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Tel Aviv University, Blavatnik School of Computer
Science
Topic *= material not covered in 2017
class |
2017 Presentations |
Newest notes |
Older notes |
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Introductory
topics |
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Hypothesis
testing, FDR |
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Survival
analysis |
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Gene
enrichment analysis: GO, TANGO, GSEA |
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Regulatory
motif discovery |
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Introduction,
Prima, MEME, Amadeus |
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DREME,
GREAT |
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Clustering |
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k-means,
SOM, graph formulations, PCC*, CAST* |
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Click,
hierarchical clustering |
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HCS
for clustering cDNA* |
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FPF,
K-Boost, PCA |
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PCA,
TSNE |
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Consensus
clustering |
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Community
detection* |
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Biclustering |
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Cheng
& Church* |
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Samba,
ISA, CTWC*, Ping-Pong |
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Plaid
models* |
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Order-preserving
submatrix problem (OPSM)* |
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Bimax |
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Classification |
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Introduction,
dimension reduction |
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Classification
in Cancer |
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Support
vector machines (SVM) |
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Classification
in Parkinson's Disease |
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Multi
Omics Clustering (lecture by Nimrod Rappoport) |
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CoCA,
iCluster, SNF, NMF, MKL |
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Multi
Omics Integration |
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Paradigm,
Paradigm-Shift |
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ResponseNet |
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PCSF |
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Systems
Genetics (lecture by Prof. Irit Gat-Viks) |
See moodle |
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Networks
and Clinical data |
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Matisse
and Cezanne |
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CRC
prediction |
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PPGR prediction |
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Drug
targets and side effects (lecture by Prof. Roded Sharan) |
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Inference
in Networks |
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Genetic
networks |
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Bayesian
networks |
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Metabolic
networks |
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Protein
interaction networks |
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Microarray
normalization and analysis of differential expression |
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Integrated
analysis of gene expression and other data |
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Sequencing
by hybridization (SBH) |
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Sequence
reconstruction, positional SBH |
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Resequencing,
genotyping, gapped chips, long targets, errors |
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Universal
tag arrays |
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