted by FlexStem(sensitivity of 44.3% and specificity 44.9%). (d) Secondary structure predicted by HotKnots (sensitivity of 67.1% and specificity 81.0%). (e) Secondary structure

predicted by pknotsRG (sensitivity of 60.0% and specificity 66.7%).


ISSN 1947-5500




Vol. , No. , 2010

Here, the comparison analysis of the outputs areperformed between our proposed ABCRna method against tothe FlexStem [29], HotKnots [30] and pknotsRG [19]. Oneexample of this comparison pricess uses the RNA sequencetobacco mosaic virus (TMV) from 3’UTR type [64]. Thelength of TMV is equal 214 nucleotides (nt), which itsaccession number “J02415”. Our proposed ABCRna methodobtained the highest results, Sensitivity (S = 92.9%) and

Specificity (P = 95.6%), which are measured according tothe known native structure of TMV molecule. The sensitivityand specificity of FlexStem [29], HotKnots [30] andpknotsRG [19], are listed in the legend of the illustrativeFigure 8, respectively. Finally, the Figure 8 depicts aqualitative comparison analysis among the output of ourABCRna and the best result of all others methods from theliterature. This comparison analysis is applied on thesecondary structure of TMV RNA molecule, which itsimages are produced by PseudoViewer software tool [65].NUPACK [66] and pknotsRE [67] methods cannot predictthe secondary structure of RNA sequences in larger than thelength of 200 nt and 150 nt, respectively. The reason behindthat the both algorithms NUPACK [66] and pknotsRE [67],

require an enormous amount of memory (RAM) and run inexponential time. To reach to a fair comparison, the outputsfor these scenarios put out of the result. Also, all five existingmethods (FlexStem [29], HotKnots [30], pknotsRG [19],NUPACK [66] and pknotsRE [67]), have been implementedin the same machine, a PC Ubuntu 10.04 64-bit Linux OS,with AMD Phenom-II 810 2.6-GHz Quad-Core processorand Dual Channel 4GB (2x2GB) DDR2-800 Memory

(RAM).

Table 1 summarizes the final comparison analysis of theresults among the predicted RNA structures from ourproposed ABCRna method and the best ones from FlexStem[29], HotKnots [30], pknotsRG [19], NUPACK [66] andpknotsRE [67] methods. The comparison process has beendone in respects to the three accuracy metrics listed inEquations (1, 2 and 3). The evaluation of these comparativeresults were performed and verified according to the standardnative structures of each RNA molecule. The analyses showthe results of the ABCRna method are significantly betterthan the results of other methods from literature, in terms of sensitivity, specificity and F-measure.

The highest results are in bold, and “*” denotes that the algorithm is unable to complete the run.

IV. CONCLUSION AND FUTURE WORK

This paper presented a novel hybrid method for solvingRNA secondary structure with pseudoknot functionalclasses. This hybrid method includes ABC model as a globaloptimization method, hybridized with CBR as a localoptimization technique. The proposed method used theexisting results from KnotSeeker and UNAFold to generate asecondary structure of RNA includes pseudoknots, by usingan existing cases.

Three evaluation mechanisms are used to measure theefficiency and performance of proposed method comparingto others from literature. The sensitivity, specificity and F-measure metrics showed that successful outcomes have beenrecorded. Furthermore, three different comparative methodsare used in order to compare the obtained results. Theproposed ABCRna hybrid method outperformed othercomparators in almost all standard benchmarks. Note that thefactor for comparison is the real native structure.

We believe that the proposed hybrid method have a highpotential with a great efficiency for the problems solved byRNA community. This worthwhile domain is pregnant withseveral future research directions such as: further study casesin CBR, different global optimization, different factors of analysis and hybridize more RNA prominent methods.

ACKNOWLEDGMENT

This research was partly supported by a Universiti Sains

Malaysia (USM) Fellowship, awarded to the correspondingauthor. It was also funded by "Insentif APEX". The authorsgratefully acknowledge the reviewers for their helpful anduseful comments.

REFERENCES

[1] R. Williams and O. Luo, "Complexity, Post-genomic Biology and GeneExpression Programs," Complex Physical, Biophysical and

Econophysical Systems: Proceedings of the 22nd Canberra International

Physics Summer School, p. 319, 2009.

TABLE I. COMPARISON ACCURACY METRICS THE STRUCTURAL RESULTS OF BASE PAIRS AMONG THE ABCRNA METHOD AND OTHER METHODS.

RNA sequence ABCRna (2010) FlexStem (2008) HotKnots (2005) pknotsRG (2004) NUPACK (2003) pknotsRE (1999)

Seq. ID, Ref.RNA

class

Accession

Number

Length

(nt)S P F S P F S P F S P F S P F S P F

HDV-It_g [68] Ribozymes X04451 87 90.6 93.5 92.1 84.4 79.4 81.8 71.9 82.1 76.7 90.6 93.5 92.1 62.5 62.5 62.5 81.3 81.3 81.3

BMV [69] 3’-UTR J02042 145 73.8 62.0 67.4 45.2 38.8 41.8 31.0 31.7 31.3 47.6 43.5 45.5 45.2 44.2 44.7 45.2 44.2 44.7

FMDV-A [70] 5’-UTR AY593751 165 83.3 54.1 65.6 66.7 41.0 50.8 12.5 07.3 09.2 66.7 41.0 50.8 62.5 34.9 44.8 * * *

TMV [71] 3’-UTR J02415 214 92.9 95.6 94.2 44.3 44.9 44.6 67.1 81.0 73.4 60.0 66.7 63.2 * * * * * *


ISSN 1947-5500




Vol. 8 , No. 8 , 2010

[2] F. Buchholz, R. Kittler, M. Slabicki, and M. Theis, "Enzymaticallyprepared RNAi libraries," Nat Meth, vol. 3, pp. 696-700, 2006.

[3] J. Sperschneider and A. Datta, "KnotSeeker: Heuristic pseudoknotdetection in long RNA sequences," RNA, vol. 14, pp. 630-640, April

2008 2008.[4] G. Sengle, A. Eisenführ, P. S. Arora, J. S. Nowick, and M. Famulok,

"Novel RNA catalysts for the Michael reaction," Chemistry & Biology,

vol. 8, pp. 459-473, 2001.[5] J. Mattick, "RNA regulation: a new genetics?," Nature Reviews Genetics,

vol. 5, pp. 316-323, 2004.

[6] O. Nureki, D. Vassylyev, M. Tateno, A. Shimada, T. Nakama, S. Fukai,M. Konno, T. Hendrickson, P. Schimmel, and S. Yokoyama, "Enzyme

structure with two catalytic sites for double-sieve selection of substrate,"Science, vol. 280, p. 578, 1998.

[7] R. Rambo and J. Tainer, "Bridging the solution divide: comprehensive

structural analyses of dynamic RNA, DNA, and protein assemblies by

small-angle X-ray scattering," Current Opinion in Structural Biology,

vol. 20, pp. 128-137, 2010.[8] A. Heck, "Native mass spectrometry: a bridge between interactomics and

structural biology," Nature Methods, vol. 5, pp. 927-933, 2008.

[9] H.-K. Cheong, E. Hwang, C. Lee, B.-S. Choi, and C. Cheong, "Rapidpreparation of RNA samples for NMR spectroscopy and X-ray

crystallography," Nucl. Acids Res., vol. 32, pp. e84-, June 15, 2004 2004.

[10] R. M. Al-Khatib, R. Abdullah, and N. Abdul Rashid, "A ComparativeTaxonomy of Parallel Algorithms for RNA Secondary Structure

Prediction," Evolutionary Bioinformatics, vol. 6, p. 27, 2010.[11] C. Wilson, J. Nix, and J. Szostak, "Functional Requirements for Specific

Ligand Recognition by a Biotin-Binding RNA Pseudoknot†," Biochemistry, vol. 37, pp. 14410-14419, 1998.

[12] A. Roth and R. Breaker, "The structural and functional diversity of metabolite-binding riboswitches," Annual review of biochemistry, vol. 78,

pp. 305-334, 2009.

[13] Y. Takakura, "Towards therapeutic application of RNA-mediated generegulation," Advanced Drug Delivery Reviews, vol. 61, pp. 667-667,

2009.

[14] P. Menzel, J. Gorodkin, and P. F. Stadler, "The tedious task of findinghomologous noncoding RNA genes," RNA, vol. 15, pp. 2075-2082,

December 2009 2009.

[15] R. M. Al-Khatib, R. Abdullah, and N. Abdul Rashid, "A Survey of Compute Intensive Algorithms for Ribo Nucleic Acids Structural

Detection," Journal of Computer Science, vol. 5, pp. 680-689, 2009.[16] C. W. A. Pleij, K. Rietveld, and L. Bosch, "A new principle of RNA

folding based on pseudoknotting," Nucl. Acids Res., vol. 13, pp. 1717-1731, March 11, 1985 1985.

[17] M. Zuker, "Mfold web server for nucleic acid folding and hybridization

prediction," Nucl. Acids Res., vol. 31, pp. 3406-3415, July 1, 2003 2003.[18] I. L. Hofacker, "Vienna RNA secondary structure server," Nucl. Acids

Res., vol. 31, pp. 3429-3431, July 1, 2003 2003.

[19] J. Reeder and R. Giegerich, "Design, implementation and evaluation of apractical pseudoknot folding algorithm based on thermodynamics," BMC

Bioinformatics, vol. 5, p. 104, 2004.

[20] T. Akutsu, "Dynamic programming algorithms for RNA secondarystructure prediction with pseudoknots," Discrete Applied Mathematics,

vol. 104, pp. 45-62, 2000.

[21] R. B. Lyngso and C. N. S. Pedersen, "RNA Pseudoknot Prediction inEnergy-Based Models," Journal of Computational Biology, vol. 7, pp.409-427, 2000.

[22] Q. Dong and Z. Wu, "A linear-time algorithm for solving the molecular

distance geometry problem with exact inter-atomic distances," Journal of

Global Optimization, vol. 22, pp. 365-375, 2002.

[23] U. Schِning and M. von Knop, "Using Stochastic Indexed Grammars for

RNA Structure PredictionWith Pseudoknots," Bulletin of the EATCS, p.185, 2010.

[24] D. Karaboga, "An idea based on honey bee swarm for numericaloptimization," in Technical Report-TR06, Erciyes University,

Engineering Faculty, Computer Engineering Department , 2005.

[25] H. Chang, "Converging Marriage in Honey-Bees Optimization and

Application to Stochastic Dynamic Programming," Journal of Global

Optimization, vol. 35, pp. 423-441, 2006.

[26] N. Markham and M. Zuker, "DINAMelt web server for nucleic acid

melting prediction," Nucleic acids research, vol. 33, p. W577, 2005.[27] T. Furusawa, R. Rakwal, H. W. Nam, J. Shibato, G. K. Agrawal, Y. S.

Kim, Y. Ogawa, Y. Yoshida, Y. Kouzuma, Y. Masuo, and M. Yonekura,

"Comprehensive Royal Jelly (RJ) Proteomics Using One- and Two-

Dimensional Proteomics Platforms Reveals Novel RJ Proteins and

Potential Phospho/Glycoproteins," Journal of Proteome Research, vol. 7,pp. 3194-3229, 2008.

[28] A. G. O. Yeh and X. Shi, "Case-based reasoning (CBR) in developmentcontrol," International Journal of Applied Earth Observation and

Geoinformation, vol. 3, pp. 238-251, 2001.[29] X. Chen, S. He, D. Bu, F. Zhang, Z. Wang, R. Chen, and W. Gao,

"FlexStem: improving predictions of RNA secondary structures with

pseudoknots by reducing the search space," Bioinformatics, vol. 24, p.1994, 2008.

[30] J. REN, B. RASTEGARI, A. CONDON, and H. H. HOOS, "HotKnots:

Heuristic prediction of RNA secondary structures includingpseudoknots," RNA, vol. 11, pp. 1494-1504, October 2005 2005.

[31] W. Tichelaar, W. G. SCHUTTER, A. C. ARNBERG, E. F. J.

BRUGGEN, and W. STENDER, "The quaternary structure of Escherichia coli RNA polymerase studied with (scanning) transmission

(immuno)electron microscopy," European Journal of Biochemistry, vol.

135, pp. 263-269, 1983.[32] H. Liu, D. Xu, J. Shao, and Y. Wang, "An RNA folding algorithm

including pseudoknots based on dynamic weighted matching,"Computational Biology and Chemistry, vol. 30, pp. 72-76, 2006.

[33] E. Westhof, "Toward atomic accuracy in RNA design," Nat Meth, vol. 7,pp. 272-273, 2010.

[34] T. Xia, J. SantaLucia, M. E. Burkard, R. Kierzek, S. J. Schroeder, X.

Jiao, C. Cox, and D. H. Turner, "Thermodynamic Parameters for anExpanded Nearest-Neighbor Model for Formation of RNA Duplexes with

Watson-Crick Base Pairs," Biochemistry, vol. 37, pp. 14719-14735, 1998.[35] O. Schrader, T. Baumstark, and D. Riesner, "A Mini-RNA containing the

tetraloop, wobble-pair and loop E motifs of the central conserved regionof potato spindle tuber viroid is processed into a minicircle," Nucl. Acids

Res., vol. 31, pp. 988-998, February 1, 2003 2003.[36] A. Bar-Shira, A. Panet, and A. Honigman, "An RNA secondary structure

juxtaposes two remote genetic signals for human T-cell leukemia virus

type I RNA 3'-end processing," J. Virol., vol. 65, pp. 5165-5173, October1, 1991 1991.

[37] I. Brierley, S. Pennell, and R. J. C. Gilbert, "Viral RNA pseudoknots:

versatile motifs in gene expression and replication," Nat Rev Micro, vol.5, pp. 598-610, 2007.

[38] M. N. Win and C. D. Smolke, "A modular and extensible RNA-based

gene-regulatory platform for engineering cellular function," Proceedings

of the National Academy of Sciences, vol. 104, pp. 14283-14288,

September 4, 2007 2007.[39] A. Mathuriya, D. A. Bader, C. E. Heitsch, and S. C. Harvey, "GTfold : a

scalable multicore code for RNA secondary structure prediction," inProceedings of the 2009 ACM symposium on Applied Computing

Honolulu, Hawaii: ACM, 2009.

[40] M. Masiero, G. Nardo, S. Indraccolo, and E. Favaro, "RNA interference:Implications for cancer treatment," Molecular Aspects of Medicine, vol.

28, pp. 143-166, 2007.

[41] T. Schmickl, H. Hamann, H. Wِrn, and K. Crailsheim, "Two differentapproaches to a macroscopic model of a bio-inspired robotic swarm,"

Robotics and Autonomous Systems, vol. 57, pp. 913-921, 2009.

[42] D. Hill and T. Webster, "Apiculture and forestry (bees and trees)," Agroforestry Systems, vol. 29, pp. 313-320, 1995.

[43] S. Ziane and A. Melouk, "A swarm intelligent multi-path routing for

multimedia traffic over mobile ad hoc networks," in Proceedings of the

1st ACM international workshop on Quality of service & amp; security inwireless and mobile networks Montreal, Quebec, Canada: ACM, 2005.

[44] Z. Lian, B. Jiao, and X. Gu, "A similar particle swarm optimization

algorithm for job-shop scheduling to minimize makespan," Applied

Mathematics and Computation, vol. 183, pp. 1008-1017, 2006.

[45] W. BRYAN, S. ADRIAN, R. DIRK, and O. JAMES, "UAV Swarm

Control: Calculating Digital Pheromone Fields with the GPU," The

Journal of Defense Modeling and Simulation: Applications,

Methodology, Technology, vol. 3, pp. 167-176, 2006.[46] P. Doherty, P. Haslum, F. Heintz, T. Merz, P. Nyblom, T. Persson, and

B. Wingman, "A Distributed Architecture for Autonomous Unmanned

Aerial Vehicle Experimentation," in Distributed Autonomous Robotic

Systems 6 , 2007, pp. 233-242.[47] E. Bonabeau, M. Dorigo, and G. Theraulaz, "Inspiration for optimization

from social insect behaviour," Nature, vol. 406, pp. 39-42, 2000.

[48] S. Remolina and K. Hughes, "Evolution and mechanisms of long life andhigh fertility in queen honey bees," AGE, vol. 30, pp. 177-185, 2008.

[49] D. Karaboga and B. Akay, "A comparative study of Artificial Bee

Colony algorithm," Applied Mathematics and Computation, vol. 214, pp.

108-132, 2009.


ISSN 1947-5500



[50] I. Cakmak, C. Sanderson, T. D. Blocker, L. Lisa PhaA. Norman, B. K. Harader-Pate, R. Tyler Reidenbaug

Barthell, and H. Wells, "Different solutions by bproblem," Animal Behaviour, vol. 77, pp. 1273-1280,

[51] T. D. Seeley, "Social foraging by honeybees: ho

foragers among patches of flowers," Behavio

Sociobiology, vol. 19, pp. 343-354, 1986.[52] T. D. Seeley, A. S. Mikheyev, and G. J. Pagano, "Dan

duration and rate of waggle-run production in relati

profitability," Journal of Comparative Physiology

Sensory, Neural, and Behavioral Physiology, vol.2000.

[53] K. R. Abbott and R. Dukas, "Honeybees consider flo

waggle dance," Animal Behaviour, vol. 78, pp. 633-63[54] T. Kubo, M. Sasaki, J. Nakamura, H. Sasagawa, K. O

and S. Natori, "Change in the Expression of Hyp

Proteins of the Worker Honeybees (Apis melliferaLRole," J Biochem, vol. 119, pp. 291-295, February 1, 1

[55] A. Aamodt and E. Plaza, "Case-based reasoning: f

methodological variations, and system approaches,"pp. 39-59, 1994.

[56] I. Watson, "Case-based reasoning is a methodologyKnowledge-Based Systems, vol. 12, pp. 303-308, 1999

[57] L. Portinale, D. Magro, and P. Torasso, "Mul

combining case-based and model-based reasonin

experimental analysis," Artificial Intelligence, vol.2004.

[58] J. Pearl, Heuristics: Intelligent search strategies for

solving, 1984.[59] Z. Geem, J. Kim, and G. V. Loganathan, "A New He

Algorithm: Harmony Search," SIMULATION, vol. 76,

[60] K. S. Lee and Z. W. Geem, "A new structural optimizon the harmony search algorithm," Computers & Str

781-798, 2004.

[61] D. Karaboga and B. Basturk, "On the performance of

(ABC) algorithm," Applied Soft Computing, vol. 8, pp.

Ra'ed M. Al-Khatib receivedegree in Computer Scienc '

University, Karak, Jordan in 1

Degree in Computer EngiSystem from Yarmouk Universit

2006. He is currently a researc

and Distributed Processing ResePhD candidate as well under t

Professor Dr. Rosni Abdullah

Professor Dr. Nur'Aini Abdul R

of Computer Sciences, Universi

in the area of Parallel Algori

Bioinformatics Applications.

Rosni Abdullah received her Ba '

Computer Science and Applied

Masters Degree in ComputeWestern Michigan Universi

Michigan, U.S.A. in 1984 and 1

She joined the School of ComUniversiti Sains Malaysia in 1

She received an award from

pursue her PhD at LoughboroUnited Kingdom in the area Pa

She was promoted to Associateand to Professor in 2008. Sheadministrative positions such

Coordinator, Programme Chair

Dean for Postgraduate Studies

is currently the Dean of the Sc

Sciences and also Head of

Distributed Processing Researfocus on grid computing an

research. Her current research w

of Parallel Algorithms fo

Applications.


m, S. Checotah, A.h, P. Nenchev, J. F.

ees to a foraging2009.

colonies allocateral Ecology and

cing bees tune both

n to nectar-source A: Neuroethology,

186, pp. 813-819,

wer danger in their

5, 2009.hashi, H. Takeuchi,

opharyngeal-Gland

.) with Age and/or996 1996.oundational issues, I Commun., vol. 7,

not a technology,"

.ti-modal diagnosis

g: a formal and

158, pp. 109-153,

computer problem

ristic Optimization

pp. 60-68, 2001.

ation method basedctures, vol. 82, pp.

rtificial bee colony

. 687-697, 2008.

[62] J. Kennedy and R. Eberhart, "Parti Networks, 1995. Proceedings., IEEE

pp. 1942-1948 vol.4.[63] P. Baldi, S. Brunak, Y. Chauvin

"Assessing the accuracy of predicti

overview," Bioinformatics, vol. 16, p.

[64] F. H. D. van Batenburg, A. P."PseudoBase: structural information Res., vol. 29, pp. 194-195, January 1,

[65] Y. Byun and K. Han, "PseudoView

large-scale RNA structures with pseu1435, 2009.

[66] R. Dirks and N. Pierce, "A partition

secondary structure including pseudo1664 - 1677, 2003.

[67] E. Rivas and S. R. Eddy, "A dynam

structure prediction including pse Biology, vol. 285, pp. 2053-2068, 19

[68] M. D. Been and G. S. Wickham, "S

Delta Virus RNA," European Journa

753, 1997.

[69] C. W. A. Pleij, J. P. Abrahams, A. v.

"The spatial folding of the 3' noncodviral RNAs," M. Brinton and R. Ru

Viruses, A. R. Liss, New York (1987),

[70] B. E. Clarke, A. L. Brown, K. M. Cand A. R. Carroll, "Potential seco

genomic RNA of foot and mouth dis

vol. 15, pp. 7067-7079, September 11[71] R. Koenig, S. Barends, A. P. Gultya

Loss, and C. W. A. Pleij, "Nemesia

with a genomic RNA having a histidGen Virol, vol. 86, pp. 1827-1833, Ju

d his Bachelorse from Mu'tah

993 and Masters

eering-Embeddedy, Irbid, Jordan in

er at the Parallel

arch Group and ahe supervision of

and Associated

' shid at the School

ti Sains Malaysia

ithms Applied to

chelor's Degree in

Mathematics and

r Science fromity, Kalamazoo,

1986 respectively.

puter Sciences at87 as a lecturer.

USM in 1993 to

gh University inrallel Algorithms.

Professor in 2000has held severalas First Year

man and Deputy

nd Research. She

hool of Computer

the Parallel and

h Group whichd bioinformatics

ork is in the area

Bioinformatics

Nur’Aini Abdul

Degree in Com

State University,

School of CompMalaysia in 198

Master's Degree

Universiti Sains1995. She contin

Sciences at Univ

from 1995. She

2002 to pursue

Malaysia, Penang

She was promoteis currently an As

Computer Scien

Parallel and DiGroup which f

bioinformatics re

is in the area o

Bioinformatics A

Retrieval, Text S

International Journal of Computer Science and Information Security,

Vol. 8 , No. 8 , 2010

le swarm optimization," in Neural

International Conference on, 1995,

, C. Andersen, and H. Nielsen,n algorithms for classification: an

. 412, 2000.

Gultyaev, and C. W. A. Pleij,on RNA pseudoknots," Nucl. Acids

2001 2001.

er3: generating planar drawings of

doknots," Bioinformatics, vol. 25, p.

function algorithm for nucleic acid

knots," J Comput Chem, vol. 24, pp.

ic programming algorithm for RNA

udoknots," Journal of Molecular

9.lf-Cleaving Ribozymes of Hepatitisl of Biochemistry, vol. 247, pp. 741-

Belkum, K. Rietveld, and L. Bosch,

' ing region of aminoacylatable plantcker, Editors, Positive Strand RNA

pp. 299–316, 1987 1987.

rrey, S. E. Newton, D. J. Rowlands,dary and tertiary structure in the

ase virus," Nucleic Acids Research,

, 1987 1987.v, D.-E. Lesemann, H. J. Vetten, S.

ing necrosis virus: a new tymovirus

ylatable tobamovirus-like 3' end," J

ne 1, 2005 2005.

Rashid received her Bachelor'suter Science from Mississippi

U.S.A. in 1985. She joined the

ter Sciences at Universiti Sains8 as a tutor. She received her

' in Computer Sciences from

Malaysia, Penang, Malaysia inued in the School of Computer

rsiti Sains Malaysia as a lecturer

eceived an award from USM in

her PhD at Universiti Sains

in the area of Parallel methods.

to Senior Lecturer in 2004. Shesociate Professor in the School of

es and also member of the

istributed Processing Researchcus on grid computing and

earch. Her current research work

Parallel Algorithms applied to

plications, Genomic Information

arch and Comparison Methods.


ISSN 1947 5500

a novel hybridization of abc with cbr for pseudoknotted rna structure

Documents