scholarly journals Feature selection based on differentially correlated gene pairs reveals the mechanism of IFN-β therapy for multiple sclerosis

PeerJ ◽  
2020 ◽  
Vol 8 ◽  
pp. e8812 ◽  
Author(s):  
Tao Jin ◽  
Chi Wang ◽  
Suyan Tian
Keyword(s):  
Gene Expression ◽  
Multiple Sclerosis ◽  
Feature Selection ◽  
Learning Strategy ◽  
Gene List ◽  
Feature Selection Method ◽  
Expression Levels ◽  
Gene Pairs ◽  
Treatment Research ◽  
Gene Expression Levels

Multiple sclerosis (MS) is one of the most common neurological disabilities of the central nervous system. Immune-modulatory therapy with Interferon-β (IFN-β) is a commonly used first-line treatment to prevent MS patients from relapses. Nevertheless, a large proportion of MS patients on IFN-β therapy experience their first relapse within 2 years of treatment initiation. Feature selection, a machine learning strategy, is routinely used in the fields of bioinformatics and computational biology to determine which subset of genes is most relevant to an outcome of interest. The majority of feature selection methods focus on alterations in gene expression levels. In this study, we sought to determine which genes are most relevant to relapse of MS patients on IFN-β therapy. Rather than the usual focus on alterations in gene expression levels, we devised a feature selection method based on alterations in gene-to-gene interactions. In this study, we applied the proposed method to a longitudinal microarray dataset and evaluated the IFN-β effect on MS patients to identify gene pairs with differentially correlated edges that are consistent over time in the responder group compared to the non-responder group. The resulting gene list had a good predictive ability on an independent validation set and explicit biological implications related to MS. To conclude, it is anticipated that the proposed method will gain widespread interest and application in personalized treatment research to facilitate prediction of which patients may respond to a specific regimen.

STAT5a and STAT6 gene expression levels in multiple sclerosis patients

Cytokine ◽  
2018 ◽  
Vol 106 ◽  
pp. 108-113 ◽  
Author(s):  
Mahsa Hatami ◽  
Tayyebali Salmani ◽  
Shahram Arsang-Jang ◽  
Mir Davood Omrani ◽  
Mehrdokht Mazdeh ◽  
...  
Keyword(s):  
Gene Expression ◽  
Multiple Sclerosis ◽  
Expression Levels ◽  
Multiple Sclerosis Patients ◽  
Gene Expression Levels

Investigation of the miRNA146a and miRNA155 gene expression levels in patients with multiple sclerosis

2020 ◽  
Vol 78 ◽  
pp. 189-193 ◽  
Author(s):  
Behrouz Shademan ◽  
Alireza Nourazarian ◽  
Masoud Nikanfar ◽  
Cigir Biray Avci ◽  
Mehdi Hasanpour ◽  
...  
Keyword(s):  
Gene Expression ◽  
Multiple Sclerosis ◽  
Expression Levels ◽  
Gene Expression Levels

scholarly journals Anti-Human Herpesvirus 6 A/B Antibodies Titers Correlate With Multiple Sclerosis-Associated Retrovirus Envelope Expression

2021 ◽  
Vol 12 ◽  
Author(s):  
Silvia Pérez-Pérez ◽  
María I. Domínguez-Mozo ◽  
M. Ángel García-Martínez ◽  
M. Celeste García-Frontini ◽  
Noelia Villarrubia ◽  
...  
Keyword(s):  
Gene Expression ◽  
Multiple Sclerosis ◽  
Human Herpesvirus ◽  
Endogenous Retrovirus ◽  
Human Endogenous Retrovirus ◽  
Expression Levels ◽  
Barr Virus ◽  
Healthy Donors ◽  
Epstein Barr ◽  
Gene Expression Levels

Human endogenous retrovirus W family envelope proteins (pHERV-W ENV/syncytin-1) have been repeatedly associated with multiple sclerosis (MS). Here, we have focused on the study of pHERV-W ENV/syncytin-1 expression levels in MS patients (relapsing and progressive forms) and in healthy donors (HD) and on exploring their possible relationship with Epstein-Barr virus (EBV) and human herpesvirus-6A/B (HHV-6A/B). We included blood samples from 101 MS patients and 37 HD to analyze antiviral antibody titers by ELISA and pHERV-W ENV/syncytin-1 expression levels by flow cytometry as well as by qPCR. Patients with relapsing MS forms showed significantly higher pHERV-W ENV/syncytin-1 protein and gene expression levels than HD. Progressive MS patients also showed significantly higher protein and gene expression levels than both HD and relapsing MS patients. Regarding antiviral antibodies titers, anti-HHV-6A/B IgM levels were positively correlated with pHERV-W ENV/syncytin-1 protein expression levels in patients with relapsing MS, while in the progressive forms patients this correlation was found with anti-HHVA/B IgG levels. Therefore, pHERV-W ENV could be involved in MS pathogenesis, playing a role in relapsing and progressive forms. Besides, anti-HHV-6A/B antibodies positively correlated with pHERV-W ENV expression. Further studies are needed to better understand this possible relationship.

scholarly journals Multi-Level Model to Predict Antibody Response to Influenza Vaccine Using Gene Expression Interaction Network Feature Selection

2019 ◽  
Vol 7 (3) ◽  
pp. 79 ◽  
Author(s):  
Saeid Parvandeh ◽  
Greg Poland ◽  
Richard Kennedy ◽  
Brett McKinney
Keyword(s):  
Gene Expression ◽  
Machine Learning ◽  
Feature Selection ◽  
Antibody Response ◽  
Influenza Vaccine ◽  
Interaction Network ◽  
Expression Levels ◽  
Antibody Titers ◽  
Multi Level ◽  
Gene Expression Levels

Vaccination is an effective prevention of influenza infection. However, certain individuals develop a lower antibody response after vaccination, which may lead to susceptibility to subsequent infection. An important challenge in human health is to find baseline gene signatures to help identify individuals who are at higher risk for infection despite influenza vaccination. We developed a multi-level machine learning strategy to build a predictive model of vaccine response using pre−vaccination antibody titers and network interactions between pre−vaccination gene expression levels. The first-level baseline−antibody model explains a significant amount of variation in post-vaccination response, especially for subjects with large pre−existing antibody titers. In the second level, we clustered individuals based on pre−vaccination antibody titers to focus gene−based modeling on individuals with lower baseline HAI where additional response variation may be predicted by baseline gene expression levels. In the third level, we used a gene−association interaction network (GAIN) feature selection algorithm to find the best pairs of genes that interact to influence antibody response within each baseline titer cluster. We used ratios of the top interacting genes as predictors to stabilize machine learning model generalizability. We trained and tested the multi-level approach on data with young and older individuals immunized against influenza vaccine in multiple cohorts. Our results indicate that the GAIN feature selection approach improves model generalizability and identifies genes enriched for immunologically relevant pathways, including B Cell Receptor signaling and antigen processing. Using a multi-level approach, starting with a baseline HAI model and stratifying on baseline HAI, allows for more targeted gene−based modeling. We provide an interactive tool that may be extended to other vaccine studies.

An Integrated Feature Selection Algorithm for Cancer Classification using Gene Expression Data

2019 ◽  
Vol 21 (9) ◽  
pp. 631-645 ◽  
Author(s):  
Saeed Ahmed ◽  
Muhammad Kabir ◽  
Zakir Ali ◽  
Muhammad Arif ◽  
Farman Ali ◽  
...  
Keyword(s):  
Gene Expression ◽  
Feature Selection ◽  
Gene Expression Data ◽  
Classification Accuracy ◽  
Early Stage ◽  
Small Sample Size ◽  
Feature Selection Method ◽  
Small Sample ◽  
Expression Data ◽  
Base Function

Aim and Objective: Cancer is a dangerous disease worldwide, caused by somatic mutations in the genome. Diagnosis of this deadly disease at an early stage is exceptionally new clinical application of microarray data. In DNA microarray technology, gene expression data have a high dimension with small sample size. Therefore, the development of efficient and robust feature selection methods is indispensable that identify a small set of genes to achieve better classification performance. Materials and Methods: In this study, we developed a hybrid feature selection method that integrates correlation-based feature selection (CFS) and Multi-Objective Evolutionary Algorithm (MOEA) approaches which select the highly informative genes. The hybrid model with Redial base function neural network (RBFNN) classifier has been evaluated on 11 benchmark gene expression datasets by employing a 10-fold cross-validation test. Results: The experimental results are compared with seven conventional-based feature selection and other methods in the literature, which shows that our approach owned the obvious merits in the aspect of classification accuracy ratio and some genes selected by extensive comparing with other methods. Conclusion: Our proposed CFS-MOEA algorithm attained up to 100% classification accuracy for six out of eleven datasets with a minimal sized predictive gene subset.

scholarly journals A Sparse and Low-Rank Regression Model for Identifying the Relationships Between DNA Methylation and Gene Expression Levels in Gastric Cancer and the Prediction of Prognosis

Genes ◽  
2021 ◽  
Vol 12 (6) ◽  
pp. 854
Author(s):  
Yishu Wang ◽  
Lingyun Xu ◽  
Dongmei Ai
Keyword(s):  
Gene Expression ◽  
Gastric Cancer ◽  
Dna Methylation ◽  
Regression Model ◽  
The Cancer Genome Atlas ◽  
Low Rank ◽  
Expression Levels ◽  
Rank Regression ◽  
Prediction Of Prognosis ◽  
Gene Expression Levels

DNA methylation is an important regulator of gene expression that can influence tumor heterogeneity and shows weak and varying expression levels among different genes. Gastric cancer (GC) is a highly heterogeneous cancer of the digestive system with a high mortality rate worldwide. The heterogeneous subtypes of GC lead to different prognoses. In this study, we explored the relationships between DNA methylation and gene expression levels by introducing a sparse low-rank regression model based on a GC dataset with 375 tumor samples and 32 normal samples from The Cancer Genome Atlas database. Differences in the DNA methylation levels and sites were found to be associated with differences in the expressed genes related to GC development. Overall, 29 methylation-driven genes were found to be related to the GC subtypes, and in the prognostic model, we explored five prognoses related to the methylation sites. Finally, based on a low-rank matrix, seven subgroups were identified with different methylation statuses. These specific classifications based on DNA methylation levels may help to account for heterogeneity and aid in personalized treatments.

scholarly journals High heterogeneity undermines generalization of differential expression results in RNA-Seq analysis

2021 ◽  
Vol 15 (1) ◽  
Author(s):  
Weitong Cui ◽  
Huaru Xue ◽  
Lei Wei ◽  
Jinghua Jin ◽  
Xuewen Tian ◽  
...  
Keyword(s):  
Gene Expression ◽  
Differential Expression ◽  
Small Sample ◽  
Differentially Expressed ◽  
Cancer Type ◽  
Rna Seq ◽  
Sample Sizes ◽  
Large Sample ◽  
Expression Levels ◽  
Gene Expression Levels

Abstract Background RNA sequencing (RNA-Seq) has been widely applied in oncology for monitoring transcriptome changes. However, the emerging problem that high variation of gene expression levels caused by tumor heterogeneity may affect the reproducibility of differential expression (DE) results has rarely been studied. Here, we investigated the reproducibility of DE results for any given number of biological replicates between 3 and 24 and explored why a great many differentially expressed genes (DEGs) were not reproducible. Results Our findings demonstrate that poor reproducibility of DE results exists not only for small sample sizes, but also for relatively large sample sizes. Quite a few of the DEGs detected are specific to the samples in use, rather than genuinely differentially expressed under different conditions. Poor reproducibility of DE results is mainly caused by high variation of gene expression levels for the same gene in different samples. Even though biological variation may account for much of the high variation of gene expression levels, the effect of outlier count data also needs to be treated seriously, as outlier data severely interfere with DE analysis. Conclusions High heterogeneity exists not only in tumor tissue samples of each cancer type studied, but also in normal samples. High heterogeneity leads to poor reproducibility of DEGs, undermining generalization of differential expression results. Therefore, it is necessary to use large sample sizes (at least 10 if possible) in RNA-Seq experimental designs to reduce the impact of biological variability and DE results should be interpreted cautiously unless soundly validated.

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