Computational models for predicting drug responses in cancer research.
about
The cornucopia of meaningful leads: Applying deep adversarial autoencoders for new molecule development in oncologySystematic assessment of multi-gene predictors of pan-cancer cell line sensitivity to drugs exploiting gene expression data.A tool for discovering drug sensitivity and gene expression associations in cancer cells.Discovering novel pharmacogenomic biomarkers by imputing drug response in cancer patients from large genomics studies.A New View of Pathway-Driven Drug Resistance in Tumor Proliferation.Open source machine-learning algorithms for the prediction of optimal cancer drug therapies.Precision and recall oncology: combining multiple gene mutations for improved identification of drug-sensitive tumours.Global proteomics profiling improves drug sensitivity prediction: results from a multi-omics, pan-cancer modeling approach.Learning with multiple pairwise kernels for drug bioactivity prediction.CRISPR knockout screening identifies combinatorial drug targets in pancreatic cancer and models cellular drug responsePATRI, a Genomics Data Integration Tool for Biomarker Discovery
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P2860
Computational models for predicting drug responses in cancer research.
description
2016 nî lūn-bûn
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2016年の論文
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2016年学术文章
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2016年学术文章
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2016年学术文章
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2016年学术文章
@zh-my
2016年学术文章
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2016年學術文章
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2016年學術文章
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2016年學術文章
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name
Computational models for predicting drug responses in cancer research.
@en
type
label
Computational models for predicting drug responses in cancer research.
@en
prefLabel
Computational models for predicting drug responses in cancer research.
@en
P2860
P356
P1476
Computational models for predicting drug responses in cancer research.
@en
P2093
Francisco Azuaje
P2860
P356
10.1093/BIB/BBW065
P5008
P577
2016-07-21T00:00:00Z
2017-09-01T00:00:00Z