A Survey and Systematic Assessment of Computational Methods for Drug Response Prediction

Chen, Jinyu; Zhang, Louxin

doi:10.1101/697896

Cited by 9 publications

(13 citation statements)

References 44 publications

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“…There are several reports of predicting drug response using ML or deep neural networks, often integrating molecular features 13–15 . Chen and Zhang did a recent survey and systematic assessment of four classical methods and 13 computational methods for drug response prediction using four public drug/response datasets.…”

Section: Preclinical Research (T1)mentioning

confidence: 99%

Artificial intelligence in clinical and translational science: Successes, challenges and opportunities

Bernstam

Shireman

Meric‐Bernstam

et al. 2021

Clinical Translational Sci

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Section: Preclinical Research (T1)mentioning

confidence: 99%

Artificial intelligence in clinical and translational science: Successes, challenges and opportunities

Bernstam

Shireman

Meric‐Bernstam

et al. 2021

Clinical Translational Sci

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“…Moreover, in drug-related tasks, irregular or non-Euclidean drug structures could be embedded through convolution neural networks (CNN) [8,17,18] and graph representations [19][20][21] to aid the modeling. Nevertheless, gene expression consistently shows the most informative in drug response prediction [22,23], whereas mutation and CNV profiles contribute little to improve the accuracy [24]. Although the integration of multiple omics profiles could improve the learning performance, its application is limited in practice where only gene expression data is often accessible.…”

Section: Introductionmentioning

confidence: 99%

TransCell: In silico characterization of genomic landscape and cellular responses from gene expressions through a two-step deep transfer learning

Yeh

Paithankar

Chen

et al. 2022

Preprint

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Gene expression profiling of new or modified cell lines becomes routine today; however, obtaining comprehensive molecular characterization and cellular responses for a variety of cell lines, including those derived from underrepresented groups, is not trivial when resources are minimal. Using gene expression to predict other measurements has been actively explored; however, systematic investigation of its predictive power in various measurements has not been well studied. We evaluate commonly used machine learning methods and present TransCell, a two-step deep transfer learning framework that utilizes the knowledge derived from pan-cancer tumor samples to predict molecular features and responses. Among these models, TransCell has the best performance in predicting metabolite, gene effect score (or genetic dependency), and drug sensitivity, and has comparable performance in predicting mutation, copy number variation, and protein expression. Notably, TransCell improved the performance by over 50% in drug sensitivity prediction and achieved a correlation of 0.7 in gene effect score prediction. Furthermore, predicted drug sensitivities revealed potential repurposing candidates for new 100 pediatric cancer cell lines, and predicted gene effect scores reflected BRAF resistance in melanoma cell lines. Together, we investigate the predictive power of gene expression in six molecular measurement types and develop a web portal (http://apps.octad.org/transcell/) that enables the prediction of 352,000 genomic and cellular response features solely from gene expression profiles.

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“…A wide range of new machine learning techniques have been explored, including recommendation systems [13], ranking methods [14,15], generative models [10,16], ensemble models [17,18,19,20,21], and deep learning approaches [22,23,24,25,26], with some incorporating novel design ideas such as attention [27] and visual representation of genomic features [28]. A number of excellent review articles have recently been published on the topic of drug response prediction, with substantial overlap and special emphases on data integration [29], feature selection [30], experimental comparison [31], machine learning methods [32], systematic benchmarking [33], combination therapy [34], deep learning results [35], and meta-review [36]. Despite the tremendous progress in drug response prediction, significant challenges remain: (1) Inconsistencies across studies in genomic and response profiling have long been documented [37,38,39,40].…”

Section: Introductionmentioning

confidence: 99%

A cross-study analysis of drug response prediction in cancer cell lines

Xia¹,

Allen²,

Balaprakash³

et al. 2021

Preprint

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To enable personalized cancer treatment, machine learning models have been developed to predict drug response as a function of tumor and drug features. However, most algorithm development efforts have relied on cross validation within a single study to assess model accuracy. While an essential first step, cross validation within a biological data set typically provides an overly optimistic estimate of the prediction performance on independent test sets. To provide a more rigorous assessment of model generalizability between different studies, we use machine learning to analyze five publicly available cell line-based data sets: NCI60, CTRP, GDSC, CCLE and gCSI. Based on observed experimental variability across studies, we explore estimates of prediction upper bounds. We report performance results of a variety of machine learning models, with a multitasking deep neural network achieving the best cross-study generalizability. By multiple measures, models trained on CTRP yield the most accurate predictions on the remaining testing data, and gCSI is the most predictable among the cell line data sets included in this study. With these experiments and further simulations on partial data, two lessons emerge: (1) differences in viability assays can limit model generalizability across studies, and (2) drug diversity, more than tumor diversity, is crucial for raising model generalizability in preclinical screening.

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A Survey and Systematic Assessment of Computational Methods for Drug Response Prediction

Cited by 9 publications

References 44 publications

Artificial intelligence in clinical and translational science: Successes, challenges and opportunities

Artificial intelligence in clinical and translational science: Successes, challenges and opportunities

TransCell: In silico characterization of genomic landscape and cellular responses from gene expressions through a two-step deep transfer learning

A cross-study analysis of drug response prediction in cancer cell lines

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