A Multi-classifier Model to Identify Mitochondrial Respiratory Gene Signatures in Human Cancer

Mallik, Saurav; Seth, Soumita; Bhadra, Tapas; Tomar, Namrata; Zhao, Zhongming

doi:10.1109/bibm47256.2019.8982945

Cited by 3 publications

(5 citation statements)

References 57 publications

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“…In this step

normalization [ 24 ] was used and after that we applied

[ 18 , 25 ]. After applying

normalization tool, we detected DEGs from the predicted gene expression data for downstream analysis through

[ 19 ].…”

Section: Methodsmentioning

confidence: 99%

“…Through the entire analysis, we used ByMethyl R package [ 10 ]. Next, we identified differentially expressed genes (DEGs) using downstream analysis, Empirical Bayes test using

[ 17 , 18 , 19 ]. After we applied a recently released deep learning method, “ nnet ” (feed-forward neural network based model) [ 20 ] to interpret those DEGs for determining the classification capacity of uterine cancer and normal groups, we then estimated all classification metrics (average accuracy, average sensitivity, average specificity, average precision, average overall error rate and area under curve (AUC)) using 10-fold cross validation.…”

Section: Introductionmentioning

confidence: 99%

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A Linear Regression and Deep Learning Approach for Detecting Reliable Genetic Alterations in Cancer Using DNA Methylation and Gene Expression Data

Mallik

Seth

Bhadra

et al. 2020

Genes

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DNA methylation change has been useful for cancer biomarker discovery, classification, and potential treatment development. So far, existing methods use either differentially methylated CpG sites or combined CpG sites, namely differentially methylated regions, that can be mapped to genes. However, such methylation signal mapping has limitations. To address these limitations, in this study, we introduced a combinatorial framework using linear regression, differential expression, deep learning method for accurate biological interpretation of DNA methylation through integrating DNA methylation data and corresponding TCGA gene expression data. We demonstrated it for uterine cervical cancer. First, we pre-filtered outliers from the data set and then determined the predicted gene expression value from the pre-filtered methylation data through linear regression. We identified differentially expressed genes (DEGs) by Empirical Bayes test using Limma. Then we applied a deep learning method, “nnet” to classify the cervical cancer label of those DEGs to determine all classification metrics including accuracy and area under curve (AUC) through 10-fold cross validation. We applied our approach to uterine cervical cancer DNA methylation dataset (NCBI accession ID: GSE30760, 27,578 features covering 63 tumor and 152 matched normal samples). After linear regression and differential expression analysis, we obtained 6287 DEGs with false discovery rate (FDR) <0.001. After performing deep learning analysis, we obtained average classification accuracy 90.69% (±1.97%) of the uterine cervical cancerous labels. This performance is better than that of other peer methods. We performed in-degree and out-degree hub gene network analysis using Cytoscape. We reported five top in-degree genes (PAIP2, GRWD1, VPS4B, CRADD and LLPH) and five top out-degree genes (MRPL35, FAM177A1, STAT4, ASPSCR1 and FABP7). After that, we performed KEGG pathway and Gene Ontology enrichment analysis of DEGs using tool WebGestalt(WEB-based Gene SeT AnaLysis Toolkit). In summary, our proposed framework that integrated linear regression, differential expression, deep learning provides a robust approach to better interpret DNA methylation analysis and gene expression data in disease study.

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“…In this step

normalization [ 24 ] was used and after that we applied

[ 18 , 25 ]. After applying

normalization tool, we detected DEGs from the predicted gene expression data for downstream analysis through

[ 19 ].…”

Section: Methodsmentioning

confidence: 99%

“…Through the entire analysis, we used ByMethyl R package [ 10 ]. Next, we identified differentially expressed genes (DEGs) using downstream analysis, Empirical Bayes test using

Section: Introductionmentioning

confidence: 99%

A Linear Regression and Deep Learning Approach for Detecting Reliable Genetic Alterations in Cancer Using DNA Methylation and Gene Expression Data

Mallik

Seth

Bhadra

et al. 2020

Genes

Self Cite

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“…where m 1 denotes the sample size for the diseased group, m 2 signifies the sample size for the control group, and the total sample size m = m 1 + m 2 . βk , p k notify the corresponding contrast estimator and posterior sample variance for the genes, respectively, [46][47][48].…”

Section: Identifying Differentially Expressed Genes Using Limma-voommentioning

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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“…Many machine learning research activities have been focused on supervised learning. A wide range of data types can be examined and processed using supervised learning techniques (Nasteski, 2017;Mallik et al, 2019;Roy et al, 2018).…”

Section: Supervised Machine Learningmentioning

confidence: 99%

“…These statistical methods can be used to cleanse data and put it together for modeling. These statistical speculation tests and estimation statistics can be useful resources in model selection and in presenting the skills and predictions of the ultimate models (Mallik et al, 2019;Roy et al, 2018). There are several statistical tests that could be grouped into two classes, e.g., parametric and nonparametric tests.…”

Section: Statistic Testmentioning

confidence: 99%

Feature Selection, Clustering, and IoMT on Biomedical Engineering for COVID-19 Pandemic: A Comprehensive Review

Islam¹,

Seth²,

Bhadra³

et al. 2023

JDSIS

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In this era, feature clustering is a prominent technique in data mining. Features clustering have also huge application in biomedical research for multiple purpose including grouping, features reduction and many more. The Internet of Medical Things (IoMT) is a promising and emerging field of research that is having a major impact on knowledge retrieval and networking. IoMT also has significant application in biomedical research regarding remote monitoring and remote healthcare services. In this COVID-19 pandemic situation, psychological effect and human reaction have become a major concern of further research. A dataset can be reduced in size by using feature selection techniques. To facilitate subsequent processing, this will make the data easier to manage. Feature selection is also used to clean, reduce, and reduce dimensions of data. The clustering method has proven to be a powerful tool for finding patterns and structure in both labeled and unlabeled data sets. Our study basically provides various state-of-the-art methods regarding medical IoMT for remote healthcare, feature clustering for information retrieval regarding biomedical science. In this study, we are used five different type of feature selection like Minimum Redundancy - Maximum Relevance (mRMR), Random Forest, Normalized Mutual Information Feature Selection (NMIFS), F-Test and Chi-Square and five different type of Clustering algorithms like Hierarchical Clustering, Density-based spatial clustering of applications with noise (DBSCAN) Clustering, K-Means Clustering, Shrinkage Clustering, and Fuzzy C-Means Clustering. Finally, this study is very useful to understand and apply the appropriate IoMT, feature clustering, and catharsis on the various biomedical applications for the benevolence of society.

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A Multi-classifier Model to Identify Mitochondrial Respiratory Gene Signatures in Human Cancer

Cited by 3 publications

References 57 publications

A Linear Regression and Deep Learning Approach for Detecting Reliable Genetic Alterations in Cancer Using DNA Methylation and Gene Expression Data

A Linear Regression and Deep Learning Approach for Detecting Reliable Genetic Alterations in Cancer Using DNA Methylation and Gene Expression Data

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

Feature Selection, Clustering, and IoMT on Biomedical Engineering for COVID-19 Pandemic: A Comprehensive Review

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