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Improving prediction of phenotypic drug response on cancer cell lines using deep convolutional network
2019
BMC Bioinformatics
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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?
2021
Briefings in Bioinformatics
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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]
2018
arXiv
pre-print
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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
2020
npj Precision Oncology
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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]
2020
bioRxiv
pre-print
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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
2019
International Journal of Molecular Sciences
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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]
2021
arXiv
pre-print
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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
2021
Journal of Genetics and Genomics
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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
2022
Journal of Personalized Medicine
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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
2021
Genome Medicine
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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
APPLICATIONS OF GENETICS, GENOMICS AND BIOINFORMATICS IN DRUG DISCOVERY
2017
Biocomputing 2018
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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
2019
SALAS Discovery
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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
2020
Frontiers in Pharmacology
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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]
2020
bioRxiv
pre-print
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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]
2020
biorxiv/medrxiv
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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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