Collaborative matrix factorization with multiple similarities for predicting drug-target interactions

Zheng, Xiaokun; Ding, Hao; Mamitsuka, Hiroshi; Zhu, Shanfeng

doi:10.1145/2487575.2487670

Cited by 287 publications

(219 citation statements)

References 25 publications

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“…Similarly as in previous studies (e.g., [30]), the performance of BRDTI method was evaluated via five times repeated 10-fold cross-validation (5x10-fold CV). In each of the five repetitions, we randomly assign known DTIs to one out of ten splits.…”

Section: Evaluation and Resultsmentioning

confidence: 99%

“…The proposed BRDTI method was compared with four state of the art approaches: BLM-NII [25], WNN-GIP [28], NetLapRLS [26] and CMF [30]. Grid-search was used to tune methods' hyperparameters, details can be found in supplementary materials.…”

Section: Evaluation and Resultsmentioning

confidence: 99%

“…Machine learning methods in general leverage features based on the structure of drugs and targets (e.g., [9,[19][20][21]), drugs' side-effects [22], and the knowledge of already confirmed DTIs [23][24][25][26][27][28][29][30][31][32]. In particular, in case of Bipartite Local Models (BLM) [23] and its extensions (e.g.…”

Section: Introductionmentioning

confidence: 99%

“…KBMF utilizes drugs' and targets' similarity kernels , , decomposes original DTI matrix as ≈ and thus considers drugs' and targets' similarity in DTI prediction. Zheng et al [30] proposed the multiple similarities one-class matrix factorization (MSCMF) model with additive regularization based on multiple drug and target similarity matrices. Liu et al [31] proposed neighborhood regularized logistic matrix factorization (NRLMF), with similar regularization terms as in MSCMF, however the similarity matrices were reduced via the nearest neighbor approach.…”

Section: Introductionmentioning

confidence: 99%

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Drug-target interaction prediction: A Bayesian ranking approach

Peška

Búza

Koller

2017

Computer Methods and Programs in Biomedicine

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Background and Objective: In silico prediction of drug-target interactions (DTI) could provide valuable information and speed-up the process of drug repositioning -finding novel usage for existing drugs. In our work, we focus on machine learning algorithms supporting drug-centric repositioning approach, which aims to find novel usage for existing or abandoned drugs. We aim at proposing a per-drug ranking-based method, which reflects the needs of drug-centric repositioning research better than conventional drug-target prediction approaches. Methods: We propose Bayesian Ranking Prediction of Drug-Target Interactions(BRDTI). The method is based on Bayesian Personalized Ranking matrix factorization (BPR) which has been shown to be an excellent approach for various preference learning tasks, however, it has not been used for DTI prediction previously. In order to successfully deal with DTI challenges, we extended BPR by proposing: (i) the incorporation of target bias, (ii) a technique to handle new drugs and (iii) content alignment to take structural similarities of drugs and targets into account. Conclusions:Based on the evaluation, we can conclude that BRDTI is an appropriate choice for researchers looking for an in silico DTI prediction technique to be used in drugcentric repositioning scenarios. BRDTI Software and supplementary materials are available online at www.ksi.mff.cuni.cz/~peska/BRDTI.

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Section: Evaluation and Resultsmentioning

confidence: 99%

Section: Evaluation and Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Drug-target interaction prediction: A Bayesian ranking approach

Peška

Búza

Koller

2017

Computer Methods and Programs in Biomedicine

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“…Matrix factorization semi-supervised MSCMF [140] Drug-target interaction prediction by integrating known drug-target interactions along with multiple drug and target similarities. Matrix Factorization semi-supervised DDR [141] Drug-disease association prediction by integrating known drugdisease association along with multiple drug and target similarities.…”

Section: Databasementioning

confidence: 99%

Integrative methods for analyzing big data in precision medicine

2016

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We provide an overview of recent developments in big data analyses in the context of precision medicine and health informatics. With the advance in technologies capturing molecular and medical data, we entered the area of Big Data in biology and medicine. These data offer many opportunities to advance precision medicine.We outline key challenges in precision medicine and present recent advances in data integrationbased methods to uncover personalized information from big data produced by various omics studies. We survey recent integrative methods for disease subtyping, bio-markers discovery and drug repurposing, and list the tools that are available to domain scientists. Given the ever-growing nature of these big data, we highlight key issues that big data integration methods will face.

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Quasi‐supervised Strategies for Compound‐protein Interaction Prediction

Çakı

Karaçali

2021

Molecular Informatics

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Bilge Karaçalı, not only for his excellent guidance but also for his continued trust and patience through my master's study.I also appreciate Assoc. Prof. Serhat Tozburun and Dr. Ibrahim Akkaya, who are my former lab lead and member, for their support and encouragement through this journey.I would like to thank my thesis committee members, Assist. Prof. Dr. Ezgi Karaca and Assist. Prof. Dr. Mehmet Serkan Apaydın, for their valuable contributions to my thesis.Finally, I would like to express my warmest thanks to my dear mother, Kadriye Çakı, my dear father, Kenan Çakı, and to my aunts and uncles for their endless support throughout my whole life.

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Collaborative matrix factorization with multiple similarities for predicting drug-target interactions

Cited by 287 publications

References 25 publications

Drug-target interaction prediction: A Bayesian ranking approach

Drug-target interaction prediction: A Bayesian ranking approach

Integrative methods for analyzing big data in precision medicine

Quasi‐supervised Strategies for Compound‐protein Interaction Prediction

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