Next Article in Journal
A Modified Cloud Particles Differential Evolution Algorithm for Real-Parameter Optimization
Next Article in Special Issue
Dependent Shrink of Transitions for Calculating Firing Frequencies in Signaling Pathway Petri Net Model
Previous Article in Journal
A Procedure for Identification of Appropriate State Space and ARIMA Models Based on Time-Series Cross-Validation
Previous Article in Special Issue
Community Structure Detection for Directed Networks through Modularity Optimisation
Article Menu

Export Article

Open AccessReview

Algorithms for Drug Sensitivity Prediction

Electrical and Computer Engineering Department, Texas Tech University, Lubbock, TX 79409, USA
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Academic Editors: Tatsuya Akutsu and Takeyuki Tamura
Algorithms 2016, 9(4), 77;
Received: 31 August 2016 / Revised: 14 November 2016 / Accepted: 14 November 2016 / Published: 17 November 2016
(This article belongs to the Special Issue Biological Networks)
PDF [3237 KB, uploaded 17 November 2016]


Precision medicine entails the design of therapies that are matched for each individual patient. Thus, predictive modeling of drug responses for specific patients constitutes a significant challenge for personalized therapy. In this article, we consider a review of approaches that have been proposed to tackle the drug sensitivity prediction problem especially with respect to personalized cancer therapy. We first discuss modeling approaches that are based on genomic characterizations alone and further the discussion by including modeling techniques that integrate both genomic and functional information. A comparative analysis of the prediction performance of four representative algorithms, elastic net, random forest, kernelized Bayesian multi-task learning and deep learning, reflecting the broad classes of regularized linear, ensemble, kernelized and neural network-based models, respectively, has been included in the paper. The review also considers the challenges that need to be addressed for successful implementation of the algorithms in clinical practice. View Full-Text
Keywords: drug sensitivity prediction; personalized medicine; prediction algorithms; tumor response modeling drug sensitivity prediction; personalized medicine; prediction algorithms; tumor response modeling

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

Share & Cite This Article

MDPI and ACS Style

De Niz, C.; Rahman, R.; Zhao, X.; Pal, R. Algorithms for Drug Sensitivity Prediction. Algorithms 2016, 9, 77.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics



[Return to top]
Algorithms EISSN 1999-4893 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top