Feature Selection for Alzheimer’s Gene Expression Data Using Modified Binary Particle Swarm Optimization

Ramaswamy, R.; Premalatha, K.; Palaniswamy, Swathypriyadharsini

doi:10.1080/03772063.2021.1962747

Cited by 17 publications

(5 citation statements)

References 30 publications

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“…Therefore, feature selection algorithms were used to screen the lncRNAs whose expression levels were associated with melanoma metastasis [ 65 , 66 ]. Three feature selection algorithms were evaluated using lncRNA features, including SVM–RFE [ 49 ], variance [ 50 ] and t-test [ 51 ]. We used the above feature selection algorithms to select 200 lncRNAs and compared their classification performances with each other.…”

Section: Resultsmentioning

confidence: 99%

“…Three feature selection algorithms were evaluated in this study, including SVM–RFE [ 49 ], variance [ 50 ] and t-test [ 51 ]. SVM–RFE trained a support vector machine (SVM) model [ 52 , 53 ] and selected features using their weights in the trained SVM model to alleviate the possibility of the “large p small n” paradigm [ 37 ].…”

Section: Methodsmentioning

confidence: 99%

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Integration of lncRNAs, Protein-Coding Genes and Pathology Images for Detecting Metastatic Melanoma

Liu

Fan

et al. 2022

Genes

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Melanoma is a lethal skin disease that develops from moles. This study aimed to integrate multimodal data to predict metastatic melanoma, which is highly aggressive and difficult to treat. The proposed EnsembleSKCM method evaluated the prediction performances of long noncoding RNAs (lncRNAs), protein-coding messenger genes (mRNAs) and pathology images (images) for metastatic melanoma. Feature selection was used to screen for metastatic biomarkers in the lncRNA and mRNA datasets. The integrated EnsembleSKCM model was built based on the weighted results of the lncRNA-, mRNA- and image-based models. EnsembleSKCM achieved 0.9444 in the prediction accuracy of metastatic melanoma and outperformed the single-modal prediction models based on the lncRNA, mRNA and image data. The experimental data suggest the importance of integrating the complementary information from the three data modalities. WGCNA was used to analyze the relationship of molecular-level features and image features, and the results show connections between them. Another cohort was used to validate our prediction.

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Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Integration of lncRNAs, Protein-Coding Genes and Pathology Images for Detecting Metastatic Melanoma

Liu

Fan

et al. 2022

Genes

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“…The integration of feature selection methods such as Genetic Algorithm (GA) [43,44], Particle Swarm Optimization (PSO) [45], and Recursive Feature Elimination (RFE) [46] further enhances the effectiveness of QSAR models, allowing the identification of the most relevant molecular descriptors, allowing for a more focused and precise modeling process. This ensures that the QSAR model is built on the most informative features, ultimately improving its predictive accuracy and interpretability.…”

Section: Molecular Descriptorsmentioning

confidence: 99%

Ensemble Machine Learning Approach for Quantitative Structure Activity Relationship Based Drug Discovery: A Review

Noviandy,

Maulana,

Idroes

et al. 2023

Infolitika J. Data Sci.

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This comprehensive review explores the pivotal role of ensemble machine learning techniques in Quantitative Structure-Activity Relationship (QSAR) modeling for drug discovery. It emphasizes the significance of accurate QSAR models in streamlining candidate compound selection and highlights how ensemble methods, including AdaBoost, Gradient Boosting, Random Forest, Extra Trees, XGBoost, LightGBM, and CatBoost, effectively address challenges such as overfitting and noisy data. The review presents recent applications of ensemble learning in both classification and regression tasks within QSAR, showcasing the exceptional predictive accuracy of these techniques across diverse datasets and target properties. It also discusses the key challenges and considerations in ensemble QSAR modeling, including data quality, model selection, computational resources, and overfitting. The review outlines future directions in ensemble QSAR modeling, including the integration of multi-modal data, explainability, handling imbalanced data, automation, and personalized medicine applications while emphasizing the need for ethical and regulatory guidelines in this evolving field.

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“…In this study, we use GA to select the most optimal molecular descriptor due to its ability to efficiently explore the extensive space of possible descriptors and identify concise and relevant representations of chemical features [26,27]. GA works by iteratively generating heuristic solutions that represent different subsets of molecular descriptors.…”

Section: Feature Selectionmentioning

confidence: 99%

Integrating Genetic Algorithm and LightGBM for QSAR Modeling of Acetylcholinesterase Inhibitors in Alzheimer's Disease Drug Discovery

Noviandy,

Maulana,

Idroes

et al. 2023

Malacca Pharm.

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This study explores the use of Quantitative Structure-Activity Relationship (QSAR) studies using genetic algorithm (GA) and LightGBM to search for acetylcholinesterase (AChE) inhibitors for Alzheimer's disease. The study uses a dataset of 6,157 AChE inhibitors and their IC50 values. A LightGBM model is trained and evaluated for classification performance. The results show that the LightGBM model achieved high performance on the training and testing set, with an accuracy of 92.49% and 82.47%, respectively. This study demonstrates the potential of GA and LightGBM in the drug discovery process for AChE inhibitors in Alzheimer's disease. The findings contribute to the drug discovery process by providing insights about AChE inhibitors that allow more efficient screening of potential compounds and accelerate the identification of promising candidates for development and therapeutic use.

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Feature Selection for Alzheimer’s Gene Expression Data Using Modified Binary Particle Swarm Optimization

Cited by 17 publications

References 30 publications

Integration of lncRNAs, Protein-Coding Genes and Pathology Images for Detecting Metastatic Melanoma

Integration of lncRNAs, Protein-Coding Genes and Pathology Images for Detecting Metastatic Melanoma

Ensemble Machine Learning Approach for Quantitative Structure Activity Relationship Based Drug Discovery: A Review

Integrating Genetic Algorithm and LightGBM for QSAR Modeling of Acetylcholinesterase Inhibitors in Alzheimer's Disease Drug Discovery

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