A computational method for drug sensitivity prediction of cancer cell lines based on various molecular information.

Determining sensitive drugs for a patient is one of the most critical problems in precision medicine. Using genomic profiles of the tumor and drug information can help in tailoring the most efficient treatment for a patient. In this paper, we proposed a classification machine learning approach that predicts the sensitive/resistant drugs for a cell line. It can be performed by using both drug and cell line similarities, one of the cell line or drug similarities, or even not using any similarity information. This paper investigates the influence of using previously defined as well as two newly introduced similarities on predicting anti-cancer drug sensitivity. The proposed method uses max concentration thresholds for assigning drug responses to class labels. Its performance was evaluated using stratified five-fold cross-validation on cell line-drug pairs in two datasets. Assessing the predictive powers of the proposed model and three sets of methods, including state-of-the-art classification methods, state-of-the-art regression methods, and off-the-shelf classification machine learning approaches shows that the proposed method outperforms other methods. Moreover, The efficiency of the model is evaluated in tissue-specific conditions. Besides, the novel sensitive associations predicted by this model were verified by several supportive evidence in the literature and reliable database. Therefore, the proposed model can efficiently be used in predicting anti-cancer drug sensitivity. Material and implementation are available at https://github.com/fahmadimoughari/CDSML.

DisCoVering potential candidates of RNAi-based therapy for COVID-19 using computational methods.

The ongoing pandemic of a novel coronavirus (SARS-CoV-2) leads to international concern; thus, emergency interventions need to be taken. Due to the time-consuming experimental methods for proposing useful treatments, computational approaches facilitate investigating thousands of alternatives simultaneously and narrow down the cases for experimental validation. Herein, we conducted four independent analyses for RNA interference (RNAi)-based therapy with computational and bioinformatic methods. The aim is to target the evolutionarily conserved regions in the SARS-CoV-2 genome in order to down-regulate or silence its RNA. miRNAs are denoted to play an important role in the resistance of some species to viral infections. A comprehensive analysis of the miRNAs available in the body of humans, as well as the miRNAs in bats and many other species, were done to find efficient candidates with low side effects in the human body. Moreover, the evolutionarily conserved regions in the SARS-CoV-2 genome were considered for designing novel significant siRNA that are target-specific. A small set of miRNAs and five siRNAs were suggested as the possible efficient candidates with a high affinity to the SARS-CoV-2 genome and low side effects. The suggested candidates are promising therapeutics for the experimental evaluations and may speed up the procedure of treatment design. Materials and implementations are available at: https://github.com/nrohani/SARS-CoV-2.

ADRML: anticancer drug response prediction using manifold learning.

One of the prominent challenges in precision medicine is to select the most appropriate treatment strategy for each patient based on the personalized information. The availability of massive data about drugs and cell lines facilitates the possibility of proposing efficient computational models for predicting anticancer drug response. In this study, we propose ADRML, a model for Anticancer Drug Response Prediction using Manifold Learning to systematically integrate the cell line information with the drug information to make accurate predictions about drug therapeutic. The proposed model maps the drug response matrix into the lower-rank spaces that lead to obtaining new perspectives about cell lines and drugs. The drug response for a new cell line-drug pair is computed using the low-rank features. The evaluation of ADRML performance on various types of cell lines and drug information, in addition to the comparisons with previously proposed methods, shows that ADRML provides accurate and robust predictions. Further investigations about the association between drug response and pathway activity scores reveal that the predicted drug responses can shed light on the underlying drug mechanism. Also, the case studies suggest that the predictions of ADRML about novel cell line-drug pairs are validated by reliable pieces of evidence from the literature. Consequently, the evaluations verify that ADRML can be used in accurately predicting and imputing the anticancer drug response.

CDAP: An Online Package for Evaluation of Complex Detection Methods.

Methods for detecting protein complexes from protein-protein interaction networks are of the most critical computational approaches. Numerous methods have been proposed in this area. Therefore, it is necessary to evaluate them. Various metrics have been proposed in order to compare these methods. Nevertheless, it is essential to define new metrics that evaluate methods both qualitatively and quantitatively. In addition, there is no tool for the comprehensive comparison of such methods. In this paper, a new criterion is introduced that can fully evaluate protein complex detection algorithms. We introduce CDAP (Complex Detection Analyzer Package); an online package for comparing protein complex detection methods. CDAP can quickly rank the performance of methods based on previously defined as well as newly introduced criteria in various settings (4 PPI datasets and 3 gold standards). It has the capability of integrating various methods and apply several filterings on the results. CDAP can be easily extended to include new datasets, gold standards, and methods. Furthermore, the user can compare the results of a custom method with the results of existing methods. Thus, the authors of future papers can use CDAP for comparing their method with the previous ones. A case study is done on YGR198W, a well-known protein, and the detected clusters are compared to the known complexes of this protein.

A novel algorithm for parameter estimation of Hidden Markov Model inspired by Ant Colony Optimization.

HMM is a powerful method to model data in various fields. Estimation of Hidden Markov Model parameters is an NP-Hard problem. We propose a heuristic algorithm called "AntMarkov" to improve the efficiency of estimating HMM parameters. We compared our method with four algorithms. The comparison was conducted on 5 different simulated datasets with different features. For further evaluation, we analyzed the performance of algorithms on the prediction of protein secondary structures problem. The results demonstrate that our algorithm obtains better results with respect to the results of the other algorithms in terms of time efficiency and the amount of similarity of estimated parameters to the original parameters and log-likelihood. The source code of our algorithm is available in https://github.com/emdadi/HMMPE.