Integrative computational biology

点击翻译按钮获取中文摘要

Objective: Integrative computational biology is a necessary platform to fathom biology of complex diseases and impact individualized medicine. However, advancing computational tools in isolation is insufficient to impact computational biology. Many theoretically excellent approaches are inadequate for the high-throughput biological domains, due to the scale or complexity of the problem, or due to using unrealistic assumptions. Forming hypotheses and developing computational models of biological systems without the possibility to test them limits the potential to derive realistic models. We propose to improve scalability robustness, sensitivity and specificity of pattern discovery algorithms, and integrate them to support a methodical approach to the systems biology analysis and visualization of high-throughput data in cancer research.   Approach: To address challenges in cancer informatics, we propose to: 1) integrate multiple data resources into biological data marts; 2) design tools for systematic, network-based analysis of heterogeneous biological data; 3) apply the tools to modeling epithelial cancers and initiate validation studies via collaborations. We will augment the tools with statistical and machine learning algorithms to enable probabilistic reasoning, deterministic and sound pattern identification, stability, scalability and robustness. There are three main areas of algorithm development: 1) clustering and data mining algorithms for cancer profiles; 2) graph theory algorithms for protein interaction networks; and 3) integration and visualization of results in the context of networks, to aid in hypotheses generation, interpretation of results, and model creation.      Significance: We aim to develop integrated and scalable algorithms, and apply them to realize intelligent molecular medicine. Results from these analyses will be integrated into the computational models, which will improve their accuracy and thus applicability, and in turn will impact in the respective fields. An important function of this proposal is the training of bioinformatics professionals for which there is still a severe deficit. We will release tools and resources for free academic use.