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WCCI 2020 Tutorial

Multi-modality for Biomedical Problems: Theory and Applications.

Abstract

With the exploration of omics technologies, researchers are able to collect high-throughput biomedical data. The explosion of these new frontier omics technologies produces diverse genomic datasets such as microarray gene expression, miRNA expression, DNA sequence, 3D structures etc. These different representations (modality) of the biomedical data contain distinct, useful and complementary information of different samples. As a consequence, there is a growing interest in collecting ”multi-modal” data for the same set of subjects and integrating this heterogeneous information to obtain more profound insights into the underlying biological system. The current tutorial will discuss in detail different problems of bioinformatics and the concepts of multimodality in bioinformatics. In recent years different machine learning and deep learning based approaches become popular in dealing with multimodal data. A detailed discussion along this direction will also be presented in the tutorial. This tutorial will be an advanced survey equally of interest to academic researchers and industry practitioners - very timely with so much vibrant research in the computational biology domain over the past 5 years.

Time and location: To Be Announced.

Tutorial Syllabus

  • Introduction
    • Introduction to Bioinformatics
    • Introduction to Machine Learning Techniques
  • Different Bioinformatics Problems
    • Gene Expression Profile Clustering
    • Protein Function Prediction
    • Binding site Prediction
    • Protein Folding Prediction
  • Applications of Machine Learning in Bioinformatics Problems
    • Clustering Problem
    • Classification Problem
    • Multi-objective Optimization
  • Deep Learning in Solving Bioinformatics Problem
    • Introduction of Deep Learning
    • Convolutional Neural Network in Bioinformatics
    • Recurrent Neural Network in Bioinformatics
    • Capsule Network in Bioinformatics
    • Graph Convolutional Neural Network in Bioinformatics
  • Applications of Deep Learning in Solving Multi-modal Problem
    • Neccessity of Multi-modal Approach
    • Deep Multi-modal Approach in Bioinformatics
Tutorial Slides: Will Be Uploaded.

Presenters

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Dr. Sriparna Saha

Associate Professor, Departmment of Computer Science and Engineering, INDIAN INSTITUTE of TECHNOLOGY Patna

Dr. Sriparna Saha research interests include machine learning, multi-objective optimization, evolutionary techniques, text mining and biomedical information extraction. She is the recipient of the Google India Women in Engineering Award, 2008, NASI YOUNG SCIENTIST PLATINUM JUBILEE AWARD 2016, BIRD Award 2016, IEI Young Engineers’ Award 2016, Humboldt Research Fellowship 2016, Indo-U.S. Fellowship for Women in STEMM (WISTEMM) Women Overseas Fellowship program 2018, SERB WOMEN IN EXCELLENCE AWARD 2018, SERB Early Career Research Award 2018, DUO-India fellowship 2020, and CNRS fellowship. She has published papers on those topics in reputed fora like IEEE/ACM Transactions on Computational Biology and Bioinformatics, IEEE Intelligent Systems, IEEE Computational Intelligence Magazine, Scientific Reports, ACM Transactions on Knowledge Discovery from Data, ECIR and many more.

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Pratik Dutta

PHD RESEARCH SCHOLAR, Departmment of Computer Science and Engineering, INDIAN INSTITUTE of TECHNOLOGY Patna

Mr. Pratik Dutta received his BE and ME degree from Indian Institute of Engineering Science and Technology, Shibpur in 2013 and 2015, respectively. His research interest lies in computational biology, genomic sequence, protein-protein interaction, machine learning and deep learning techniques. He has published various research articles in different prestigious fora like Elsevier Computers in Biology and Medicine, IEEE/ACM Transactions on Computational Biology and Bioinformatics, IEEE Journal of Biomedical and Health Informatics, IEEE Congress on Evolutionary Computation, Scientific Reports,

Publications of the Presenters related to the Tutorial

  • N. Arya and S. Saha (2019), “Multi-modal classification for human breast cancer prognosis prediction: Proposal of deep-learning based stacked ensemble model”, IEEE/ACM Transactions on Computational Biology and Bioinformatics (impact factor: 2.428).
  • P. Dutta, S. Saha, S. Pai, A. Kumar (2019), “A Protein Interaction Information-based Generative Model for Enhancing Gene Clustering”, Scientific Reports, a journal of the Nature Research family.
  • S. Yadav, P. Ramteke, A. Ekbal, S. Saha, P. Bhattacharyya (2019), “Exploring Disorder-aware Attention for Clinical Event Extraction”, ACM Transactions on Multimedia Computing Communications and Applications (impact factor: 2.25)
  • S. Maitra, M. Hasanuzzaman, S. Saha (2019). “Unified Multi-view Clustering Algorithm using Multi-objective Optimization Coupled with Generative Model”. ACM Transactions on Knowledge Discovery from Data (impact factor: 1.489)
  • A. Qureshi, S. Saha , M. Hasanuzzaman, G. Dias, “Multi-task Representation Learning for Multimodal Estimation of Depression Level”, IEEE Intelligent Systems Volume 34(5), (2019)(impact factor: 4.464).
  • P. Dutta, S. Saha, S. Chopra, and V. Miglani (2019): “Ensembling of Gene Clusters utilizing Deep Learning and Protein-protein Interaction Information”, IEEE/ACM Transactions on Computational Biology and Bioinformatics (impact factor: 2.428).
  • S. Maitra and S. Saha (2019): “A Multiobjective Multiview Cluster Ensemble Technique: Application in Patient Subclassification”, Plos One (h5 index: 180, impact factor: 2.766).
  • P. Dutta, S. Saha , S. Gulati, ”Graph-based Hub Gene Selection Technique using Protein Interaction Information: Application to Sample Classification”, IEEE Journal on Biomedical and Health Informatics, IEEE, 2019 (accepted) (Impact factor: 3.850)
  • S. Mitra, S. Saha, S. Acharya (2018): “Fusion of Stabilityand Multi-objective Optimization for Solving Cancer Tissue Classification Problem”, Expert Systems With Applications, Elsevier, 2018, Vol. 113, Pages 377-396 (Impact Factor: 3.768).
  • S. Acharya, S. Saha, P. Pradhan (2018): ”Multi-factored gene-gene proximity measures exploiting biological knowledge extracted from Gene Ontology : application in gene clustering”, IEEE/ACM Transactions on Computational Biology and Bioinformatics, Elsevier, 2018 (Impact Factor: 2.428).
  • S. Saha , S. Acharya, Kavya K, Saisree (2017): “Simultaneous Clustering and Feature Weighting using Multiobjective Optimization for Identifying Functionally Similar miRNAs ”, IEEE Journal of Biomedical and Health Informatics, Vol. 22(5), Pages 1684-1690 (Impact Factor: 3.451)
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For any query regarding the tutorial please email to Tutorial Presenters