scholarly journals A Novel Framework for Drug Synergy Prediction using Differential Evolution based Multinomial Random Forest

2019 ◽  
Vol 10 (5) ◽  
Author(s):  
Jaspreet Kaur ◽  
Dilbag Singh ◽  
Manjit Kaur
Keyword(s):  
Random Forest ◽  
Differential Evolution ◽  
Drug Synergy ◽  
Synergy Prediction

scholarly journals Anti-cancer Drug Synergy Prediction in Understudied Tissues using Transfer Learning

2020 ◽  
Author(s):  
Yejin Kim ◽  
Shuyu Zheng ◽  
Jing Tang ◽  
W. Jim Zheng ◽  
Zhao Li ◽  
...  
Keyword(s):  
Prediction Model ◽  
Transfer Learning ◽  
Treatment Options ◽  
Cancer Drug ◽  
Drug Synergy ◽  
Learning From Data ◽  
Cancer Types ◽  
Improved Accuracy ◽  
Synergy Prediction

AbstractMotivationExploring an exponentially increasing yet more promising space, high-throughput combinatorial drug screening has advantages in identifying cancer treatment options with higher efficacy without degradation in terms of safety. A key challenge is that accumulated number of observations in in-vitro drug responses varies greatly among different cancer types, where some tissues (such as bone and prostate) are understudied than the others. Thus, we aim to develop a drug synergy prediction model for understudied data-poor tissues as overcoming data scarcity problem.ResultsWe collected a comprehensive set of genetic, molecular, phenotypic features for cancer cell lines from six different databases. We developed a drug synergy prediction model based on deep neural networks to integrate multi-modal input and utilize transfer learning from data-rich tissues to data-poor tissues. We showed improved accuracy in predicting drug synergy in understudied tissues without enough drug combination screening data nor after-treatment transcriptome. Our synergy prediction model can be used to rank synergistic drug combinations in understudied tissues and thus help prioritizing future in-vitro experiments.Availability and ImplementationOur algorithm will be publicly available via https://github.com/yejinjkim/drug-synergy-prediction

scholarly journals Prediction of drug synergy score using ensemble based differential evolution

2019 ◽  
Vol 13 (1) ◽  
pp. 24-29
Author(s):  
Harpreet Singh ◽  
Prashant Singh Rana ◽  
Urvinder Singh
Keyword(s):  
Differential Evolution ◽  
Drug Synergy

scholarly journals Anticancer drug synergy prediction in understudied tissues using transfer learning

2020 ◽  
Author(s):  
Yejin Kim ◽  
Shuyu Zheng ◽  
Jing Tang ◽  
Wenjin Jim Zheng ◽  
Zhao Li ◽  
...  
Keyword(s):  
Prediction Model ◽  
Transfer Learning ◽  
Learning Strategy ◽  
Mean Squared Error ◽  
Treatment Options ◽  
Drug Synergy ◽  
Learning From Data ◽  
Improved Accuracy ◽  
Synergy Prediction

Abstract Objective Drug combination screening has advantages in identifying cancer treatment options with higher efficacy without degradation in terms of safety. A key challenge is that the accumulated number of observations in in-vitro drug responses varies greatly among different cancer types, where some tissues are more understudied than the others. Thus, we aim to develop a drug synergy prediction model for understudied tissues as a way of overcoming data scarcity problems. Materials and Methods We collected a comprehensive set of genetic, molecular, phenotypic features for cancer cell lines. We developed a drug synergy prediction model based on multitask deep neural networks to integrate multimodal input and multiple output. We also utilized transfer learning from data-rich tissues to data-poor tissues. Results We showed improved accuracy in predicting synergy in both data-rich tissues and understudied tissues. In data-rich tissue, the prediction model accuracy was 0.9577 AUROC for binarized classification task and 174.3 mean squared error for regression task. We observed that an adequate transfer learning strategy significantly increases accuracy in the understudied tissues. Conclusions Our synergy prediction model can be used to rank synergistic drug combinations in understudied tissues and thus help to prioritize future in-vitro experiments. Code is available at https://github.com/yejinjkim/synergy-transfer.

Multi-objective differential evolution based random forest for e-health applications

2019 ◽  
Vol 33 (05) ◽  
pp. 1950022 ◽  
Author(s):  
Manjit Kaur ◽  
Hemant Kumar Gianey ◽  
Dilbag Singh ◽  
Munish Sabharwal
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Differential Evolution ◽  
Parameter Tuning ◽  
Machine Learning Techniques ◽  
Medical Applications ◽  
Efficient Manner ◽  
Multi Objective ◽  
Learning Techniques ◽  
Health Applications

Many machine learning techniques have been used in past few decades for various medical applications. However, these techniques suffer from parameter tuning issue. Therefore, an efficient tuning of these parameters has an ability to improve the performance of existing machine learning techniques. Therefore, in this work, a novel multi-objective differential evolution based random forest technique is proposed. The proposed technique is able to tune the parameters of random forest in an efficient manner. Extensive experiments are carried out by considering the proposed and the existing competitive machine learning techniques on various medical applications. It is observed that the proposed technique outperforms existing techniques in terms of accuracy, f-measure, sensitivity and specificity.

Abstract PR04: A network-based approach for drug synergy prediction from gene expression data

2013 ◽  
Author(s):  
Mariano J. Alvarez ◽  
Yao Shen ◽  
Charles Karan ◽  
Mukesh Bansal ◽  
Michela Mattioli ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Expression Data ◽  
Drug Synergy ◽  
Synergy Prediction

scholarly journals Strategies to Enhance Logic Modeling-Based Cell Line-Specific Drug Synergy Prediction

2020 ◽  
Vol 11 ◽  
Author(s):  
Barbara Niederdorfer ◽  
Vasundra Touré ◽  
Miguel Vazquez ◽  
Liv Thommesen ◽  
Martin Kuiper ◽  
...  
Keyword(s):  
Cell Line ◽  
Specific Drug ◽  
Logic Modeling ◽  
Drug Synergy ◽  
Synergy Prediction
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