RETRACTED ARTICLE: Predicting autism spectrum disorder from associative genetic markers of phenotypic groups using machine learning

Sekaran, K. Chandra; Sudha, M.

doi:10.1007/s12652-020-02155-z

Cited by 19 publications

(8 citation statements)

References 44 publications

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“…The supervised ML classification algorithms effectively mine the analytical insights from large amounts of information. It handles vast biological databases for multi‐faceted applications such as bioinformatics, 55‐57 medical imaging, 58 drug discovery, 59,60 and so on, In this study, the HSIC‐Lasso identified candidate biomarkers are trained with LR, decision tree, support vector machines with linear kernel, k‐nearest neighbor, and RF models. The performance of the algorithms is evaluated using accuracy, precision, and recall scores and provided separately for binary (case/control) and multiclass severity classification (case/mild/moderate/severe).…”

Section: Discussionmentioning

confidence: 99%

Molecular modeling of C1‐inhibitor as SARS‐CoV‐2 target identified from the immune signatures of multiple tissues: An integrated bioinformatics study

Sekaran

Varghese

Ramanathan

et al. 2022

Cell Biochemistry & Function

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The expeditious transmission of the severe acute respiratory coronavirus 2 (SARS-CoV-2), a strain of COVID-19, crumbled the global economic strength and caused a veritable collapse in health infrastructure. The molecular modeling of the novel coronavirus research sounds promising and equips more evidence about the pragmatic therapeutic options. This article proposes a machine-learning framework for identifying potential COVID-19 transcriptomic signatures. The transcriptomics data contains immune-related genes collected from multiple tissues (blood, nasal, and buccal) with accession number: GSE183071. Extensive bioinformatics work was carried out to identify the potential candidate markers, including differential expression analysis, protein interactions, gene ontology, and KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway enrichment studies. The overlapping investigation found SERPING1, the gene that encodes a glycosylated plasma protein C1-INH, in all three datasets. Furthermore, the immuno-informatics study was conducted on the C1-INH protein. 5DU3, the protein identifier of C1-INH, was fetched to identify the antigenicity, major histocompatibility (MHC) Class I and II binding epitopes, allergenicity, toxicity, and immunogenicity. The screening of peptides satisfying the vaccine-design criteria based on the metrics mentioned above is performed. The drug-gene interaction study reported that Rhucin is strongly associated with SERPING1. HSIC-Lasso (Hilbert-Schmidt independence criterion-least absolute shrinkage and selection operator), a model-free biomarker selection technique, was employed to identify the genes having a nonlinear relationship with the target class. The gene subset is trained with supervised machine learning models by a leave-one-out cross-validation method. Explainable artificial intelligence techniques perform the model interpretation analysis.

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Section: Discussionmentioning

confidence: 99%

Molecular modeling of C1‐inhibitor as SARS‐CoV‐2 target identified from the immune signatures of multiple tissues: An integrated bioinformatics study

Sekaran

Varghese

Ramanathan

et al. 2022

Cell Biochemistry & Function

Self Cite

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“…Both the layers have nodes connected to each other ( Mahendran et al, 2020 ; Sureshkumar et al, 2020 ). The major components in RBMs are bias, weight, and activation function ( Le Roux and Bengio, 2008 ; Sekaran and Sudha, 2020 ). The output is produced after processing with the activation function.…”

Section: Methodsmentioning

confidence: 99%

Improving the Classification of Alzheimer’s Disease Using Hybrid Gene Selection Pipeline and Deep Learning

et al. 2021

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Alzheimer’s is a progressive, irreversible, neurodegenerative brain disease. Even with prominent symptoms, it takes years to notice, decode, and reveal Alzheimer’s. However, advancements in technologies, such as imaging techniques, help in early diagnosis. Still, sometimes the results are inaccurate, which delays the treatment. Thus, the research in recent times focused on identifying the molecular biomarkers that differentiate the genotype and phenotype characteristics. However, the gene expression dataset’s generated features are huge, 1,000 or even more than 10,000. To overcome such a curse of dimensionality, feature selection techniques are introduced. We designed a gene selection pipeline combining a filter, wrapper, and unsupervised method to select the relevant genes. We combined the minimum Redundancy and maximum Relevance (mRmR), Wrapper-based Particle Swarm Optimization (WPSO), and Auto encoder to select the relevant features. We used the GSE5281 Alzheimer’s dataset from the Gene Expression Omnibus We implemented an Improved Deep Belief Network (IDBN) with simple stopping criteria after choosing the relevant genes. We used a Bayesian Optimization technique to tune the hyperparameters in the Improved Deep Belief Network. The tabulated results show that the proposed pipeline shows promising results.

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“…A hybrid deep learning model [12] is proposed with information gain ratio for dimension reduction and Deep Belief Network (DBN) based on Gaussian Restricted Boltzmann Machine (GRBM) for classification of samples from disease to its control. K. Sekaran and M. Sudha proposed a gene selection strategy in [13] for recognizing the autistic gene in the genomic microarray data. The optimum features are selected by the signal-to-noise ratio holding a logistic sigmoid function, the Hilbert-Schmidt Independence Criterion Lasso, along with a regularized genetic algorithm employed for gene classification.…”

Section: Related Workmentioning

confidence: 99%

An Intelligent Hybrid Ensemble Gene Selection Model for Autism Using DNN

Anurekha¹,

Geetha²

2023

Intelligent Automation &Amp; Soft Computing

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Autism Spectrum Disorder (ASD) is a complicated neurodevelopmental disorder that is often identified in toddlers. The microarray data is used as a diagnostic tool to identify the genetics of the disorder. However, microarray data is large and has a high volume. Consequently, it suffers from the problem of dimensionality. In microarray data, the sample size and variance of the gene expression will lead to overfitting and misclassification. Identifying the autism gene (feature) subset from microarray data is an important and challenging research area. It has to be efficiently addressed to improve gene feature selection and classification. To overcome the challenges, a novel Intelligent Hybrid Ensemble Gene Selection (IHEGS) model is proposed in this paper. The proposed model integrates the intelligence of different feature selection techniques over the data partitions. In this model, the initial gene selection is carried out by data perturbation, and the final autism gene subset is obtained by functional perturbation, which reduces the problem of dimensionality in microarray data. The functional perturbation module employs three meta-heuristic swarm intelligence-based techniques for gene selection. The obtained gene subset is validated by the Deep Neural Network (DNN) model. The proposed model is implemented using python with six National Center for Biotechnology Information (NCBI) gene expression datasets. From the comparative study with other existing state-of-the-art systems, the proposed model provides stable results in terms of feature selection and classification accuracy.

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RETRACTED ARTICLE: Predicting autism spectrum disorder from associative genetic markers of phenotypic groups using machine learning

Cited by 19 publications

References 44 publications

Molecular modeling of C1‐inhibitor as SARS‐CoV‐2 target identified from the immune signatures of multiple tissues: An integrated bioinformatics study

Molecular modeling of C1‐inhibitor as SARS‐CoV‐2 target identified from the immune signatures of multiple tissues: An integrated bioinformatics study

Improving the Classification of Alzheimer’s Disease Using Hybrid Gene Selection Pipeline and Deep Learning

An Intelligent Hybrid Ensemble Gene Selection Model for Autism Using DNN

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