Learning Feature-Based Protein-DNA Recognition Models from SELEX Data

Channel:
United States Simons Institute for the Theory of Computing
Subscribers:
68,700
Published on ● Video Link: https://www.youtube.com/watch?v=mC1T-NopcO0


Duration: 31:47
548 views
6

Harmen Bussemaker, Columbia University
Regulatory Genomics and Epigenomics
https://simons.berkeley.edu/talks/harmen-bussemaker-03-07-16



Other Videos By Simons Institute for the Theory of Computing


2016-04-04Categories, Representations, and Counting Complexity
2016-04-04Counting with Bounded Treewidth
2016-03-29The Complexity of Computing Quantities
2016-03-22Decoding the Human Microbiome
2016-03-21Threshold Phenomena in Combinatorics
2016-03-11Discovery of Transcription Factor Binding Motifs from Large Sequence Sets
2016-03-11Algorithms for Single Cell RNAseq Analysis
2016-03-11Spectral Algorithms for Learning HMMs and Tree HMMs for Epigenetics Data
2016-03-11Accurate, Fast, and Model-Aware Transcript Expression Quantification with Salmon
2016-03-11Reconstructing Dynamic Signaling and Regulatory Networks
2016-03-11Learning Feature-Based Protein-DNA Recognition Models from SELEX Data
2016-03-11Deep Learning Frameworks for Regulatory Genomics and Epigenomics
2016-03-11Mapping Nucleosome Positions Using DNase
2016-03-11Genome in 3D: Modeling Chromosome Organization
2016-03-11Computational Genomics of Post-Transcriptional Gene Regulation
2016-03-11Modeling Gene Expression and Chromatin State in Terms of Regulatory Sites
2016-03-11Challenges in Sequence-to-Expression Modeling
2016-03-11Understanding DNA-binding Preferences of Transcription Factor Homologs
2016-03-11Selecting Genomics Assays
2016-03-11Personal Transcription Factor Binding Site Mutations Point to Personal Medical Histories
2016-03-11Elucidating Sequence-Structure Binding Motifs by Uncovering Selection Trends in HT-SELEX Experiments


Tags:
Simons Institute
UC Berkeley
computer science
theory of computing
Algorithmic Challenges in Genomics
Harmen Bussemaker