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A novel constraint-based structure learning algorithm using marginal causal prior knowledge.

ABSTRACT: Causal discovery with prior knowledge is important for improving performance. We consider the incorporation of marginal causal relations, which correspond to the presence or absence of directed paths in a causal model. We propose the Marginal Prior Causal Knowledge PC (MPPC) algorithm to incorporate marginal causal relations into a constraint-based structure learning algorithm. We provide the theorems of conditional independence properties by combining observational data and marginal causal relations. We compare the MPPC algorithm with other structure learning methods in both simulation studies and real-world networks. The results indicate that, compare with other constraint-based structure learning methods, MPPC algorithm can incorporate marginal causal relations and is more effective and more efficient.

SUBMITTER: Yu Y

PROVIDER: S-EPMC11335901 | biostudies-literature | 2024 Aug

REPOSITORIES: biostudies-literature

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A novel constraint-based structure learning algorithm using marginal causal prior knowledge.

Yu Yifan Y Hou Lei L Liu Xinhui X Wu Sijia S Li Hongkai H Xue Fuzhong F

Scientific reports 20240820 1

Causal discovery with prior knowledge is important for improving performance. We consider the incorporation of marginal causal relations, which correspond to the presence or absence of directed paths in a causal model. We propose the Marginal Prior Causal Knowledge PC (MPPC) algorithm to incorporate marginal causal relations into a constraint-based structure learning algorithm. We provide the theorems of conditional independence properties by combining observational data and marginal causal rela ...[more]

PMID: 39164273

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Project description:BackgroundIdentifying drug-target interactions (DTIs) plays a key role in drug development. Traditional wet experiments to identify DTIs are expensive and time consuming. Effective computational methods to predict DTIs are useful to narrow the searching scope of potential drugs and speed up the process of drug discovery. There are a variety of non-negativity matrix factorization based methods to predict DTIs, but the convergence of the algorithms used in the matrix factorization are often overlooked and the results can be further improved.ResultsIn order to predict DTIs more accurately and quickly, we propose an alternating direction algorithm to solve graph regularized non-negative matrix factorization with prior knowledge consistency constraint (ADA-GRMFC). Based on known DTIs, drug chemical structures and target sequences, ADA-GRMFC at first constructs a DTI matrix, a drug similarity matrix and a target similarity matrix. Then DTI prediction is modeled as the non-negative factorization of the DTI matrix with graph dual regularization terms and a prior knowledge consistency constraint. The graph dual regularization terms are used to integrate the information from the drug similarity matrix and the target similarity matrix, and the prior knowledge consistency constraint is used to ensure the matrix decomposition result should be consistent with the prior knowledge of known DTIs. Finally, an alternating direction algorithm is used to solve the matrix factorization. Furthermore, we prove that the algorithm can converge to a stationary point. Extensive experimental results of 10-fold cross-validation show that ADA-GRMFC has better performance than other state-of-the-art methods. In the case study, ADA-GRMFC is also used to predict the targets interacting with the drug olanzapine, and all of the 10 highest-scoring targets have been accurately predicted. In predicting drug interactions with target estrogen receptors alpha, 17 of the 20 highest-scoring drugs have been validated.

Dataset Information

A novel constraint-based structure learning algorithm using marginal causal prior knowledge.

Publications

A novel constraint-based structure learning algorithm using marginal causal prior knowledge.

Similar Datasets

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