A comprehensive survey on computational learning methods for analysis of gene expression data

Bhandari, Nikita; Walambe, Rahee; Kotecha, Ketan; Khare, Satyajeet P.

doi:10.3389/fmolb.2022.907150

Cited by 14 publications

(11 citation statements)

References 242 publications

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“…We developed a unique strategy of preprocessing microarray data to build a robust model that can be used to analyze a single sample. We used the feature selection approach (DGEA) for better “explainability” in clinical settings 12 and SVM and PAM for binary classification analysis. The two algorithms use different logic for classification, and a consensus rule resulted in higher precision without any significant drop in accuracy compared to the individual models ( Figure 3A and B ).…”

Section: Discussionmentioning

confidence: 99%

“…Support vector machines (SVM) and nearest shrunken centroids (NSC) are popular examples of supervised learning ML algorithms used in genomics, particularly in transcriptomics. 12 …”

Section: Introductionmentioning

confidence: 99%

“…Support vector machines (SVM) and nearest shrunken centroids (NSC) are popular examples of supervised learning ML algorithms used in genomics, particularly in transcriptomics. 12 The ML tools have already provided gene expression markers with higher diagnostic potential. 9,10,13 Currently, the ML-based in silico approaches are limited to publicly available microarray datasets.…”

Section: Introductionmentioning

confidence: 99%

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Ensemble Learning for Higher Diagnostic Precision in Schizophrenia Using Peripheral Blood Gene Expression Profile

Wagh,

Kottat,

Agrawal

et al. 2024

NDT

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Introduction Stigma contributes to a significant part of the burden of schizophrenia (SCZ), therefore reducing false positives from the diagnosis would be liberating for the individuals with SCZ and desirable for the clinicians. The stigmatization associated with schizophrenia advocates the need for high-precision diagnosis. In this study, we present an ensemble learning-based approach for high-precision diagnosis of SCZ using peripheral blood gene expression profiles. Methodology The machine learning (ML) models, support vector machines (SVM), and prediction analysis for microarrays (PAM) were developed using differentially expressed genes (DEGs) as features. The SCZ samples were classified based on a voting ensemble classifier of SVM and PAM. Further, microarray-based learning was used to classify RNA sequencing (RNA-Seq) samples from our case-control study (Pune-SCZ) to assess cross-platform compatibility. Results Ensemble learning using ML models resulted in a significantly higher precision of 80.41% (SD: 0.04) when compared to the individual models (SVM-radial: 71.69%, SD: 0.04 and PAM 77.20%, SD: 0.02). The RNA sequencing samples from our case-control study (Pune-SCZ) resulted in a moderate precision (59.92%, SD: 0.05). The feature genes used for model building were enriched for biological processes such as response to stress, regulation of the immune system, and metabolism of organic nitrogen compounds. The network analysis identified RBX1, CUL4B, DDB1, PRPF19 , and COPS4 as hub genes. Conclusion In summary, this study developed robust models for higher diagnostic precision in psychiatric disorders. Future efforts will be directed towards multi-omic integration and developing “explainable” diagnostic models.

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Section: Discussionmentioning

confidence: 99%

“…Support vector machines (SVM) and nearest shrunken centroids (NSC) are popular examples of supervised learning ML algorithms used in genomics, particularly in transcriptomics. 12 …”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Ensemble Learning for Higher Diagnostic Precision in Schizophrenia Using Peripheral Blood Gene Expression Profile

Wagh,

Kottat,

Agrawal

et al. 2024

NDT

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“…We used differential gene expression analysis (DGEA) for feature selection, which can be important for application of models into clinical settings. Feature selection methods perform better than extraction methods for their "explainability" in clinical settings (Bhandari et al, 2022). In addition, the simple filter methods such as DGEA are computationally less intensive as compared to embedded methods of feature selection.…”

Section: Discussionmentioning

confidence: 99%

Ensemble learning for higher diagnostic precision in schizophrenia using peripheral blood gene expression profile

Wagh

Agrawal²,

Purohit³

et al. 2023

Preprint

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The need for molecular biomarkers for schizophrenia has been well recognized. Recently, peripheral blood gene expression profiling and machine learning (ML) tools have become popular for biomarker discovery. The stigmatization associated with schizophrenia advocates the need for diagnostic models with higher precision. In this study, we propose a strategy to develop higher-precision ML models using ensemble learning. We performed a meta-analysis using peripheral blood expression microarray data. The ML models: Support Vector Machines (SVM), and Prediction Analysis for Microarrays (PAM) were developed using differentially expressed genes as features. The ensemble of SVM-radial and PAM predicted test samples with a precision of 81.33% (SD: 0.078). The precision of the ensemble model was significantly higher than SVM-radial (63.83%, SD: 0.081) and PAM (66.89%, SD: 0.097). The feature genes identified were enriched for biological processes such as response to stress, response to stimulus, regulation of the immune system, and metabolism of organic nitrogen compounds. The network analysis of feature genes identified PRF1, GZMB, IL2RB, ITGAL, and IL2RG as hub genes. Additionally, the ensemble model developed using microarray data classified the RNA-Sequencing samples with moderately high precision. The pipeline developed in this study allows the prediction of a single microarray and RNA-Sequencing sample. In summary, this study developed robust models for clinical application and suggested ensemble learning for higher diagnostic precision in psychiatric disorders.

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“…First, the computational analysis to identify statistically significant differences; and second, the “biological” analysis to identify biologically significant differences. The computational analysis is challenging because of the large amount of data that must be handle by researchers, who require adequate training and specialization ( Bhandari et al, 2022 ); fortunately, researchers count with abundant manuals and reviews that describe computational and informatic tools used for statistical analyses of massive gene expression data ( e. g. , Kappelmann-Fenzl, 2021 ). In contrast, the articles that address how to analyze data for biological significance ( e. g. , Olson, 2006 ; Davidson, 2015 ) are scarce, even though statistical significance should be computed considering biologically meaningful contexts.…”

Section: Introductionmentioning

confidence: 99%

Cortical type: a conceptual tool for meaningful biological interpretation of high-throughput gene expression data in the human cerebral cortex

Sancha-Velasco,

Uceda-Heras,

García-Cabezas

2023

Front. Neuroanat.

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The interpretation of massive high-throughput gene expression data requires computational and biological analyses to identify statistically and biologically significant differences, respectively. There are abundant sources that describe computational tools for statistical analysis of massive gene expression data but few address data analysis for biological significance. In the present article we exemplify the importance of selecting the proper biological context in the human brain for gene expression data analysis and interpretation. For this purpose, we use cortical type as conceptual tool to make predictions about gene expression in areas of the human temporal cortex. We predict that the expression of genes related to glutamatergic transmission would be higher in areas of simpler cortical type, the expression of genes related to GABAergic transmission would be higher in areas of more complex cortical type, and the expression of genes related to epigenetic regulation would be higher in areas of simpler cortical type. Then, we test these predictions with gene expression data from several regions of the human temporal cortex obtained from the Allen Human Brain Atlas. We find that the expression of several genes shows statistically significant differences in agreement with the predicted gradual expression along the laminar complexity gradient of the human cortex, suggesting that simpler cortical types may have greater glutamatergic excitability and epigenetic turnover compared to more complex types; on the other hand, complex cortical types seem to have greater GABAergic inhibitory control compared to simpler types. Our results show that cortical type is a good predictor of synaptic plasticity, epigenetic turnover, and selective vulnerability in human cortical areas. Thus, cortical type can provide a meaningful context for interpreting high-throughput gene expression data in the human cerebral cortex.

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A comprehensive survey on computational learning methods for analysis of gene expression data

Cited by 14 publications

References 242 publications

Ensemble Learning for Higher Diagnostic Precision in Schizophrenia Using Peripheral Blood Gene Expression Profile

Ensemble Learning for Higher Diagnostic Precision in Schizophrenia Using Peripheral Blood Gene Expression Profile

Ensemble learning for higher diagnostic precision in schizophrenia using peripheral blood gene expression profile

Cortical type: a conceptual tool for meaningful biological interpretation of high-throughput gene expression data in the human cerebral cortex

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