Using Feature Selection Technique for Drug-Target Interaction Networks Prediction

Yu, Weiming; Jiang, Zhuolin; Wang, J.; Tao, Ran

doi:10.2174/092986711798347270

Cited by 11 publications

(4 citation statements)

References 32 publications

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“…Many published papers have demonstrated that the optimized features could improve predictive accuracy [19–25]. For high-dimension data, some features are noise or redundant information which has negative contribution to the prediction.…”

Section: Resultsmentioning

confidence: 99%

Predicting Presynaptic and Postsynaptic Neurotoxins by Developing Feature Selection Technique

Tang

Yang

Zhang

et al. 2017

BioMed Research International

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Presynaptic and postsynaptic neurotoxins are proteins which act at the presynaptic and postsynaptic membrane. Correctly predicting presynaptic and postsynaptic neurotoxins will provide important clues for drug-target discovery and drug design. In this study, we developed a theoretical method to discriminate presynaptic neurotoxins from postsynaptic neurotoxins. A strict and objective benchmark dataset was constructed to train and test our proposed model. The dipeptide composition was used to formulate neurotoxin samples. The analysis of variance (ANOVA) was proposed to find out the optimal feature set which can produce the maximum accuracy. In the jackknife cross-validation test, the overall accuracy of 94.9% was achieved. We believe that the proposed model will provide important information to study neurotoxins.

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Section: Resultsmentioning

confidence: 99%

Predicting Presynaptic and Postsynaptic Neurotoxins by Developing Feature Selection Technique

Tang

Yang

Zhang

et al. 2017

BioMed Research International

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“…Using the same approach, we identified new genetic determinants that play important functional roles in the thermostable proteins [ 31 ], halostable proteins [ 32 ], and P1B-ATPase heavy metal transporters [ 21 ]. Furthermore, feature selection techniques and other learning methods such as bipartite learning graph and semi-supervised algorithms have already been used in drug-target interactions and the capability of these methods in predicting drug-target datasets has been proven [ 33 ]. In the present study, we used the same strategy to evaluate correlation between HCV gene attributes at nucleotide levels with treatment response.…”

Section: Discussionmentioning

confidence: 99%

Prediction of hepatitis C virus interferon/ribavirin therapy outcome based on viral nucleotide attributes using machine learning algorithms

2014

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BackgroundHepatitis C virus (HCV) causes chronic hepatitis C in 2-3% of world population and remains one of the health threatening human viruses, worldwide. In the absence of an effective vaccine, therapeutic approach is the only option to combat hepatitis C. Interferon-alpha (IFN-alpha) and ribavirin (RBV) combination alone or in combination with recently introduced new direct-acting antivirals (DAA) is used to treat patients infected with HCV. The present study utilized feature selection methods (Gini Index, Chi Squared and machine learning algorithms) and other bioinformatics tools to identify genetic determinants of therapy outcome within the entire HCV nucleotide sequence.ResultsUsing combination of several algorithms, the present study performed a comprehensive bioinformatics analysis and identified several nucleotide attributes within the full-length nucleotide sequences of HCV subtypes 1a and 1b that correlated with treatment outcome. Feature selection algorithms identified several nucleotide features (e.g. count of hydrogen and CG). Combination of algorithms utilized the selected nucleotide attributes and predicted HCV subtypes 1a and 1b therapy responders from non-responders with an accuracy of 75.00% and 85.00%, respectively. In addition, therapy responders and relapsers were categorized with an accuracy of 82.50% and 84.17%, respectively. Based on the identified attributes, decision trees were induced to differentiate different therapy response groups.ConclusionsThe present study identified new genetic markers that potentially impact the outcome of hepatitis C treatment. In addition, the results suggest new viral genomic attributes that might influence the outcome of IFN-mediated immune response to HCV infection.Electronic supplementary materialThe online version of this article (doi:10.1186/1756-0500-7-565) contains supplementary material, which is available to authorized users.

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“…Dimensionality reduction: These techniques like PCA and t-Distributed Stochastic Neighbor Embedding (t-SNE) help visualize and interpret complex pharmacological datasets by transforming high-dimensional data into lower-dimensional representations while preserving essential information. They aid in better understanding and decision-making [64][65][66][67][68][69][70].…”

Section: Feature Selection and Dimensionality Reduction Techniquesmentioning

confidence: 99%

Artificial Intelligence and Machine Learning in Pharmacological Research: Bridging the Gap Between Data and Drug Discovery

Singh,

Kumar,

Payra

et al. 2023

Cureus

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Artificial intelligence (AI) has transformed pharmacological research through machine learning, deep learning, and natural language processing. These advancements have greatly influenced drug discovery, development, and precision medicine. AI algorithms analyze vast biomedical data identifying potential drug targets, predicting efficacy, and optimizing lead compounds. AI has diverse applications in pharmacological research, including target identification, drug repurposing, virtual screening, de novo drug design, toxicity prediction, and personalized medicine. AI improves patient selection, trial design, and real-time data analysis in clinical trials, leading to enhanced safety and efficacy outcomes. Post-marketing surveillance utilizes AI-based systems to monitor adverse events, detect drug interactions, and support pharmacovigilance efforts. Machine learning models extract patterns from complex datasets, enabling accurate predictions and informed decision-making, thus accelerating drug discovery. Deep learning, specifically convolutional neural networks (CNN), excels in image analysis, aiding biomarker identification and optimizing drug formulation. Natural language processing facilitates the mining and analysis of scientific literature, unlocking valuable insights and information. However, the adoption of AI in pharmacological research raises ethical considerations. Ensuring data privacy and security, addressing algorithm bias and transparency, obtaining informed consent, and maintaining human oversight in decision-making are crucial ethical concerns. The responsible deployment of AI necessitates robust frameworks and regulations. The future of AI in pharmacological research is promising, with integration with emerging technologies like genomics, proteomics, and metabolomics offering the potential for personalized medicine and targeted therapies. Collaboration among academia, industry, and regulatory bodies is essential for the ethical implementation of AI in drug discovery and development. Continuous research and development in AI techniques and comprehensive training programs will empower scientists and healthcare professionals to fully exploit AI's potential, leading to improved patient outcomes and innovative pharmacological interventions.

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Using Feature Selection Technique for Drug-Target Interaction Networks Prediction

Cited by 11 publications

References 32 publications

Predicting Presynaptic and Postsynaptic Neurotoxins by Developing Feature Selection Technique

Predicting Presynaptic and Postsynaptic Neurotoxins by Developing Feature Selection Technique

Prediction of hepatitis C virus interferon/ribavirin therapy outcome based on viral nucleotide attributes using machine learning algorithms

Artificial Intelligence and Machine Learning in Pharmacological Research: Bridging the Gap Between Data and Drug Discovery

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