Dataset Information

Machine learning from large-scale proteomics accurately ranks anti-cancer drugs based on efficacy

ABSTRACT: We present an approach, named Drug Ranking Using ML (DRUML), which uses omics data to produce ordered lists of > 400 drugs based on their effectiveness in decreasing cancer cell proliferation. We trained and validated DRUML using in-house proteomics and phosphoproteomics data from a panel of 26 AML, 10 esophageal and 12 hepatocellular carcinoma cell lines in triplicate (three independent cultures per cell line) by LC-MS/MS

INSTRUMENT(S): Q Exactive Plus

ORGANISM(S): Homo Sapiens (human)

TISSUE(S): Myeloblast, Hepatocyte, Epithelial Cell Of Esophagus, Peripheral Blood, Epithelial Cell, Esophageal Epithelial Cell

DISEASE(S): Acute Myeloid Leukemia,Esophageal Cancer,Hepatocellular Carcinoma

SUBMITTER: Maruan Hijazi

LAB HEAD: Pedro Cutillas

PROVIDER: PXD019591 | Pride | 2021-02-22

REPOSITORIES: Pride

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Publications

Drug ranking using machine learning systematically predicts the efficacy of anti-cancer drugs.

Gerdes Henry H Casado Pedro P Dokal Arran A Hijazi Maruan M Akhtar Nosheen N Osuntola Ruth R Rajeeve Vinothini V Fitzgibbon Jude J Travers Jon J Britton David D Khorsandi Shirin S Cutillas Pedro R PR

Nature communications 20210325 1

Artificial intelligence and machine learning (ML) promise to transform cancer therapies by accurately predicting the most appropriate therapies to treat individual patients. Here, we present an approach, named Drug Ranking Using ML (DRUML), which uses omics data to produce ordered lists of >400 drugs based on their anti-proliferative efficacy in cancer cells. To reduce noise and increase predictive robustness, instead of individual features, DRUML uses internally normalized distance metrics of d ...[more]

PMID: 33767176

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Project description:In gas chromatography-mass spectrometry-based untargeted metabolomics, metabolites are identified by comparing mass spectra and chromatographic retention time with reference databases or standard materials. In that sense, machine learning has been used to predict the retention time of metabolites lacking reference data. However, the retention time prediction of trimethylsilyl derivatives of metabolites, typically analyzed in untargeted metabolomics using gas chromatography, has been poorly explored. Here, we provide a rationalized framework for machine learning-based retention time prediction of trimethylsilyl derivatives of metabolites in gas chromatography. We compared different machine learning paradigms, in addition to exploring the influence of the computational molecular structure representation to train the prediction models: fingerprint class and fingerprint calculation software. Our study challenged predicted retention time when using chemical ionization and electron impact ionization sources in simulated and real cases, demonstrating a good correct identity ranking capability by machine learning, despite observing a limited false identity filtering power in cases where a spectrum or a monoisotopic mass match to multiple candidates. Specifically, machine learning prediction yielded median absolute and relative retention index (relative retention time) errors of 37.1 retention index units and 2%, respectively. In addition, fingerprint class and fingerprint calculation software, as well as the molecular structural similarity between the training and test or real case sets, showed to be critical modulators of the prediction performance. Finally, we leveraged the structural similarity between the training and test or real case set to determine the probability that the prediction error is below a specific threshold. Overall, our study demonstrates that predicted retention time can provide insights into the true structure of unknown metabolites by ranking from the most to the least plausible molecular identity, and sets the guidelines to assess the confidence in metabolite identification using predicted retention time data.

Dataset Information

Machine learning from large-scale proteomics accurately ranks anti-cancer drugs based on efficacy

Publications

Drug ranking using machine learning systematically predicts the efficacy of anti-cancer drugs.

Similar Datasets

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