This presentation is about the usage of Artificial Intelligence in Bioinformatics. These slides give the basic knowledge about usage of Artificial Intelligence in Bioinformatics.

1. BIOINFORMATICS

2. BIOINFORMATICS:
Definition

3. 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.

4. 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

5. Bioinformatics:
Relation to Artificial
Intelligence

6. 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

7. 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}.

8. 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

9. 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.

11. Current Study of
Bioinformatics:
Some Statistics

12. 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
3) 2012 Impact Factor2.017
4) 2011 Impact Factor0.898
5) 2010 Impact Factor0.976
6) 2009 Impact Factor1.688
7) 2008 Impact Factor1.255
8) 2007 Impact Factor1.226

14. Current Study: A sneak
peek

15.  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.

16.  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.

17.  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.

18. 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

19. Applications of
Bioinformatics:

20. 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

21. 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

22.  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.

23.  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.