Special Session Call for Papers

Special Session


Machine Learning in Bioinformatics and Computational Biology

18-21 Dec. 2011, Honolulu, Hawaii, , USA



The quick development of sophisticated computational tools and their natural introduction in biological problems is responsible for the revolution that took place in the life sciences in the last ten-fifteen years. Currently we are able to collect a huge amount of biological data, but there is a large gap between these data and a better understanding of the underlying phenomena. Clearly, we are in a situation in which our data gathering capabilities greatly surpassed our ability to analyze that data. Bioinformatics and Computational Biology are fields of research that can help us exploit the deluge of data obtained with high-throughput techniques, such as microarrays. Classical statistics has developed well established methods to deal with situations involving data sets with large number of data points but not some many variables. However, many of the modern problems involve the opposite situation: many variables and very few data points. Machine learning methods have proven to be successfully applied in several bioinformatics-oriented studies, and their application onlarge-scale datasets make them particularly suitable for the type and structure of modern high-throughput data in life sciences.

This session will focus on machine learning methods developed for or applied in Bioinformatics and Computational Biology. The scope of this session includes drug targets identification and analysis of collateral effects, diagnosis devices including early-detection screening, bio-markers identification, analysis of signaling and metabolic pathways, data mining of heterogeneous data sources, and machine learning methods such as support vector machines, artificial neural networks, methods for cluster analysis, but not only, applied in any of the areas mentioned above. This session will bring together researchers in machine learning, bioinformatics, data mining, biology, and statistics to share their expertise to advance Bioinformatics and Computational Biology towards the goal of a better understanding of the complex phenomena of life as we know it.


We encourage submission of papers on novel bioinformatics and computational biology methods using machine learning techniques and focusing on drug targets identification and analysis of collateral effects, diagnosis, definition of biological markers, analysis of signaling pathways, data mining of heterogeneous data sources, including but not limited to:

and address technical issues including, but not limited to:


Paper Submission Deadline : July 15, 2011
Notification of acceptance : September 2, 2011
Camera-ready papers & Pre-registration : October 1, 2011
The ICMLA Conference : December 18-21, 2011

The special session will be held as a part of the ICMLA11 conference. The authors would submit papers through the main conference submission website. Papers must correspond to the requirements detailed in the instructions to authors. Accepted papers must be presented by one of the authors to be published in the conference proceeding. If you have any questions, do not hesitate to direct your questions to sorin@wayne.edu

All paper submissions will be handled electronically. Detailed instructions for submitting the papers are provided on the conference home page at


Special Session Chairs:

Sorin Draghici, Wayne State University, USA (sorin@wayne.edu)

Vasile Palade, University of Oxford, UK (vasile.palade@comlab.ox.ac.uk)

Program Committee Members:

Giuseppe Amato, CNR, Italy
Razvan Andonie, Central Washington University, Washington, USA

Valeriu Beiu, UAE University, Al Ain, United Arab Emirates

Duccio Cavalieri, University of Florence, Italy

Michael Defoin-Plattel, Rothamsted Research, UK

A.L.A.J. (Andre) Dekker,MAASTRO Clinic, Netherlands

Purvesh Khatri, Stanford University, USA

Yves Lussier, University of Chicago, USA

Andrea Splendiani, Rothamsted Research, UK

Eric Rouchka, University of Louisville, USA

Laurentiu Tarca, National Institutes of Health, USA

Gwenn Volkert, Kent State University, USA

Jinbo Xu, Toyota Technical Institute-Chicago, USA