MOLI: multi-omics late integration with deep neural networks for drug response prediction

Sharifi-Noghabi H, Zolotareva O, Collins CC, Ester M (2019)
Bioinformatics 35(14): I501-I509.

Konferenzbeitrag | Veröffentlicht | Englisch
 
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Abstract / Bemerkung
Motivation Historically, gene expression has been shown to be the most informative data for drug response prediction. Recent evidence suggests that integrating additional omics can improve the prediction accuracy which raises the question of how to integrate the additional omics. Regardless of the integration strategy, clinical utility and translatability are crucial. Thus, we reasoned a multi-omics approach combined with clinical datasets would improve drug response prediction and clinical relevance. Results We propose MOLI, a multi-omics late integration method based on deep neural networks. MOLI takes somatic mutation, copy number aberration and gene expression data as input, and integrates them for drug response prediction. MOLI uses type-specific encoding sub-networks to learn features for each omics type, concatenates them into one representation and optimizes this representation via a combined cost function consisting of a triplet loss and a binary cross-entropy loss. The former makes the representations of responder samples more similar to each other and different from the non-responders, and the latter makes this representation predictive of the response values. We validate MOLI on in vitro and in vivo datasets for five chemotherapy agents and two targeted therapeutics. Compared to state-of-the-art single-omics and early integration multi-omics methods, MOLI achieves higher prediction accuracy in external validations. Moreover, a significant improvement in MOLI's performance is observed for targeted drugs when training on a pan-drug input, i.e. using all the drugs with the same target compared to training only on drug-specific inputs. MOLI's high predictive power suggests it may have utility in precision oncology. Availability and implementation https://github.com/hosseinshn/MOLI. Supplementary information Supplementary data are available at Bioinformatics online.
Erscheinungsjahr
2019
Band
35
Ausgabe
14
Seite(n)
I501-I509
Konferenz
Biennial Joint Meeting of the 27th Annual Conference on Intelligent Systems for Molecular Biology (ISMB) / 18th European Conference on Computational Biology (ECCB)
Konferenzort
Basel, SWITZERLAND
ISSN
1367-4803
eISSN
1460-2059
Page URI
https://pub.uni-bielefeld.de/record/2936939

Zitieren

Sharifi-Noghabi H, Zolotareva O, Collins CC, Ester M. MOLI: multi-omics late integration with deep neural networks for drug response prediction. Bioinformatics. 2019;35(14):I501-I509.
Sharifi-Noghabi, H., Zolotareva, O., Collins, C. C., & Ester, M. (2019). MOLI: multi-omics late integration with deep neural networks for drug response prediction. Bioinformatics, 35(14), I501-I509. doi:10.1093/bioinformatics/btz318
Sharifi-Noghabi, H., Zolotareva, O., Collins, C. C., and Ester, M. (2019). MOLI: multi-omics late integration with deep neural networks for drug response prediction. Bioinformatics 35, I501-I509.
Sharifi-Noghabi, H., et al., 2019. MOLI: multi-omics late integration with deep neural networks for drug response prediction. Bioinformatics, 35(14), p I501-I509.
H. Sharifi-Noghabi, et al., “MOLI: multi-omics late integration with deep neural networks for drug response prediction”, Bioinformatics, vol. 35, 2019, pp. I501-I509.
Sharifi-Noghabi, H., Zolotareva, O., Collins, C.C., Ester, M.: MOLI: multi-omics late integration with deep neural networks for drug response prediction. Bioinformatics. 35, I501-I509 (2019).
Sharifi-Noghabi, Hossein, Zolotareva, Olga, Collins, Colin C., and Ester, Martin. “MOLI: multi-omics late integration with deep neural networks for drug response prediction”. Bioinformatics 35.14 (2019): I501-I509.

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