Computational Cancer Biology 2016
2016-02-01..2016-02-05
Berkeley, United States
| title | author | length |
|---|---|---|
| The Current State of Mutation Detection and Prospects for Long Read Sequencing | Jared Simpson | 34m |
Consortium processes 2,800 tumor‑normal pairs (≈500 TB) across ~16,000 cloud cores and validates mutations by NimbleGen capture of ~130,000 sites with ~1,000×… | ||
| Modeling Cancer Evolution from Genomic Data | Niko Beerenwinkel | 36m |
Single-cell phylogenies are reconstructed from noisy binary mutation matrices using an MCMC prune-and-regraft sampler that integrates out sample attachments… | ||
| Interaction-based Methods for Uncovering Genes Functionally Important in Cancers | Mona Singh | 34m |
Pipeline maps tumor missense variants onto homologous structures requiring 60% sequence identity and models about 22% of human proteins, finding binding… | ||
| Joint Analysis of Multiple Cancer Types for Revealing Disease-Specific Genomic Events | Ron Shamir, David Amar | 30m |
Joint analysis uses a curated compendium of 13,000 GEO profiles (170 datasets, 17 platforms) and COSMIC whole‑exome calls (~9,000 patients plus TCGA) mapped to… | ||
| Somatic Mutations that Alter RNA Splicing in Human Cancers | Angela Brooks | 28m |
Uses matched exome and RNA-Seq from TCGA and JunctionBase to call unannotated splicing changes and quantify PSI, links U2AF1 S34 hotspot to altered three‑prime… | ||
| Algorithms for Population Genomics and Cancer Genomics | Serafim Batzoglou | 36m |
Serafim Batzoglou, Stanford University
Computational Cancer Biology
https://simons.berkeley.edu/talks/serafim-batzoglou-02-02-2016 | ||
| Mechanisms of Amplification in Tumor Genomes | Vineet Bafna | 38m |
Focuses on focal amplifications and tests three mechanisms: breakage–fusion–bridge (BFB), chromothripsis, and episome/double-minute amplification. Presents a… | ||
| Challenges and Opportunities in Intelligent Molecular Medicine | Igor Jurisica | 34m |
Integrative pipelines combine TCGA genomics, proteomics and curated interaction databases to derive actionable models: a 14‑gene prognostic signature for… | ||
| Fast and Scalable Inference of Cancer Cell Lineages Using Multi-sample Deep Sequencing Somatic SNVs | Victoria Popic | 20m |
A tool called litchi infers tumor lineage trees from deep multisample somatic SNV frequencies by grouping variants into binary presence profiles, GMM… | ||
| Molecular Data Integration for Precision Medicine in Breast Cancer | Laura Van 'T Veer | 36m |
A multi-arm adaptive platform called ISPY2 adaptive trial runs on newly diagnosed high‑risk breast cancer with a six‑month neoadjuvant endpoint, 1,700 screened… | ||
| Molecular Characterization for Diagnoses and Treatment | David Haussler | 55m |
Proposes a genome graph reference that stores common variants (roughly ≥1%) as DNA-string nodes with bi‑directed joins; builders used CACTUS, de Bruijn and the… | ||
| CoMEt: A Statistical Approach to Identify Combinations of Mutually Exclusive Alterations in Cancer | Max Leiserson | 16m |
CoMEt introduces a contingency-table score and an algorithm that computes an exact tail p-value for mutual exclusivity in k-gene sets by enumerating only tail… | ||
| Finding Mutated Subnetworks Associated with Survival in Cancer | Fabio Vandin | 33m |
Proposes an algorithm (NoMAS) that uses color-coding plus a network diffusion on network step to search for connected k-gene subnetworks maximizing a… | ||
| Diverse High Throughput Technologies in Cancer Research and Synthetic Biology | Zohar Yakhini | 31m |
Joint analysis links matched miRNA and mRNA from ~100 breast tumors using minimum hypergeometric tests on Agilent arrays to show miR‑29 anti‑correlates with… | ||
| The Copy Number Transformation Problem | Meirav Zehavi | 14m |
Defines copy-number profiles as integer vectors and operations as unit ±1 operations on contiguous intervals; proves each position needs at most max copy… | ||
| Inferring Selective Advantage Relationships to Reconstruct Cancer Progression Models | Marco Antoniotti | 22m |
Algorithms named Capri and Caprese reconstruct cancer progression as directed relations of selective advantage using the Suppes probabilistic causality score… | ||
| Toward Pathway-and Pancancer-Guided Interpretation of an Individual’s Cancer Genome | Josh Stuart | 36m |
Reference maps from a pan-cancer atlas (11,000 samples, 33 tumor types) place individual tumors by molecular phenotype, flag ~20–25% of cases that cross tissue… | ||
| Somatic Structural Variation Discovery in Multiple Cancer Genomes | Iman Hajirasouliha | 10m |
A pipeline called LAB-C takes multiple whole-genome BAMs plus targeted deep sequencing of ~1,000 somatic SNVs, builds a tumor lineage with LICHeE, clusters… | ||
| Towards Inference of Fitness Landscapes in Human Cancer | Sohrab Shah | 40m |
Introduces three concrete tools and results: X-seq integrates mutations with gene expression across ~3,000 TCGA tumors (≈500,000 mutations) and flags ~30 novel… | ||
| Tumor Evolution - Simplicity and Constraints | Paul Spellman | 45m |
Compares 42 tumors from 6 patients using whole-genome 40x plus deep exomes 150–200x, mutation calling and copy‑number analysis against TCGA. Finds 3p loss in… | ||
| Inference of Personalized Drug Targets via Network Propagation | Dana Silverbush | 19m |
Method simulates in silico knockouts on the HIPPY PPI network using random-walk with restart from patient mutations, repeats 1,000 random healthy propagations… | ||
| Complexity Issues in Rearrangement Evolution | Chris Greenman | 27m |
Counts explicit evolutionary sequences for common rearrangements: one breakage–fusion–bridge yields 1 outcome, two yields 2, three breakpoints give 2^k choices… | ||
| Inferring Evolution by Copy Number Variations in Tumor Cell Populations | Russell Schwartz | 35m |
Uses FISH with 8–15 probes on hundreds of single cells per tumor to record copy number gains and losses, models evolution as probe-wise gains/losses mapped to… | ||
| Interpreting Cancer Genomes with Network Knowledge | Trey Ideker | 31m |
Argument: genome sequencing alone cannot recover most cancer genes because mutations are rare per patient and sit in a long tail. A pipeline propagates… | ||
| Multi-State Perfect Phylogeny Mixture Deconvolution and Applications to Cancer Sequencing | Mohammed El Kebir | 17m |
Defines the multi-state perfect phylogeny mixture deconvolution problem as factoring a variant-allele frequency tensor into a shared usage matrix shared across… | ||
| Optimizing Combination Cancer Therapy Based on Single Cell Analysis | Sylvia Plevritis | 37m |
Single-cell mass cytometry measuring ~40 markers per cell is ordered into a Spade minimum spanning tree that recovers developmental branches, then populations… | ||
| Survival Time Prediction from Mutation Profiles Using Gene Networks | Marine Le Morvan | 15m |
Uses TCGA exome binary matrices and models survival with a linear model learned by ranking SVM, reporting concordance around 0.5–0.55 for raw mutation… | ||
| Modeling Mutational Patterns in Tumor Genomic Data | Ewa Szczurek | 28m |
Proposes a muX generative model that jointly models coverage, impurity and false positives/negatives and a waiting-time model that explains mutual exclusivity… | ||
| Mutual Exclusivity of Mutations Across Different Cancer Types - A Molecular Network Perspective | Teresa Przytycka | 34m |
Teresa Przytycka, National Institutes of Health
Computational Cancer Biology
https://simons.berkeley.edu/talks/teresa-przytycka-02-04-2016 | ||
| BitPhylogeny: A Probabilistic Framework for Reconstructing Intra-tumor Phylogenies | Thomas Sakoparnig | 13m |
Probabilistic pipeline reconstructs intra-tumor phylogenies from phased reads using a tree-structured Dirichlet process prior that yields node masses summing… | ||
| Expression Analysis of Tumors Based on Patterns in Alternative Splicing | Elizabeth Purdom | 35m |
Presents an empirical Bayes workflow that models exon inclusion as an overdispersed binomial with empirical Bayes by using Efron’s dispersion trick, showing… | ||
| Rank-Based Representations to Learn from Omics Data | Jean-Philippe Vert | 41m |
Proposes using rank-based representations—quantile normalization and pairwise comparisons—to change omics feature space, and jointly optimizes the quantile… | ||
| nothing matches | ||