BIBM 2016 – Workshop

BIBM2016

Welcome to BIBM Workshop

Machine Learning and Big Data Research for Disease Classification and Complex Phenotyping

Disease classification is a fundamental problem in diagnostics, genetic association, and treatment matching and personalization. Refinement of disease classification can lead to customized treatment for a complex disease. With the advances and fast development in machine learning and big data techniques, there is great progress in disease classification. Different from general classification, disease classification has a variety of problems to deal with, such as missing values, heterogeneity across different data sources, the need to factor in biological knowledge and medical knowledge of a disorder and so on.
Novel methods, statistical models and software systems are needed to address the challenges in disease classification and complex phenotyping. Classic methods may not achieve the analytic goal in this area. For instance, multiple imputation may be insufficient to deal with the missing values that mix between obligated missing and random missing. Obligated-missing values actually encode important diagnostic information. The heterogeneous data dimensions in disease classification impose additional challenges. Sophisticated transfer learning (or domain adaptation) and multi-task learning might be feasible solutions, but additional caution may also be necessary in modeling temporal or spatial structures in the data. In certain cases, temporal modeling and spatial modeling are necessary, in which case tensor and network analysis may be meaningful.
Disease classification is usually along two lines: classifying by clinical manifestations or by etiology. Clinical classifications are often useful for treatment and management. Etiologic classifications can be more useful for prevention. For both methods, phenotyping is very important to characterize and represent the disease. Up to date, there is great progress in disease data acquisition and collection as well as in the development of machine learning and bioinformatics methods, which create a dedicated subarea to health care. This workshop aims to provide a forum for academic and industrial researchers and physicians to exchange research ideas/designs and share research findings to promote the development in this area.
 

Topics

In this workshop, we solicit papers that cover but are not limited to the following topics.

  • Novel mathematical and statistical models for disease classification
  • Case studies of various diseases related to classification or complex phenotyping
  • The integration of multi-scale data for disease classification
  • Feature selection and grouping strategies to facilitate disease classification
  • New methods to deal with missing values in well-defined study data or in electronic medical records
  • The application of big data technologies such as deep learning, parallel and distributed computing to disease data processing
  • Quantitative disease phenotyping from electronic medical records
  • Understanding clinical symptoms from the genetic perspective
  • Evaluation of whether a disease subtype predicts differences in treatment outcomes
  • Patient similarity learning
  • QTL or eQTL with multiple quantitative subphenotypes of a complex disorder
  • Studies that prove the advantages of disease classification
  • The identification of novel biomarkers for a disease that helps clarify the disease definition
Submission

Your paper should be formatted to IEEE Computer Society Proceedings Manuscript Formatting Guidelines
(http://www.ieee.org/conferences_events/conferences/publishing/templates.html).

Although we accept submissions in the form of PDF, PS, and DOC/RTF files, you are strongly encouraged to generate a PDF version for your paper submission if your paper was prepared in Word.

Please submit your paper at https://wi-lab.com/cyberchair/2016/bibm16/scripts/ws_submit.php

All papers accepted for workshops will be included in the Workshop Proceedings published by the IEEE Computer Society Press, which are indexed by EI.  Selected papers will have their extended versions published in a Special Issue of

IEEE/ACM Transactions on Computational Biology and Bioinformatics
Presentation schedule

9:10-9:25 Extreme Large Margin Distribution Machine and Its Applications for Biomedical Datasets. Presentation
Zhiyong Yang, Jingcheng Lu, and Taohong Zhang
9:25-9:40 Interpretable Models to Predict Breast Cancer
Pedro Ferreira, Inês Dutra, Rogerio Salvini, and Elizabeth Burnside
9:40-9:55 Replication and Validation of Genome-wide Associations with Feed Efficiency of Dairy Cattle. Presentation video
Tingyang Xu, Jiangwen Sun, Fatir Qureshi, Erin E. Connor, John B. Cole, and Jinbo Bi
9:55:10:10 A new feature selection approach for optimizing prediction models, applied to breast cancer subtype classification. Slides
Huy Pham, Alioune Ngom, and Luis Rueda
10:10-10:25 Research on Early Risk Predictive Model and Discriminative Feature Selection of Cancer Based on Real-world Routine Physical Examination Data
Guixia Kang and Zhuang Ni
10:25-10:40 Predicting the host of influenza viruses based on the word vector
Beibei Xu, Zhiying Tan, Kenli Li, Taijiao Jiang, and Yousong Peng
10:40-11:00 coffee break
11:00-11:15 Automated human physical function measurement using constrained high dispersal network with svm-linear. Slides
Dan Meng, Guitao Cao, Xinyu Song, Weiting Chen, and Wenming Cao
11:15-11:30 Layerwise feature selection in Stacked Sparse Auto-Encoder for tumor type prediction
Vikas Singh, Nikhil Baranwal, Rahul K. Sevakula, Nishchal K Verma, and Yan Cui
11:30-11:45 Improved Microarray Data Analysis using Feature Selection Methods with Machine Learning Methods
Kalpdrum Passi, Jing Sun, and Chakresh Kumar Jain
Organizing Chairs

Jinbo Bi, Associate Professor of Computer Science and Engineering Department, University of Connecticut
Guoqing Chao, NIH-funded Research Associate, Department of Computer Science, University of Connecticut
Yue Zhao, PhD candidate, Department of Computer Science, University of Connecticut
Program Committee

Le Lu, Staff Scientist, Department of Clinical Image Processing Service, National Institutes of Health
Sanguthevar Rajasekaran, Professor of Computer Science and Engineering, University of Connecticut
Xin Wang, Staff Scientist, Clinical Informatics Division, Phillip Global Research, Boston, MA
Shuiwang Ji, Associate Professor, Department of Electrical Engineering and Computer Science, Washington State University, WA
Jiangwen Sun, NIH-funded Research Associate, Department of Computer Science, University of Connecticut
Yu-Ping Wang, Associate Professor of Biomedical Engineering, Tulane University
Important Dates

Oct 30, 2016: Due date for full workshop papers submission
Nov 10, 2016: Notification of paper acceptance to authors
Nov 20,  2016: Camera-ready of accepted papers
Dec 15-18 2016: Workshops