CS 4491/CS 7990 SPECIAL TOPICS IN BIOINFORMATICS

Mingon Kang, PhD Computer Science, Kennesaw State University Self Introduction

 Instructor: Mingon Kang

 http://ksuweb.kennesaw.edu/~mkang9  Or Google “Mingon Kang” and click the first one.

 Research interests:  Bioinformatics, Machine Learning, Data Mining, and Big Data Analytics

 Projects you may be interested in:  Several Genomics projects in Bioinformatics  Medical image classification Course Information

 Instructor: Mingon Kang, PhD  Office: J-339  Email: [email protected] include ‘CS4991’ or ‘CS7790’ in the subject of your message when you email.  Office Hours:  Tuesday: 2-4pm, Wednesday: 10am-noon  By appointment  Lecture slides, Homework, and other materials are posted on the course web page at: http://ksuweb.kennesaw.edu/~mkang9/?menu=CS4491_ 7990  lecture recordings will be in D2L Choice of Language

 You can use your favorite language, but R, Matlab, Python are highly recommended.

 The course will briefly introduce R in case you have no experience of those script languages.

 Better for file I/O of textual biological data

 Better to do matrix manipulation

 Fast Prototyping Expected Background

 For graduate students: Coursework in data structures and algorithms, or CS 5040 as per admissions analysis.

 For undergraduate students: CS 3304, CS 3410

 Statistics: good if you’ve had some background, but not required

 Molecular biology: no knowledge assumed, but an interest in learning some basic molecular biology is mandatory What is Bioinformatics

 Application of computer and information technology to problems in biology, particular molecular biology. NIH definitions:

“Bioinformatics applies principles of information sciences and technologies to make the vast, diverse, and complex life sciences data more understandable and useful. Computational biology uses mathematical and computational approaches to address theoretical and experimental questions in biology. Although bioinformatics and computational biology are distinct, there is also significant overlap and activity at their interface.”

 sometimes used synonymously with computational biology or computational molecular biology Other Terminologies

 Biomedical informatics? It is defined by National Library of Medicine (NLM) as “the intersection of basic informational and computing sciences with an application domain in biomedicine.”

 Biometrics? It is the science of using biological properties to identify individuals; for example, face recognition, finger prints, a retina scan, and voice recognition. Goal of this Course

 Understanding the types and sources of data available for computational biology.

 Understanding the important computational problems in molecular biology.

 Understanding the most significant & interesting algorithms.

 Identifying opportunities in this field, and perhaps formulating projects to explore further. What this course will do

 Give you an understanding of main issues in molecular biological computing: sequence, structure, and function.

 Give you an opportunity to implement some widely used algorithms.

 Give you exposure to classic computational problems, as manifested in biology.

 Give you exposure to classic biological problems, as represented computationally.

 Practice How to read and write research papers Topics in Bioinformatics

 Computational Biology

 Gene Expression Analysis

 Gene Regulatory Network Inference

 Markov Chain Model for Gene Identification

 Genome-Wide Association Studies (GWAS) and expression Quantitative Trait Loci (eQTL) analysis

 Next Generation Sequencing Data Analysis

 Deep Learning in Bioinformatics Reference Books

 N. Jones & P. Pevzner, "An Introduction to Bioinformatics Algorithms," 2004, ISBN 0262101068

 Supratim Choudhuri, Bioinformatics for Beginners: Genes, Genomes, Molecular Evolution, Databases and Analytical Tools, 2014, ISBN: 0124104711 Evaluation (tentative)

 Homework Assignment (4-5 assignments: 40%)

 Mainly writing codes for algorithm implementation  Late assignments will be accepted up to 24 hours after the due date for 50% credit.

 Midterm (20%) + Final (20%)

 Allows two pages of cheat sheets

 Two research papers (5% and 15%)

 Using Latex Academic Integrity

 Academic dishonesty  Cheating  Plagiarism  Collusion  The submission for credit of any work or materials that are attributable in whole or in part to another person  Taking an examination for another person  Any act designed to give unfair advantage to a student or the attempt to commit