Inductive matrix completion for predicting adverse drug reactions (ADRs) integrating drug–target interactions

Li, Rong; Dong, Yongcheng; Kuang, Qifan; Wu, Yiming; Li, Yizhou; Zhu, Min; Li, Menglong

doi:10.1016/j.chemolab.2015.03.013

Cited by 19 publications

(36 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A similar approach was presented by Zitnic et al 14 (also described earlier) for disease classification, where e.g., five different gene-gene similarity matrices were all factorized using a common projection matrix. In addition to disease classification and drug repurposing, matrix factorization has also been used for DTI prediction 163 and drug-ADR associations 164 .…”

Section: Data Integration In Computational Pharmacologymentioning

confidence: 99%

“…Recent applications of matrix factorization-based methods 15,28,[158][159][160] demonstrate some important advantages of this type of approach, in particular, ease of multi-scale integration as well as data imputation within a mathematically rigorous formulation. Matrix factorization approximates a (usually large) matrix as a product of lower-rank matrices.…”

Section: Matrix Factorizationmentioning

confidence: 99%

“…In addition to disease classification and drug repurposing, matrix factorization has also been used for DTI prediction 159 and drug-ADR associations. 160 Another advantage of matrix factorization is its utility for data imputation via 'matrix completion,' where missing entries in a matrix are filled in based on the observed entries. Many techniques [161][162][163] to solve this problem were developed as a result of the Netflix competition, 164 which posed movie recommendation as a matrix completion problem.…”

Section: Matrix Factorizationmentioning

confidence: 99%

See 2 more Smart Citations

In silico methods for drug repurposing and pharmacology

Hodos

Kidd²,

Shameer³

et al. 2016

WIREs Mechanisms of Disease

281

195

View full text Add to dashboard Cite

Data in the biological, chemical, and clinical domains are accumulating at ever-increasing rates and have the potential to accelerate and inform drug development in new ways. Challenges and opportunities now lie in developing analytic tools to transform these often complex and heterogeneous data into testable hypotheses and actionable insights. This is the aim of computational pharmacology, which uses in silico techniques to better understand and predict how drugs affect biological systems, which can in turn improve clinical use, avoid unwanted side effects, and guide selection and development of better treatments. One exciting application of computational pharmacology is drug repurposing- finding new uses for existing drugs. Already yielding many promising candidates, this strategy has the potential to improve the efficiency of the drug development process and reach patient populations with previously unmet needs such as those with rare diseases. While current techniques in computational pharmacology and drug repurposing often focus on just a single data modality such as gene expression or drug-target interactions, we rationalize that methods such as matrix factorization that can integrate data within and across diverse data types have the potential to improve predictive performance and provide a fuller picture of a drug's pharmacological action.

show abstract

Section: Data Integration In Computational Pharmacologymentioning

confidence: 99%

Section: Matrix Factorizationmentioning

confidence: 99%

Section: Matrix Factorizationmentioning

confidence: 99%

See 1 more Smart Citation

In silico methods for drug repurposing and pharmacology

Hodos

Kidd²,

Shameer³

et al. 2016

WIREs Mechanisms of Disease

281

195

View full text Add to dashboard Cite

show abstract

“…We prove that our multiplicative learning algorithm, which does not require to set a learning rate nor applying projection functions to guaranteed non-negative constraints, convergences to a globally optimal solution point with a first-order convergence rate. And unlike non-convex matrix decomposition models proposed previously for the side effect prediction problem [10,11,12], these theoretical guarantees of convergence imply the reproducibility of the solutions under arbitrary initializations: a desirable property for biological interpretation.…”

Section: Contributionsmentioning

confidence: 99%

“…A similar approach was used by Zhang et al [11], that also included smoothness constraints derived from drug side information. Li et al [12] proposed an inductive matrix completion approach that integrates side information using kernel matrices of drugs and side effects.…”

Section: Introductionmentioning

confidence: 99%

The Geometric Sparse Matrix Completion Model for Predicting Drug Side effects

Galeano

Paccanaro

2019

Preprint

View full text Add to dashboard Cite

Pair-input associations for drug-side effects are obtained through expensive placebocontrolled experiments in human clinical trials. An important challenge in computational pharmacology is to predict missing associations given a few entries in the drug-side effect matrix, as these predictions can be used to direct further clinical trials. Here we introduce the Geometric Sparse Matrix Completion (GSMC) model for predicting drug side effects. Our high-rank matrix completion model learns non-negative sparse matrices of coefficients for drugs and side effects by imposing smoothness priors that exploit a set of pharmacological side information graphs, including information about drug chemical structures, drug interactions, molecular targets, and disease indications. Our learning algorithm is based on the diagonally rescaled gradient descend principle of non-negative matrix factorization. We prove that it converges to a globally optimal solution with a first-order rate of convergence. Experiments on large-scale side effect data from human clinical trials show that our method achieves better prediction performance than six state-of-the-art methods for side effect prediction while offering biological interpretability and favouring explainable predictions.

show abstract

Machine learning prediction of side effects for drugs in clinical trials

Galeano

Paccanaro

2022

Cell Reports Methods

View full text Add to dashboard Cite

Inductive matrix completion for predicting adverse drug reactions (ADRs) integrating drug–target interactions

Cited by 19 publications

References 37 publications

In silico methods for drug repurposing and pharmacology

In silico methods for drug repurposing and pharmacology

The Geometric Sparse Matrix Completion Model for Predicting Drug Side effects

Machine learning prediction of side effects for drugs in clinical trials

Contact Info

Product

Resources

About