Reconstructing cancer drug response networks using multitask learning

Translating in vitro results to clinical tests is a major challenge in systems biology. Here we present a new Multi-Task learning framework which integrates thousands of cell line expression experiments to reconstruct drug specific response networks in cancer. The reconstructed networks correctly id...

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Published in:BMC systems biology 2017-10, Vol.11 (1), p.96-96, Article 96
Main Authors: Ruffalo, Matthew, Stojanov, Petar, Pillutla, Venkata Krishna, Varma, Rohan, Bar-Joseph, Ziv
Format: Article
Language:English
Subjects:
Apoptosis
Breast cancer
Cancer
Cell growth
Drugs
Experiments
Gene expression
Genomes
Kinases
Learning
Melanoma
Methods
Networks
Prostate cancer
Proteins
Usability
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cited_by cdi_FETCH-LOGICAL-c427t-748e70f22870dbdd836f7405ddcb139bdd81479438e6010c19c1d644787f0d6f3
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container_title BMC systems biology
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creator Ruffalo, Matthew
Stojanov, Petar
Pillutla, Venkata Krishna
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Bar-Joseph, Ziv
description Translating in vitro results to clinical tests is a major challenge in systems biology. Here we present a new Multi-Task learning framework which integrates thousands of cell line expression experiments to reconstruct drug specific response networks in cancer. The reconstructed networks correctly identify several shared key proteins and pathways while simultaneously highlighting many cell type specific proteins. We used top proteins from each drug network to predict survival for patients prescribed the drug. Predictions based on proteins from the in-vitro derived networks significantly outperformed predictions based on known cancer genes indicating that Multi-Task learning can indeed identify accurate drug response networks.
doi_str_mv 10.1186/s12918-017-0471-8
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subjects Apoptosis
Breast cancer
Cancer
Cell growth
Drugs
Experiments
Gene expression
Genomes
Kinases
Learning
Melanoma
Methods
Networks
Prostate cancer
Proteins
Usability
title Reconstructing cancer drug response networks using multitask learning
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