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source: Simons Institute 2016年2月5日
Computational Cancer Biology Feb. 1 – Feb. 5, 2016
Computational Cancer Biology is a rapidly expanding area, utilizing deep sequencing techniques (“Next Generation Sequencing”) that facilitate the sequencing of tens of thousands of tumor genomes, along with other matching information. Large international projects are collecting and organizing this data, but developing powerful algorithms for the data analysis is a bottleneck. Current analysis techniques combine graph theoretic and machine learning approaches. One such line of work builds on the rich combinatorial and algorithmic theory of genome rearrangements. Another aims to improve classification of cancer patients and reveal biomarkers for specific disease subtypes, with the goal of improved diagnosis, prognosis and patient stratification. This workshop aims to survey the state of the art in this field and explore new algorithmic approaches with potentially large impact.
For more information, please visit https://simons.berkeley.edu/workshops/genomics2016-1.
These presentations were supported in part by an award from the Simons Foundation.
The Current State of Mutation Detection and Prospects for Long Read Sequencing 33:54 Jared Simpson, Ontario Institute for Cancer Research https://simons.berkeley.edu/talks/jar...
Modeling Cancer Evolution from Genomic Data 35:40
Interaction-based Methods for Uncovering Genes Functionally Important in Cancers 33:35
Joint Analysis of Multiple Cancer Types for Revealing Disease-Specific Genomic Events 30:12
Somatic Mutations that Alter RNA Splicing in Human Cancers 28:20
Algorithms for Population Genomics and Cancer Genomics 36:09
Mechanisms of Amplification in Tumor Genomes 38:09
Challenges and Opportunities in Intelligent Molecular Medicine 34:26
Fast and Scalable Inference of Cancer Cell Lineages Using Multi-sample Deep Sequencing Somatic SNVs 19:53
Molecular Data Integration for Precision Medicine in Breast Cancer 35:49
Molecular Characterization for Diagnoses and Treatment 55:26
CoMEt: A Statistical Approach to Identify Combinations of Mutually Exclusive Alterations in Cancer 16:13
Finding Mutated Subnetworks Associated with Survival in Cancer 32:59
Diverse High Throughput Technologies in Cancer Research and Synthetic Biology 31:28
The Copy Number Transformation Problem 14:14
Inferring Selective Advantage Relationships to Reconstruct Cancer Progression Models 21:50
Toward Pathway-and Pancancer-Guided Interpretation of an Individual’s Cancer Genome 36:14
Somatic Structural Variation Discovery in Multiple Cancer Genomes 10:26
Towards Inference of Fitness Landscapes in Human Cancer 40:25
Tumor Evolution - Simplicity and Constraints 45:26
Inference of Personalized Drug Targets via Network Propagation 18:37
Complexity Issues in Rearrangement Evolution 27:25
Inferring Evolution by Copy Number Variations in Tumor Cell Populations 35:23
Interpreting Cancer Genomes with Network Knowledge 31:09
Multi-State Perfect Phylogeny Mixture Deconvolution and Applications to Cancer Sequencing 17:29
Optimizing Combination Cancer Therapy Based on Single Cell Analysis 36:40
Survival Time Prediction from Mutation Profiles Using Gene Networks 15:18
Modeling Mutational Patterns in Tumor Genomic Data 27:43
Mutual Exclusivity of Mutations Across Different Cancer Types - A Molecular Network Perspective 33:43
BitPhylogeny: A Probabilistic Framework for Reconstructing Intra-tumor Phylogenies 12:40
Expression Analysis of Tumors Based on Patterns in Alternative Splicing 34:47
Rank-Based Representations to Learn from Omics Data 41:22
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