Description about the Workshop
This workshop aims to provide in-depth knowledge and practical examples for applying machine learning techniques in biological/biomedical research on two aspects: (i) a classification problem, discriminating disease and neutral mutations in proteins and (ii) a real value prediction, predicting the inhibitory concentration of compounds for a drug target. A systematic approach for developing an efficient computational tool will be explained in detail with assessment procedures and extensive tutorials. The projects designed in this workshop will help to follow the protocol independently and achieve the goal. The participants of the workshop will be capable of developing reliable bioinformatics tools upon successful completion of the program.
Profile of the Instructor
Prof. M Michael Gromiha received his Ph.D from Bharathidasan University, India and served as STA fellow, RIKEN Researcher, Research Scientist and Senior Scientist at Computational Biology Research Center, AIST, Japan till 2010. Currently, he is working as a Professor at Indian Institute of Technology (IIT) Madras, India. He is teaching courses on bioinformatics, protein structure and function, protein interactions: computational techniques and handling computational biology lab. His main research interests are computational studies on protein structure and function, mutational analysis, machine learning techniques and development of databases and algorithms. He has published over 350 research articles/reviews, 10 editorials and two books on Protein Bioinformatics (Elsevier/Academic Press) and Protein interactions (World Scientific). His papers received about 14,000 citations and his h-index is 61. He has received several awards including Institute Research and Development Award, Tamil Nadu Scientist Award, ASC Masila Vijay Award for excellence in scientific research and publications. He is serving as Associate Editor/Editorial Board Member in 10 internationally reputed journals. He is ranked as topmost 0.5% of researchers in Bioinformatics/Biophysics/overall in the world. He is a Fellow of the Indian National Science Academy (FNA).
Session Schedule
Day 1 :
Module 1: Development of Algorithm (10:00 a.m. – 11:30 a.m)
Concepts Covered:Â
Datasets/features/methods/evaluation.
Learning outcome:
Efficiently and systematically approaching a bioinformatics problem.
Module 2: An Introduction to Weka Machine Learning Workbench (11:30 a.m. – 01:00 p.m.)
Concepts Covered:Â
Machine learning techniques/input features/interpretation of output
Learning outcome:
Features available in Weka and details on input and output files.
Module 3: Live Demo (02:00 p.m. – 04:00 p.m.)
Concepts Covered:
Working example
Learning outcome:
Learn methods and validation procedures in WEKA
Assessment: Multiple Choice Question
Day 2 :
Project 1: (10:00 a.m. – 01:00 p.m.)
Distinguishing between disease and neutral mutations
Learning outcome:
Reliable methods and interpretations for discrimination
Project 2: (02:00 p.m. – 04:00 p.m.)
Predicting inhibitory concentration of compounds for a drug target
Learning outcome:
Reliable methods and interpretations for prediction
Assessment: Short Project
Eligibility
Desirable academic qualification:
B.Sc/B.Tech/ M.Sc/M.Tech /PhD etc. (Pursuing/ Completed);
Basic knowledge in programming would be beneficial, but not mandatory.
Intended Audience:
Students, PhD scholars, Teachers, Industry
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