Biomedical & Clinical Informatics Lab

Department of Computational Medicine & Bioinformatics

BIBM 2019

International Workshop on:

Applications of Machine Learning and Signal Processing in Biomedical Informatics and Computational Genomics

November 18-21, 2019 @ San Diego, CA, USA

Description

Availability of genomic, proteomic, transcriptomic (which for simplicity we call them omics) data, biomedical images and clinical data, improvements in computational systems and advent of big-data analytics have created an opportunity to address many important questions in biology, genomics and medicine. However, high dimensionality, heterogeneity, multimodality, noisiness, incompleteness, and inter-dependency of such data and images hinder their analyses. The benefit of employing advanced signal processing and machine learning (recently deep learning) techniques to tackle the above challenges has been proven in other fields. However, relatively limited applications of these techniques have been made in biomedical informatics and computational genomics, where there are many challenging tasks that can be done using these advanced techniques. Also, there are many open problems in signal processing and machine learning that need to be solved for effective use in biomedical informatics and computational genomics. This workshop aims to provide a forum for academic and industrial researchers to exchange research ideas and share research findings to promote the development or refining of machine learning and signal processing methods for biomedical informatics, bioinformatics and computational genomics.

Research topics included in the workshop

1. Machine learning and deep learning techniques for biomedical signal and image analysis
2. Machine learning and deep learning techniques for analyzing high-throughput sequencing data
3. Machine learning for integrative analysis of high-throughput omics data
4. Machine learning for phenotypic predictive models
5. Machine learning for protein structure prediction
6. Signal processing and machine learning for prediction of phylogenic trees and homology detection
7. Machine learning and signal processing for single cell data analysis
8. Machine learning for detection of molecular signatures of cancer
9. Machine learning and signal processing in epigenetics
10. Machine learning and signal processing for detection of genomic structural variations and copy number variations
11. Algorithms and methods for clustering in bioinformatics
12. Algorithms and methods for classification of genomic data
13. Machine learning for biological network analysis and text mining for knowledge extraction

Important Dates

Due date for full workshop paper submission: Sept 20, 2019
Notification of paper acceptance to authors: Oct 15, 2019
Camera-ready of accepted papers: Nov. 1, 2019
Workshops: Nov. 18-21, 2019 @ San Diego, CA, USA.

Submission

Please submit a full length paper (up to 6 pages in IEEE 2-column format) through the online submission system here. Please follow the IEEE conference paper format.

Registration

Please note that at least one author of an accepted paper needs to register in order to have the paper published in the proceedings.

Program Co-Chairs

Sheida Nabavi, University of Connecticut, Storrs, CT, USA
Kayvan Najarian, University of Michigan, Ann Arbor, MI, USA

Program Committee Members

Maryam Bagherian, University of Michigan, Ann Arbor, MI, USA
Harm Derksen, University of Michigan, Ann Arbor, MI, USA
Jonathan Gryak, University of Michigan, Ann Arbor, MI, USA
Iman Hajirasouliha, Weill Cornell Medicine, New York, NY, USA
Amin Zollanvari, Nazarbayev University, Astana, Kazakhstan