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The aim of this workshop called Bioinformatics and Artificial Intelligence (BAI) is to bring together active scholars and practionners in the frontier of Artificial Intelligence (AI) and Bioinformatics. AI holds a tremendous repertoire of algorithms and methods that constitute the core of different topics of bioinformatics and computational biology research. BAI goals are twofolds : How can AI techniques contribute to bioinformatics research ?, and How can bioinformatics research raise new fundamental questions in AI ? Contributions will clearly point out answers to one of these goals focusing on AI techniques as well as focusing on biological problems.

Important dates:

  • Deadline for Paper Submission: April 18th April 30th, 2016
  • Author Notification: May 30th, 2016
  • Camera Ready Deadline: June 10th, 2016
  • Workshop: July 11th, 2016

Useful links:

Keynote Speaker

logo Dr. Dmitri Chklovskii, PhD.
Group Leader of the SCDA: Neuroscience Group
Simons Foundation, NY, USA

Dmitri .Mitya. Chklovskii is a Group Leader for Neuroscience at the Simons Center for Data Analysis. His research is aimed at reverse engineering the brain by reconstructing comprehensive maps of neural connections called connectomes and developing an algorithmic theory of neural computation using online machine learning. He studied physics and engineering in St. Petersburg, Russia and holds a PhD from MIT. After being a Junior Fellow at the Harvard Society of Fellows he made a transition to theoretical neuroscience and was a Sloan Fellow at the Salk Institute. From 1999 to 2007 he served first as an Assistant, and later an Associate Professor at Cold Spring Harbor Laboratory. From 2007 to 2014 he was a Group Leader at the Howard Hughes Medical Institute's Janelia Farm Research Campus where he initiated and led a collaborative project that assembled the largest-ever connectome.


Title: Biologically inspired machine learning.
Abstract:

Inspired by experimental neuroscience results we developed a family of online algorithms that reduce dimensionality, cluster and discover features in streaming data. The novelty of our approach is in starting with similarity matching objective functions used offline in Multidimensional Scaling and Symmetric Nonnegative Matrix Factorization. We derived online distributed algorithms that can be implemented by biological neural networks resembling brain circuits. Such algorithms may also be used for Big Data applications.






Invited Speakers

logo Dr. Laxmi Parida, PhD.
Distinguished RSM and Manager of the Computational Genomics Group
IBM, NY, USA

Dr. Parida is a Distinguished Research Staff Member and Manager of the Computational Genomics Group at the IBM T J Watson Research Center, Yorktown Heights, USA and a visiting professor at the Courant Institute of Mathematical Sciences. She received a Ph.D. in Computer Science from New York University in 1998. She is the currently leading the science teams in the personalized cancer medicine system .Watson for Genomics. and the "Sequence the Food Supply Chain Consortium". She has also lead the IBM Science team in the Cacao Consortium with MARS and USDA and the RecoProject/Genographic Project with National Geographic. Her research areas include population genomics, cancer genomics, plant genomics, bioinformatics algorithms and topological data analysis. She has published over one hundred and fifty peer-reviewed research papers and a monograph on pattern discovery in bioinformatics. She holds over thirty US patents. She is on the editorial board of BMC Bioinformatics, Journal of Computational Biology and an Associate Editor, IEEE/ACM Transactions on Computational Biology and Bioinformatics.


Title: Watson for Genomics: a cognitive approach to clinical oncology.
Abstract:

The confluence of genomic technologies, algorithmics and cognitive computing has brought us to the doorstep of widespread usage of personalized medicine. I will talk about Watson for Genomics that attempts to integrate the current state of knowledge of molecular oncology and pharmacogenomics with the ever-expanding body of literature to assist physicians in analyzing and acting on patient genomic profiles.



logo Achille Fokoue.
Research Staff Member
IBM, NY, USA

Achille is a member of the Cognitive Computing group at the IBM Thomas J. Watson Research Center, in Yorktown Heights, New York, USA. His research interests include knowledge representation and reasoning, data management, information integration, programming and query languages for XML, and program analysis. His current work focuses on developing theories, algorithms and systems for 1) scaling reasoning over large and expressive description logics knowledge bases that tolerate inconsistencies and uncertainties and 2) efficiently aligning large ontologies. He is also investigating applications of Semantic Web technologies in a variety of domains ranging from life sciences to text analysis.


Title: Tiresias: A system for predicting Drug-Drug Interactions Through Similarity-Based Link Prediction.
Abstract:

Drug-Drug Interactions (DDIs) are a major cause of preventable adverse drug reactions (ADRs), causing a significant burden on the patients' health and the healthcare system. It is widely known that clinical studies cannot sufficiently and accurately identify DDIs for new drugs before they are made available on the market. In addition, existing public and proprietary sources of DDI information are known to be incomplete and/or inaccurate and so not reliable. As a result, there is an emerging body of research on in-silico prediction of drug-drug interactions. We present Tiresias, a framework that takes in various sources of drug-related data and knowledge as inputs, and provides DDI predictions as outputs. The process starts with semantic integration of the input data that results in a knowledge graph describing drug attributes and relationships with various related entities such as enzymes, chemical structures, and pathways. The knowledge graph is then used to compute several similarity measures between all the drugs in a scalable and distributed framework. The resulting similarity metrics are used to build features for a large-scale logistic regression model to predict potential DDIs. We highlight the novelty of our proposed approach and perform thorough evaluation of the quality of the predictions. The results show the effectiveness of Tiresias in both predicting new interactions among existing drugs and among newly developed and existing drugs.