Inferring Drug-Target Interactions Based on Random Walk and Convolutional Neural Network

Xu, Xiaoqiang; Xuan, Ping; Zhang, Tiangang; Chen, Bingxu; Sheng, Nan

doi:10.1109/tcbb.2021.3066813

Cited by 4 publications

(2 citation statements)

References 44 publications

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“…(ii) Network-Based Inference (NBI): The method of network-based inference improves the accuracy of drug target prediction by combining and analyzing multiple bioinformatics networks and highthroughput data (Peng et al, 2021). (iii) Random Walks: identifying the most relevant nodes to a known drug target by modelling random wandering on the network Xu et al, 2022).…”

Section: B Network-based and Machine Learning-based Methods For Predi...mentioning

confidence: 99%

The Art of Finding the Right Drug Target: Emerging Methods and Strategies

Jia,

Yang,

et al. 2024

Pharmacol Rev

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Section: B Network-based and Machine Learning-based Methods For Predi...mentioning

confidence: 99%

The Art of Finding the Right Drug Target: Emerging Methods and Strategies

Jia,

Yang,

et al. 2024

Pharmacol Rev

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“…A detailed review on drug recommendation is presented in [20], which used several articles. A relational connection based heterogeneous model is presented in [21], which uses drug, proteins and drugs with side effects. The DTIPred is discussed according to random walk and convolutional neural network.…”

Section: Related Workmentioning

confidence: 99%

Multi Feature DCR based Drug Compound Selection and Recommendation System for Efficient Decision-Making using Genetic Algorithm

Aarthy¹,

Iqbal²

2023

IJACSA

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The performance of treating the cardiac diseases is dependent on the kind of drug being selected. There exist numerous decisive support systems which work according to certain characteristics and factors like drug availability, and popularity. Still, they struggle to achieve expected performance in supporting the medical practitioner. To handle this issue, a multi feature drug curing rate based drug compound selection and recommendation system (MDCRSR) is presented. The method utilizes medical histories and data set of various medical organization around the disease considered. Using the traces, the method identifies the drug compounds and features to perform preprocessing which eliminates the noisy data points. Further, the features of the traces are extracted to perform training with genetic algorithm. At the test phase, the method estimates the fitness measure for different drug combination and compounds by measuring their Drug Curing Rate (DCR). The method performs cross over and mutation to produce various populations of drug compounds. According to the curing rate, the drug compound pattern or population is selected and ranked. The ranked results are populated to the medical practitioner. The method improves the performance of recommendation system as well as drug compound selection.

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