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Improving prediction of phenotypic drug response on cancer cell lines using deep convolutional network

Pengfei Liu, Hongjian Li, Shuai Li, Kwong-Sak Leung
2019 BMC Bioinformatics  
Understanding the phenotypic drug response on cancer cell lines plays a vital role in anti-cancer drug discovery and re-purposing.  ...  After that, a fully connected network is used to predict the interaction between the drugs and the cancer cell lines.  ...  Acknowledgements We would like to thank the reviewers for their detailed suggestions which greatly improved the quality and readability of this work. 1  ... 
doi:10.1186/s12859-019-2910-6 fatcat:cxrbfrz3urfc5mcsitln2flpva

How much can deep learning improve prediction of the responses to drugs in cancer cell lines?

Yurui Chen, Louxin Zhang
2021 Briefings in Bioinformatics  
Deep neural networks have been applied to the multi-omics data being available for over 1000 cancer cell lines and tissues for better drug response prediction.  ...  Although significant progresses have been made in deep learning approach in drug response prediction, deep learning methods show their weakness for predicting the response of a drug that does not appear  ...  They also thank anonymous reviewers for their useful comments on the 1st version of the manuscript, which improve significantly our work.  ... 
doi:10.1093/bib/bbab378 pmid:34529029 fatcat:b2hgnfdocjhvxgw4aldzndt7rq

Drug cell line interaction prediction [article]

Pengfei Liu
2018 arXiv   pre-print
Understanding the phenotypic drug response on cancer cell lines plays a vital rule in anti-cancer drug discovery and re-purposing.  ...  SMILES format and cancer cell lines respectively.  ...  of phenotypic drug response on cancer cell lines.  ... 
arXiv:1812.11178v1 fatcat:xkl5d76byfgjrenib3obnhjgoe

Machine learning approaches to drug response prediction: challenges and recent progress

George Adam, Ladislav Rampášek, Zhaleh Safikhani, Petr Smirnov, Benjamin Haibe-Kains, Anna Goldenberg
2020 npj Precision Oncology  
Identifying the best treatment using computational models to personalize drug response prediction holds great promise to improve patient's chances of successful recovery.  ...  This review provides an overview of the computational challenges and advances in drug response prediction, and focuses on comparing the machine learning techniques to be of utmost practical use for clinicians  ...  Predictive drug response models, based on ridge regression, were built using expression profiles of cancer cell lines from a publicly available drug screening dataset 91, 92 to predict response to the  ... 
doi:10.1038/s41698-020-0122-1 pmid:32566759 pmcid:PMC7296033 fatcat:wznpdjf2ojgubbcgw3x4asrrgy

DeepCDR: a hybrid graph convolutional network for predicting cancer drug response [article]

Qiao Liu, Zhiqiang Hu, Rui Jiang, Mu Zhou
2020 bioRxiv   pre-print
In this study, we present DeepCDR which integrates multi-omics profiles of cancer cells and explores intrinsic chemical structures of drugs for predicting cancer drug response.  ...  Accurate prediction of cancer drug response (CDR) is challenging due to the uncertainty of drug efficacy and heterogeneity of cancer patients.  ...  Conflict of Interest: none declared.  ... 
doi:10.1101/2020.07.08.192930 fatcat:fdxiw62x5rahzjuj5isitldkea

A Deep Learning Model for Cell Growth Inhibition IC50 Prediction and Its Application for Gastric Cancer Patients

Minjae Joo, Aron Park, Kyungdoc Kim, Won-Joon Son, Hyo Sug Lee, GyuTae Lim, Jinhyuk Lee, Dae Ho Lee, Jungseok An, Jung Ho Kim, TaeJin Ahn, Seungyoon Nam
2019 International Journal of Molecular Sciences  
Here, we constructed a 1-dimensional convolution neural network model, DeepIC50, to predict three drug responsiveness classes, based on 27,756 features including mutation statuses and various drug molecular  ...  DeepIC50 could possibly accurately predict drug responsiveness, to new compounds, in diverse cancer cell lines, in the drug discovery process.  ...  Recently however, a deep neural network, cancer drug response profile scan (CDRScan) was created, using cell line genomics profiles and drug molecular properties for input [17] .  ... 
doi:10.3390/ijms20246276 pmid:31842404 fatcat:tnmasrd63bbahabuem73w7k6ya

Machine Learning Applications for Therapeutic Tasks with Genomics Data [article]

Kexin Huang, Cao Xiao, Lucas M. Glass, Cathy W. Critchlow, Greg Gibson, Jimeng Sun
2021 arXiv   pre-print
In this survey, we review the literature on machine learning applications for genomics through the lens of therapeutic development.  ...  Thanks to the increasing availability of genomics and other biomedical data, many machine learning approaches have been proposed for a wide range of therapeutic discovery and development tasks.  ...  . & Ideker, T. (2020), ‘Predicting drug response and synergy using a deep learning model of human cancer cells’, Cancer Cell 38(5), 672–684. Labuhn, M., Adams, F.  ... 
arXiv:2105.01171v1 fatcat:d2nbrjt4tvak7momoxxjlmqk2m

Large-scale pharmacogenomic studies and drug response prediction for personalized cancer medicine

Fangyoumin Feng, Bihan Shen, Xiaoqin Mou, Yixue Li, Hong Li
2021 Journal of Genetics and Genomics  
Furthermore, we comprehensively review the recent developments of computational methods in drug response prediction, covering network, machine learning, and deep learning technologies and strategies to  ...  The response rate of most anti-cancer drugs is limited because of the high heterogeneity of cancer and the complex mechanism of drug action.  ...  The commonly used network structure for drug response prediction is the dual-layer integrated cell line-drug network connecting a drug similarity network and a cell line similarity network by known drug-cell  ... 
doi:10.1016/j.jgg.2021.03.007 pmid:34023295 fatcat:yzesz6gmfngjtcamuaznrgpv6y

Leveraging Deep Learning Techniques and Integrated Omics Data for Tailored Treatment of Breast Cancer

Deeba Khan, Seema Shedole
2022 Journal of Personalized Medicine  
Second, we propose DCNN-DR, a deep convolute.ion neural network-drug response method for predicting the effectiveness of drugs on in vitro and in vivo breast cancer datasets.  ...  Multiomics data of cancer patients and cell lines, in synergy with deep learning techniques, have aided in unravelling predictive problems related to cancer research and treatment.  ...  The proposed model integrates omics data and leverages convolution neural networks to predict the response of up to 108 drugs on cancer cell lines using a threshold.  ... 
doi:10.3390/jpm12050674 fatcat:i37ylubphndvznu6rkrjwqhyf4

Deep learning in cancer diagnosis, prognosis and treatment selection

Khoa A. Tran, Olga Kondrashova, Andrew Bradley, Elizabeth D. Williams, John V. Pearson, Nicola Waddell
2021 Genome Medicine  
AbstractDeep learning is a subdiscipline of artificial intelligence that uses a machine learning technique called artificial neural networks to extract patterns and make predictions from large data sets  ...  We also assess the current limitations and challenges for the application of deep learning in precision oncology, including the lack of phenotypically rich data and the need for more explainable deep learning  ...  Acknowledgements Khoa Tran was the recipient of the Maureen and Barry Stevenson PhD Scholarship, we are grateful to Maureen Stevenson for her support.  ... 
doi:10.1186/s13073-021-00968-x pmid:34579788 pmcid:PMC8477474 fatcat:y73fumwdazft3pw47gqdcncnue


2017 Biocomputing 2018  
As the impact of genetics, genomics, and bioinformatics on drug discovery has been increasingly recognized, this session of the 2018 Pacific Symposium on Biocomputing (PSB) aims to facilitate scientific  ...  The selected papers focus on developing and applying computational approaches to understand drug mechanisms of action and develop drug combination strategies, to enable in silico drug screening, and to  ...  We also thank the following reviewers for providing expert reviews of the submitted manuscripts: Vinayagam  ... 
doi:10.1142/9789813235533_0001 fatcat:cg5lcjpmejbpfpslhjzgyfhrke

Evaluation of Machine Learning Classifiers to Predict Compound Mechanism of Action When Transferred across Distinct Cell Lines

Scott J. Warchal, John C. Dawson, Neil O. Carragher
2019 SALAS Discovery  
The aim of the current study was to evaluate and compare the performance of a classic ensemble-based tree classifier trained on extracted morphological features and a deep learning classifier using convolutional  ...  However, our CNN analysis performs worse than an ensemble-based tree classifier when trained on multiple cell lines at predicting compound mechanism of action on an unseen cell line.  ...  Funding The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by a core Cancer Research UK Edinburgh  ... 
doi:10.1177/2472555218820805 pmid:30694704 pmcid:PMC6484528 fatcat:ubcvbydlhbbjxbatuxlv3r5q5i

Artificial Intelligence to Decode Cancer Mechanism: Beyond Patient Stratification for Precision Oncology

Sandip Kumar Patel, Bhawana George, Vineeta Rai
2020 Frontiers in Pharmacology  
of diagnostic and prognostic markers, and (d) monitor patient's response to drugs/treatments and recovery.  ...  Cancer is one of the most complex diseases affecting a range of organs with diverse disease progression mechanisms and the effectors ranging from gene-epigenetics to a wide array of metabolites.  ...  Some of these widely used data sets are: (i) The genomics of Drug Sensitivity in Cancer (GDSC), (ii) Cancer Cell Line Encyclopedia (CCLE), and (iii) National Cancer Institute drug screening panel .  ... 
doi:10.3389/fphar.2020.01177 pmid:32903628 pmcid:PMC7438594 fatcat:u7mdynhnwfazbn6jhvcagorp2a

Pathway-guided deep neural network toward interpretable and predictive modeling of drug sensitivity [article]

Hui Liu, Lei Deng, Yiding Cai, Wenhao Zhang, Wenyi Yang, Bo Gao
2020 bioRxiv   pre-print
In this paper, we presented a pathway-guided deep neural network model, referred to as pathDNN, to predict the drug sensitivity to cancer cells.  ...  of disease-related pathways induced by drug treatment on cancer cells.  ...  Acknowledgments Funding:This work was funded by National Natural Science Foundation of China [grants No. 61672113, 61672541]. Conflict of interest: none declared.  ... 
doi:10.1101/2020.02.06.930503 fatcat:36m7kel5tzeavpysgkuanpzzwq

A deep learning framework for high-throughput mechanism-driven phenotype compound screening [article]

Thai-Hoang Pham, Yue T Qiu, Jucheng Zeng, Lei Xie, Ping Zhang
2020 biorxiv/medrxiv  
However, the readout from the chemical modulation of a single protein is poorly correlated with phenotypic response of organism, leading to high failure rate in drug development.  ...  In this study, we propose a mechanism-driven neural network-based method named DeepCE (Deep Chemical Expression) which utilizes graph convolutional neural network to learn chemical representation and multi-head  ...  Systematic analysis of genome-wide gene expression of chemical perturbations on human cell lines has led to significant improvements in drug discovery and systems pharmacology.  ... 
doi:10.1101/2020.07.19.211235 pmid:32743586 pmcid:PMC7386506 fatcat:cwfqrhiq6rc3lfkkpghicylfu4
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