Deep Graph and Sequence Representation Learning for Drug Response Prediction

Yan, Xiangfeng; Liu, Yong; Zhang, Wei

doi:10.1007/978-3-031-15919-0_9

Cited by 2 publications

(1 citation statement)

References 24 publications

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“…In fact, in a related field of molecular property prediction, a comparison with multiple datasets and prediction tasks suggests that on average models that use FPs or descriptors outperform graph-based models ( 154 ). We have found only one study comparing side-by-side the added value of molecular graphs against SMILES, reporting <0.5% improvement as evaluated by Pearson correlation coefficient ( 155 ). As in the case of cancer representation, further studies are required across various models and datasets to assess the predictive capabilities of molecular graphs and other drug representations to DRP.…”

Section: Discussionmentioning

confidence: 99%

Deep learning methods for drug response prediction in cancer: Predominant and emerging trends

Partin

Brettin²,

Zhu³

et al. 2023

Front. Med.

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Cancer claims millions of lives yearly worldwide. While many therapies have been made available in recent years, by in large cancer remains unsolved. Exploiting computational predictive models to study and treat cancer holds great promise in improving drug development and personalized design of treatment plans, ultimately suppressing tumors, alleviating suffering, and prolonging lives of patients. A wave of recent papers demonstrates promising results in predicting cancer response to drug treatments while utilizing deep learning methods. These papers investigate diverse data representations, neural network architectures, learning methodologies, and evaluations schemes. However, deciphering promising predominant and emerging trends is difficult due to the variety of explored methods and lack of standardized framework for comparing drug response prediction models. To obtain a comprehensive landscape of deep learning methods, we conducted an extensive search and analysis of deep learning models that predict the response to single drug treatments. A total of 61 deep learning-based models have been curated, and summary plots were generated. Based on the analysis, observable patterns and prevalence of methods have been revealed. This review allows to better understand the current state of the field and identify major challenges and promising solution paths.

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Section: Discussionmentioning

confidence: 99%

Deep learning methods for drug response prediction in cancer: Predominant and emerging trends

Partin

Brettin²,

Zhu³

et al. 2023

Front. Med.

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MCMVDRP: a multi-channel multi-view deep learning framework for cancer drug response prediction

Li,

Shi,

et al. 2024

Journal of Integrative Bioinformatics

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Drug therapy remains the primary approach to treating tumours. Variability among cancer patients, including variations in genomic profiles, often results in divergent therapeutic responses to analogous anti-cancer drug treatments within the same cohort of cancer patients. Hence, predicting the drug response by analysing the genomic profile characteristics of individual patients holds significant research importance. With the notable progress in machine learning and deep learning, many effective methods have emerged for predicting drug responses utilizing features from both drugs and cell lines. However, these methods are inadequate in capturing a sufficient number of features inherent to drugs. Consequently, we propose a representational approach for drugs that incorporates three distinct types of features: the molecular graph, the SMILE strings, and the molecular fingerprints. In this study, a novel deep learning model, named MCMVDRP, is introduced for the prediction of cancer drug responses. In our proposed model, an amalgamation of these extracted features is performed, followed by the utilization of fully connected layers to predict the drug response based on the IC50 values. Experimental results demonstrate that the presented model outperforms current state-of-the-art models in performance.

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Deep Graph and Sequence Representation Learning for Drug Response Prediction

Cited by 2 publications

References 24 publications

Deep learning methods for drug response prediction in cancer: Predominant and emerging trends

Deep learning methods for drug response prediction in cancer: Predominant and emerging trends

MCMVDRP: a multi-channel multi-view deep learning framework for cancer drug response prediction

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