ACM BCB Niagra Falls

MODI - Machine Learning Models for Multi-omics Data Integration

In conjuction with The 10th ACM Conference on Bioinformatics, Computational Biology (ACM BCB), Niagara Falls, New York, September 7-10, 2019.

Abstract:

A peer-reviewed proceedings workshop in cutting-edge machine learning approaches and applications in multi-omics data in which researchers in the field showcase and discuss their advanced approaches. The workshop will be half-day long of oral presentations of the accepted papers. We are aiming to host approximately 9 to 12 high-quality accepted works in the field. Each talk will last approximately 15 to 20 minutes, including question/answer session. A coffee with snack break will take place in the middle of the workshop for refreshment, discussions and networking.

Motivation and Scope:

The advancement in genome sequencing has helped reveal relevant information about genomic variants in protein functions, spectrums and diseases. Integrative approaches using machine learning and deep learning are applied to rebuild system biology networks of multi-omics including but not limited to DNA and RNA variants (SNPs, indels, CNA, CNV and exons, among others), protein-protein interactions networks and clinical information. Current techniques focus on integrating different molecules to (1) predict the outcomes of diseases such as survivability, progression, and type/subtype of the disease; (2) understand the behavior of molecules and build protein-protein interactions to create or repurpose drugs, in the context of precision medicine. However, the contribution of those different molecules must be deeply analyzed to target the cause rather than just the correlated factors of those molecules. The underlying computational models are aimed to learn the weights of the relationships and contributions of these different omics. We have also considered the following possible topics of the workshop (but not limited to):
  • Predictive models for various multi-omics data types
  • Clustering algorithms for multi-omics data
  • Deep learning applications in multi-omics data analysis
  • Relationship analysis among different types of data
  • Transformation-based multi-omics data integration
  • Semi-supervised models
  • Data integration for cancer biomarkers
  • Clinical aspects of specific diseases
  • Multi-omics data integration in precision medicine
  • Biological validation of multi-omics machine learning methods
  • Network-based methods for multi-omics data
  • Conceptual models of multi-omics data integration

Submission

Submitted manuscripts should not exceed 10 pages in ACM "sigconf" template on 8.5 x 11 inch paper. The accepted papers must show novel approach or application in integrating multi-omics data. We will invite our collaborators and colleagues in the field to review the papers based on clear rubric, where each paper will be evaluated by 3-5 reviewers. The reviewer will use the rubric to evaluate the work. However, to respect the academic freedom, special cases with a clarification from the reviewer will be considered for the final decisions.

To submit a paper please click on the following link: https://easychair.org/conferences/?conf=modiacmbcb2019

Partner Journal

Selected papers will be invited to submit papers to a special collecyion at Evolutionary Bioinformatics

Important Dates

  • Paper submission deadline: Jun 25, 2019
  • Notification of paper acceptance: July 10, 2019
  • Final version of accepted paper due: July 24, 2019
  • Workshop: September 7 - 10, 2019, in Niagara Falls, NY, USA

Organizers

Abed Alkhateeb is a Postdoctoral Fellow in the School of Computer Science at the University of Windsor, Canada. Abed's research interests are in machine learning models for computational biology, including next-generation sequencing analysis, machine learning approaches for cancer analysis, and deep learning for pharmacogenomics. He has more than 20 publications and conferences in the fields of bioinformatics and machine learning.

Abed Alkhateeb, Ph.D.
School of Computer Science
University of Windsor,
401 Sunset Ave, Windsor, ON, Canada
Email: alkhate@uwindsor.ca

Luis Rueda is a Full Professor in the School of Computer Science at the University of Windsor, Canada. His research interests are mainly focused on theoretical and applied machine learning and pattern re
cognition, mostly in the fields of multi-omics, data integration, transcriptomics, interactomics and genomics with applications to cancer research. He holds three patents on data encryption and has published more than 150 papers in prestigious journals and conferences in machine learning and bioinformatics. He is a Senior Member of the IEEE, and Member of ISCB, IAPR and ACM.

Luis Rueda, Ph.D.
School of Computer Science
University of Windsor,
401 Sunset Ave, Windsor, ON, Canada
Email: lrueda@uwindsor.ca