Analysis of Different Pre-Processing Techniques to the Development of Machine Learning Predictors with Gene Expression Profiles

2019 ◽  
Author(s):  
Ian Duran ◽  
Roberto Leandro ◽  
Jose Guevara-Coto
Keyword(s):  
Gene Expression ◽  
Machine Learning ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Processing Techniques

scholarly journals Integrative machine learning analysis of multiple gene expression profiles in cervical cancer

PeerJ ◽  
2018 ◽  
Vol 6 ◽  
pp. e5285 ◽  
Author(s):  
Mei Sze Tan ◽  
Siow-Wee Chang ◽  
Phaik Leng Cheah ◽  
Hwa Jen Yap
Keyword(s):  
Gene Expression ◽  
Machine Learning ◽  
Cervical Cancer ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Hpv Infection ◽  
Gene Set Enrichment Analysis ◽  
Multiple Gene ◽  
Cervical Cancers ◽  
Learning Analysis

Although most of the cervical cancer cases are reported to be closely related to the Human Papillomavirus (HPV) infection, there is a need to study genes that stand up differentially in the final actualization of cervical cancers following HPV infection. In this study, we proposed an integrative machine learning approach to analyse multiple gene expression profiles in cervical cancer in order to identify a set of genetic markers that are associated with and may eventually aid in the diagnosis or prognosis of cervical cancers. The proposed integrative analysis is composed of three steps: namely, (i) gene expression analysis of individual dataset; (ii) meta-analysis of multiple datasets; and (iii) feature selection and machine learning analysis. As a result, 21 gene expressions were identified through the integrative machine learning analysis which including seven supervised and one unsupervised methods. A functional analysis with GSEA (Gene Set Enrichment Analysis) was performed on the selected 21-gene expression set and showed significant enrichment in a nine-potential gene expression signature, namely PEG3, SPON1, BTD and RPLP2 (upregulated genes) and PRDX3, COPB2, LSM3, SLC5A3 and AS1B (downregulated genes).

scholarly journals Supervised machine learning models and protein-protein interaction network analysis of gene expression profiles induced by omega-3 polyunsaturated fatty acids

2022 ◽  
Vol 02 ◽  
Author(s):  
Sergey Shityakov ◽  
Jane Pei-Chen Chang ◽  
Ching-Fang Sun ◽  
David Ta-Wei Guu ◽  
Thomas Dandekar ◽  
...  
Keyword(s):  
Gene Expression ◽  
Machine Learning ◽  
Protein Interaction ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Interaction Network ◽  
Supervised Machine Learning ◽  
Omega 3 ◽  
Protein Protein Interaction ◽  
Protein Protein Interaction Network

Background: Omega-3 polyunsaturated fatty acids (PUFAs), such as eicosapentaenoic (EPA) and docosahexaenoic (DHA) acids, have beneficial effects on human health, but their effect on gene expression in elderly individuals (age ≥ 65) is largely unknown. In order to examine this, the gene expression profiles were analyzed in the healthy subjects (n = 96) at baseline and after 26 weeks of supplementation with EPA+DHA to determine up-regulated and down-regulated dif-ferentially expressed genes (DEGs) triggered by PUFAs. The protein-protein interaction (PPI) networks were constructed by mapping these DEGs to a human interactome and linking them to the specific pathways. Objective: This study aimed to implement supervised machine learning models and protein-protein interaction network analysis of gene expression profiles induced by PUFAs. Methods: The transcriptional profile of GSE12375 was obtained from the Gene Expression Om-nibus database, which is based on the Affymetrix NuGO array. The probe cell intensity data were converted into the gene expression values, and the background correction was performed by the multi-array average algorithm. The LIMMA (Linear Models for Microarray Data) algo-rithm was implemented to identify relevant DEGs at baseline and after 26 weeks of supplemen-tation with a p-value < 0.05. The DAVID web server was used to identify and construct the en-riched KEGG (Kyoto Encyclopedia of Genes and Genomes) pathways. Finally, the construction of machine learning (ML) models, including logistic regression, naïve Bayes, and deep neural networks, were implemented for the analyzed DEGs associated with the specific pathways. Results: The results revealed that up-regulated DEGs were associated with neurotrophin/MAPK signaling, whereas the down-regulated DEGs were linked to cancer, acute myeloid leukemia, and long-term depression pathways. Additionally, ML approaches were able to cluster the EPA/DHA-treated and control groups by the logistic regression performing the best. Conclusion: Overall, this study highlights the pivotal changes in DEGs induced by PUFAs and provides the rationale for the implementation of ML algorithms as predictive models for this type of biomedical data.

scholarly journals Machine learning algorithms reveal unique gene expression profiles in muscle biopsies from patients with different types of myositis

2020 ◽  
Vol 79 (9) ◽  
pp. 1234-1242 ◽  
Author(s):  
Iago Pinal-Fernandez ◽  
Maria Casal-Dominguez ◽  
Assia Derfoul ◽  
Katherine Pak ◽  
Frederick W Miller ◽  
...  
Keyword(s):  
Gene Expression ◽  
Machine Learning ◽  
Learning Algorithm ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Machine Learning Algorithms ◽  
Recursive Feature Elimination ◽  
Machine Learning Algorithm ◽  
Unique Gene ◽  
Muscle Biopsies

ObjectivesMyositis is a heterogeneous family of diseases that includes dermatomyositis (DM), antisynthetase syndrome (AS), immune-mediated necrotising myopathy (IMNM), inclusion body myositis (IBM), polymyositis and overlap myositis. Additional subtypes of myositis can be defined by the presence of myositis-specific autoantibodies (MSAs). The purpose of this study was to define unique gene expression profiles in muscle biopsies from patients with MSA-positive DM, AS and IMNM as well as IBM.MethodsRNA-seq was performed on muscle biopsies from 119 myositis patients with IBM or defined MSAs and 20 controls. Machine learning algorithms were trained on transcriptomic data and recursive feature elimination was used to determine which genes were most useful for classifying muscle biopsies into each type and MSA-defined subtype of myositis.ResultsThe support vector machine learning algorithm classified the muscle biopsies with >90% accuracy. Recursive feature elimination identified genes that are most useful to the machine learning algorithm and that are only overexpressed in one type of myositis. For example, CAMK1G (calcium/calmodulin-dependent protein kinase IG), EGR4 (early growth response protein 4) and CXCL8 (interleukin 8) are highly expressed in AS but not in DM or other types of myositis. Using the same computational approach, we also identified genes that are uniquely overexpressed in different MSA-defined subtypes. These included apolipoprotein A4 (APOA4), which is only expressed in anti-3-hydroxy-3-methylglutaryl-CoA reductase (HMGCR) myopathy, and MADCAM1 (mucosal vascular addressin cell adhesion molecule 1), which is only expressed in anti-Mi2-positive DM.ConclusionsUnique gene expression profiles in muscle biopsies from patients with MSA-defined subtypes of myositis and IBM suggest that different pathological mechanisms underly muscle damage in each of these diseases.

scholarly journals Machine learning algorithms for the identification of potential non-syndromic intellectual disability associated miRNAs

2021 ◽  
Author(s):  
Julián González Betancur ◽  
José Guevara-Coto ◽  
Adarli Romero
Keyword(s):  
Gene Expression ◽  
Machine Learning ◽  
Developmental Disorders ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Machine Learning Algorithms ◽  
Stochastic Gradient Descent ◽  
Temporal Gene Expression ◽  
Interesting Approach ◽  
Spatio Temporal

Abstract Background: Intellectual disabilities (IDs) are a group of developmental disorders with high phenotypic and genotypic heterogeneity. Association of genetic elements to IDs has typically been empirically accomplished, however recently, machine learning (ML) has proved to be an excellent instrument to elucidate these associations. miRNAs are short non-coding molecules that participate in spatiotemporal gene regulation, making them relevant for the understanding ID causality. Methods: In this study we used the BrainSpan spatio-temporal expression database to develop a series of machine learning predictors: SVM, RF, FF-ANN, and Stochastic Gradient Descent Classifier. These models were capable of recognizing gene expression profiles. The best classifier was used to label miRNAs associated with NS-IDs using the BrainSpan expression profiles. Results: The model with the best performance was a FF-ANN with 0.78 of F1-score, 0.78 of weighted recall and 0.78 of weighted precision. We used this model to identify miRNAs with high probability to be associated with NS-IDs using the spatio-temporal gene expression profile in the human brain. Labeled miRNAs that were annotated were associated with processes related to either IDs and-or neurodevelopmental processes. Conclusions: The development of a machine learning framework that identified potential NS-ID miRNAs represents an interesting approach for the identification of a potential list of on genes that could be subject for further experimental validation. This study also reinforces the potential of machine learning frameworks in their discovery of potential biomarkers that could improve disease detection and management.

scholarly journals Classification models for Invasive Ductal Carcinoma Progression, based on gene expression data-trained supervised machine learning

2019 ◽  
Author(s):  
Shikha Roy ◽  
Rakesh Kumar ◽  
Vaibhav Mittal ◽  
Dinesh Gupta
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Machine Learning ◽  
Invasive Ductal Carcinoma ◽  
Ductal Carcinoma ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Classification Models ◽  
Rna Seq ◽  
Late Stages

AbstractEarly detection of breast cancer and its correct stage determination are important for prognosis and rendering appropriate personalized clinical treatment to breast cancer patients. However, despite considerable efforts and progress, there is a need to identify the specific genomic factors responsible for, or accompanying Invasive Ductal Carcinoma (IDC) progression stages, which can aid the determination of the correct cancer stages. We have developed two-class machine-learning classification models to differentiate the early and late stages of invasive ductal carcinoma. The prediction models are trained with RNA-seq gene expression profiles representing different IDC stages of 610 patients, obtained from The Cancer Genome Atlas (TCGA). Different supervised learning algorithms were trained and evaluated with an enriched model learning, facilitated by different feature selection methods. We also developed a machine-learning classifier trained on the same datasets with training sets reduced data corresponding to IDC driver genes. Based on these two classifiers, we have developed a web-server Duct-BRCA-CSP to predict early stage from late stages of IDC based on input RNA-seq gene expression profiles. The analysis conducted by us also enables deeper insights into the stage-dependent molecular events accompanying breast ductal carcinoma progression. The server is publicly available at http://bioinfo.icgeb.res.in/duct-BRCA-CSP.

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