uses of artificial intelligence in bioinformatics

BIOINFORMATICS

BIOINFORMATICS:

Definition

WHAT IS BIOINFORMATICS?

Bioinformatics is the application of computer technology to the management of biological information.

Bioinformatics is an interdisciplinary research field that combines biology, computer science, mathematics and statistics into a broad-based field that will have profound impacts on all fields of biology.

Aim of Bioinformatics:

1) Organizing Data in the correct

manner

1) Proper Analysis of the Data

2) Interpreting the data in a

biologically meaningful manner

Bioinformatics:

Relation to Artificial

Intelligence

Artificial intelligence (AI) has increasingly gained attention in bioinformatics research and computational molecular biology.

1) AI Algorithms to be used for keeping records

2) Choosing a particular method for analyzing data

3) Helping Interpret Large Amount of Data quickly by using computer Technology

Example:

DNA sequencing with artificial intelligence:

Sequencing of DNA is among the most important tasks in molecular

biology. DNA chips are considered to be a more rapid alternative to more

common gel-based methods of sequencing. DNA chips commonly are

made with the set of all possible probes eight nucleotides in length

(octamers) generating 65,536 unique probes spaced on a 1.6 cm2 array

(Fodor, Read, Pirrung, Stryer, Lu and Solas, 1991). For example, consider

the DNA target sequence ATTGATTCG, with length NZ9 and a DNA chip

with all possible probes of length nZ4. A DNA chip with probe length n will

have 4n positions in the grid on the DNA chip. Thus, for a probe length 4

there exist 256 grid positions, each associated with a unique probe

sequence. All possible 4-nucleotide probes would exist in the set: {AAAA,

AAAT, ., and TTTT}.

In SBH, an appropriate length probe must be used to

unambiguously determine a target of length N. When is large (O40

nucleotides), a probe of length 4 cannot be used to reconstruct the

target with a high probability of success (Fogel, Chellapilla, &

Fogel, 1998). As N increases, the probability of redundancy in the

target increases making unambiguously reconstruction difficult

(Noble, 1995). Hence the AI methods are well suited to solve the

DNA sequencing problem unambiguously and obtain a near

optimal solution. A hidden Markov model (HMM) is a statistical

model, which is very well suited for many tasks in molecular

biology (Krogh, 1998). The most popular use of the HMM in

molecular biology is as a ‘probabilistic profile’ of a protein family,

which is called a profile HMM. From a family of proteins (or DNA)

a

profile HMM can be made for searching a database for other

members of the family. Boufounos, El-Difrawy , & Ehrlich (2004)

used HMMs in DNA sequencing, where they developed an approach

to the DNA base calling problem. In addition, they also modeled the

state emission densities using artificial neural networks and provided

a modified Baum-Welch re-estimation procedure to perform training.

Fuzzy logic is a mathematical framework, which is compatible with

poorly quantitative yet qualitatively signifi- cant data. Fuzzy logic is a

natural language for linguistic modeling, thus it is consistent with the

qualitative linguistic– graphical methods conventionally used to

describe biological systems (Woolf & Wang, 2000). Fuzzy if-then

rules were also developed to describe the basic molecular properties

and behaviors of DNA inside the living cell.

Current Study of

Bioinformatics:

Some Statistics

Current Bioinformatics

Journal Impact Factor &

Information Current impact factor: 0.92

IMPACT FACTOR RANKINGS

1) 2014 Impact Factor 0.921

2) 2013 Impact Factor1.726







Current Study: A sneak

peek

Artificial Intelligence and heuristic methods are extremely important

for the present and future developments of bioinformatics, a very

recent and strategic discipline having the potential for a revolutionary

impact on biotechnology, pharmacology, and medicine. While

computation has already transformed our industrial society, a

comparable biotechnological transformation is on the horizon. In the

last few years it has become clear that these two exponentially

growing areas are actually converging.

Molecular biologists are currently engaged in some of the most

impressive data collection projects. Recent genome-sequencing

projects are generating an enormous amount of data related to the

function and the structure of biological molecules and sequences.

AI and heuristic methods (in particular machine

learning and data mining, cluster analysis, pattern

recognition, knowledge representation) can provide

key solutions for the new challenges posed by the

progressive transformation of biology into a data-

massive science.

The main objective is to create an environment for

(1) cross-disseminating state-of-the-art knowledge both to

AI researchers and computational biologists

(2) creating a common substrate of knowledge that both

AI people and computational biologists can understand;

(3) stimulating the development of specialized AI

techniques, keeping in mind the application to

computational biology

(4) fostering new collaborations among scientists having

similar or complementary backgrounds.

Some Recent Approaches:

Computational analysis of biological data

Artificial intelligence, machine learning, and heuristic methods, including neural and belief networks

Prediction of protein structure (secondary structure, contact maps)

The working draft of the human genome

Genome annotation

Computational tools for gene regulation

Analysis of gene expression data and their applications

Computer assisted drug discovery

Knowledge discovery in biological domains

Applications of

Bioinformatics:

Bioinformatics is being used in following fields:

1) Microbial genome applications

2) Molecular medicine

3) Personalized medicine

4) Preventative medicine

5) Gene therapy

6) Drug development

7) Antibiotic resistance

8) Evolutionary studies

9) Waste cleanup Biotechnology

10) Climate change Studies

11) Alternative energy sources

12) Crop improvement

13) Forensic analysis

14) Bio-weapon creation

15) Insect resistance

16) Improve nutritional quality

17) Development of Drought resistant varieties Vetinary Science

FUTURE AND CONCLUSION

Bioinformatics in combination with AI techniques will play an increasingly important role in streamlining complex analytical workflows to perform a multi-step analysis within one analytical framework.

Such workflows enable processing and analysis of biological data that are complex, and are growing at an exponential rate.

The complexity of biological questions and thus analytical tasks for answering these questions are increasing.

AI techniques that deploy machine

learning, knowledge discovery, and

reasoning are continuously improving.

Future of bioinformatics lays in large-

scale analysis driven by computational

intelligence that will produce huge

savings in time, effort, and money and

accelerate biological discovery.

AI based tools can perform both the

complex tasks based on reasoning, as

well as repetitive menial tasks that can

be performed over a huge

combinatorial space and simulate

millions of wet-laboratory experiments.

These fields experience rapidly

growing knowledge that increased

understanding of both the human

immune system and pathogens.