Workshop on

High-Throughput Data Analysis for Proteomics and Genomics

in conjunction with BIBM


High-throughput (HT) data acquisition technologies, such as DNA microarray, protein micrarray, tissue microarray, mass spectrometry, 2D gel or fluorescent microscopy, have being increasingly used in basic biomedicine and clinics for a wide range of biomedical applications.  Advancement in computational data analysis is necessitated to keep pace with new HT technologies due to the nature of biological data, for example, small sample size while high-dimensional features (genes, proteins). With the enormous observation sets, biologists seek to make sense of this growing body of data from which meaningful inferences about biological processes can be drawn. Because of the uniqueness of HT data, classical computational data analysis methods need to be fostered and revisited to meet the indispensable needs from the wide biomedical community.

The goal of this workshop is to bring together researchers working on high-throughput data analysis to share and communicate the technical challenges and discoveries. All accepted papers will be included in the Workshop Proceedings published by the IEEE Computer Society Press. Research papers are solicited in, but not limited to, the following topics:

Applications of high-throughput (HT) data analysis to computational biology problems:

        DNA microarray data analysis

        Protein microarray data analysis

        Gene/Protein biomarker discovery

        Pathway study

        Sample prediction/classification

        Mass spectrometry data analysis

        2D gel analysis

        Gene regulatory network study

        Protein-protein, protein-gene interactions

        Protein selection and sequencing


Computational advances in HT data analysis:

        Supervisied-, Unsupervised-, and Semi-supervised Feature selection    for high dimension data

        Binary or multi-class Classification


        Outlier removal

        Small sample size and classification/clustering performance

        Data preprocessing

        Performance evaluation

        Graph theory

        Pattern discovery

        Statistical signal processing