Unsupervised Learning for Feature Representation Using Spatial Distribution of Amino Acids in Aldehyde Dehydrogenase (ALDH2) Protein Sequences

Khandelwal, Monika; Sheikh, Sabha; Rout, Ranjeet Kumar; Umer, Saiyed; Mallik, Saurav; Zhao, Zhongming

doi:10.3390/math10132228

Cited by 7 publications

(2 citation statements)

References 30 publications

(40 reference statements)

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“…The HE is often used to analyze auto-correlation in time-series analysis. It is calculated using rescaled range analysis (R/S analysis) and has a value of 0–1 ( Hurst, 1951 ; Khandelwal et al, 2022c ). A negative auto-correlation of a time series is indicated by a HE value between 0 and 0.5, while a HE value between 0.5 and 1 indicates a positive auto-correlation.…”

Section: Basic Terminologymentioning

confidence: 99%

Identification of discriminant features from stationary pattern of nucleotide bases and their application to essential gene classification

et al. 2023

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Introduction: Essential genes are essential for the survival of various species. These genes are a family linked to critical cellular activities for species survival. These genes are coded for proteins that regulate central metabolism, gene translation, deoxyribonucleic acid replication, and fundamental cellular structure and facilitate intracellular and extracellular transport. Essential genes preserve crucial genomics information that may hold the key to a detailed knowledge of life and evolution. Essential gene studies have long been regarded as a vital topic in computational biology due to their relevance. An essential gene is composed of adenine, guanine, cytosine, and thymine and its various combinations.Methods: This paper presents a novel method of extracting information on the stationary patterns of nucleotides such as adenine, guanine, cytosine, and thymine in each gene. For this purpose, some co-occurrence matrices are derived that provide the statistical distribution of stationary patterns of nucleotides in the genes, which is helpful in establishing the relationship between the nucleotides. For extracting discriminant features from each co-occurrence matrix, energy, entropy, homogeneity, contrast, and dissimilarity features are computed, which are extracted from all co-occurrence matrices and then concatenated to form a feature vector representing each essential gene. Finally, supervised machine learning algorithms are applied for essential gene classification based on the extracted fixed-dimensional feature vectors.Results: For comparison, some existing state-of-the-art feature representation techniques such as Shannon entropy (SE), Hurst exponent (HE), fractal dimension (FD), and their combinations have been utilized.Discussion: An extensive experiment has been performed for classifying the essential genes of five species that show the robustness and effectiveness of the proposed methodology.

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Section: Basic Terminologymentioning

confidence: 99%

Identification of discriminant features from stationary pattern of nucleotide bases and their application to essential gene classification

et al. 2023

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“…Single-cell analysis (SCA), as opposed to the typical study of millions of cells in bulk, provides clear information on each cell, such as particular biological factors of the cell, which is helpful to understand stem cell activity or tumor progression [1,2]. All medical disciplines such as oncology, microbiology, neurology, reproduction, and immunology greatly benefited from single-cell RNA sequencing (scRNAseq) technologies [3,4]. To detect the genome, transcriptome, other multi-omics of single cells, and single-cell sequencing technologies could be used.…”

Section: Introductionmentioning

confidence: 99%

Identifying Genetic Signatures from Single-Cell RNA Sequencing Data by Matrix Imputation and Reduced Set Gene Clustering

Seth,

Mallik,

Islam

et al. 2023

Mathematics

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In this current era, the identification of both known and novel cell types, the representation of cells, predicting cell fates, classifying various tumor types, and studying heterogeneity in various cells are the key areas of interest in the analysis of single-cell RNA sequencing (scRNA-seq) data. Due to the nature of the data, cluster identification in single-cell sequencing data with high dimensions presents several difficulties. In this paper, we introduce a new framework that combines various strategies such as imputed matrix, minimum redundancy maximum relevance (MRMR) feature selection, and shrinkage clustering to discover gene signatures from scRNA-seq data. Firstly, we conducted the pre-filtering of the “drop-out” value in the data focusing solely on imputing the identified “drop-out” values. Next, we applied the MRMR feature selection method to the imputed data and obtained the top 100 features based on the MRMR feature selection optimization scores for further downstream analysis. Thereafter, we employed shrinkage clustering on the selected feature matrix to identify the cell clusters using a global optimization approach. Finally, we applied the Limma-Voom R tool employing voom normalization and an empirical Bayes test to detect differentially expressed features with a false discovery rate (FDR) < 0.001. In addition, we performed the KEGG pathway and gene ontology enrichment analysis of the identified biomarkers using David 6.8 software. Furthermore, we conducted miRNA target detection for the top gene markers and performed miRNA target gene interaction network analysis using the Cytoscape online tool. Subsequently, we compared our detected 100 markers with our previously detected top 100 cluster-specified markers ranked by FDR of the latest published article and discovered three common markers; namely, Cyp2b10, Mt1, Alpi, along with 97 novel markers. In addition, the Gene Set Enrichment Analysis (GSEA) of both marker sets also yields similar outcomes. Apart from this, we performed another comparative study with another published method, demonstrating that our model detects more significant markers than that model. To assess the efficiency of our framework, we apply it to another dataset and identify 20 strongly significant up-regulated markers. Additionally, we perform a comparative study of different imputation methods and include an ablation study to prove that every key phase of our framework is essential and strongly recommended. In summary, our proposed integrated framework efficiently discovers differentially expressed stronger gene signatures as well as up-regulated markers in single-cell RNA sequencing data.

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Prediction of Protein-Protein Interaction Using Support Vector Machine Based on Spatial Distribution of Amino Acids

Khandelwal,

Kumar Rout,

Umer

2023

Lecture Notes in Electrical Engineering

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Unsupervised Learning for Feature Representation Using Spatial Distribution of Amino Acids in Aldehyde Dehydrogenase (ALDH2) Protein Sequences

Cited by 7 publications

References 30 publications

Identification of discriminant features from stationary pattern of nucleotide bases and their application to essential gene classification

Identification of discriminant features from stationary pattern of nucleotide bases and their application to essential gene classification

Identifying Genetic Signatures from Single-Cell RNA Sequencing Data by Matrix Imputation and Reduced Set Gene Clustering

Prediction of Protein-Protein Interaction Using Support Vector Machine Based on Spatial Distribution of Amino Acids

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