In conjunction with ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (SIGKDD '16)
Bioinformatics is the science of managing, mining, and interpreting information from biological data. Various genome projects have contributed to an exponential growth in DNA and protein sequence databases. Rapid advances in high-throughput technologies, such as microarrays, mass spectrometry and new/next-generation sequencing, can monitor quantitatively the presence or activity of thousands of genes, RNAs, proteins, metabolites, and compounds in a given biological state. The ongoing influx of these data, the pressing need to address complex biomedical challenges, and the gap between the two have collectively created exciting opportunities for data mining researchers.
While tremendous progress has been made over the years, many of the fundamental problems in bioinformatics, such as protein structure prediction, gene-environment interaction, and regulatory network mapping, have not been convincingly addressed. Besides these, new technologies such as next-generation sequencing are now producing massive amounts of sequence data; managing, mining and compressing these data raise challenging issues. Finally, there is a pressing need to use these data coupled with efficient and effective computational techniques to build models of complex biological processes and disease phenotypes. Data mining will play an essential role in addressing these fundamental problems and in the development of novel therapeutic/diagnostic/prognostic solutions in the post-genomics era of medicine.
The goal of the 16th International Workshop on Data Mining in Bioinformatics (BIOKDD'16) is to encourage KDD researchers to tackle the numerous challenges of mining and learning in Bioinformatics, Biomedical and Health Informatics. Thus this year, the workshop will feature the theme of “Latest Advances of Mining and Learning in Bioinformatics, Biomedical and Health Informatics”. This field focuses on the use of data mining and machine learning approaches for the analysis of the large amount of heterogeneous complex biological and medical data being generated together with innovative applications in biomedical and health informatics. The key goal is thus to build accurate predictive or descriptive models from data enabling either novel discoveries in basic biology and medicine or an effective use of the latest advances of data mining in healthcare.
We encourage papers that propose novel data mining techniques for areas including but not limited to
13:00-13:10 Welcome Remarks
13:10-14:00 Invited Talk: Prof. Jun Huan, Professor, Dept. of EECS, University of Kansas, Lawrence, KS.
14:00-14:30 Vipin Vijayan and Tijana Milenkovic.
Multiple network alignment via multiMAGNA++
14:30-15:00 Marco Frasca and Nicolo Cesa-Bianchi.
Multi-Task Label Propagation with Dissimilarity Measures
15:00-15:30 Tanay Kumar Saha, Ataur Katebi and Mohammad Al Hasan.
Discovery of Functional Motifs from the Interface Region of Oligomeric Proteins using Frequent Subgraph Mining Method
15:30-16:00 Coffee Break
16:00-16:50 Invited Talk: Prof. Shuiwang Ji, Associate Professor, School of EECS, Washington State University, Pullman, WA.
16:50-17:00 Closing Remarks
Papers should be at most 10 pages long, single-spaced, in font size 10 or larger with one-inch margins on all sides. Using the ACM Proceedings Format is highly recommended. Paper should be submitted in PDF format through EasyChair at the following link: https://easychair.org/conferences/?conf=biokdd2016
Papers will be published in the webpage. A selection of accepted papers will also be invited to be submitted to a special section of the reputed IEEE Transactions on Computational Biology and Bioinformatics.
Information on past workshops is available at:http://home.biokdd.org/biokdd15/
For more information on data mining see SIGKDD and kdnuggets