International Workshop on Applications of Machine Learning and Signal Processing in Bioinformatics and Computational Genomics (AMLSP-BCG 2018)

in conjunction with

IEEE International Conference on Bioinformatics and Biomedicine (BIBM 2018)


Availability of genomic, proteomic and transcriptomic (which for simplicity we call them omics) 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 omics data 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 bioinformatics and computational genomics, where there are many challenging tasks that can be done using these techniques. Also, there are many open problems in signal processing and machine learning that need to be solved for effective use in bioinformatics and computational genomics. This workshop aims to provide a forum for academic and industrial researchers to exchange research ideas/designs and share research findings to promote the development or refining of signal processing and machine learning methods for bioinformatics and computational genomics.

Research topics included in the workshop

  1. Machine learning for protein structure prediction
  2. Signal processing and machine learning for prediction of phylogenic trees and homology detection
  3. Machine learning for detection of molecular signatures of cancer
  4. RNAseq analysis using machine learning
  5. Machine learning and signal processing in epigenetics
  6. Algorithms for detection of structural variations and copy number variations
  7. Random Matrix Theory (RMT)for clustering in bioinformatics
  8. Sparse Dictionary Learning for classification of genomic data
  9. Machine learning for integrative analysis of high-throughput omics data
  10. Biological network analysis and text mining for knowledge extraction
  11. Single cell data clustering

Important dates

Sept 30, 2018: Due date for full workshop papers submission

Oct 27, 2018: Notification of paper acceptance to authors

Nov 15, 2018: Camera-ready of accepted papers

Dec 3-6, 2018: Workshops

Program Co-Chairs

Sheida Nabavi, University of Connecticut

Kayvan Najarian, University of Michigan

Program Committee Members

Sardar Ansari, University of Michigan, USA

Alan Boyle, University of Michigan, USA

Harm Derksen, University of Michigan, USA

Iman Hajirasouliha, Well Cornell Medicine, USA

Amin Zollanvari, Nazarbayev University, Kazakhstan