Bioinformatics: Trends in gene expression analysis

Raut, Shital; Sathe, S. R.; Raut, Adarsh

doi:10.1109/icbbt.2010.5479003

Cited by 16 publications

(11 citation statements)

References 10 publications

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“…Bioinformatics is a new interdisciplinary subject combining molecular biology and information technology. It is of great significance to reveal the molecular mechanism of diseases (Raut, Sathe & Raut, 2010). As an emerging technology, gene chips have been used for the high-efficiency and large-scale acquisition of biological information, and the expression profile data of diseases can be extensively collected.…”

Section: Introductionmentioning

confidence: 99%

Identification of key biomarkers and immune infiltration in the synovial tissue of osteoarthritis by bioinformatics analysis

Cai

Zhang

et al. 2020

PeerJ

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Background Osteoarthritis (OA) is the most common chronic degenerative joint disease and is mainly characterized by cartilage degeneration, subcartilage bone hyperplasia, osteophyte formation and joint space stenosis. Recent studies showed that synovitis might also be an important pathological change of OA. However, the molecular mechanisms of synovitis in OA are still not well understood. Objective This study was designed to identify key biomarkers and immune infiltration in the synovial tissue of osteoarthritis by bioinformatics analysis. Materials and Methods The gene expression profiles of GSE12021, GSE55235 and GSE55457 were downloaded from the GEO database. The differentially expressed genes (DEGs) were identified by the LIMMA package in Bioconductor, and functional enrichment analyses were performed. A protein-protein interaction network (PPI) was constructed, and module analysis was performed using STRING and Cytoscape. The CIBERSORT algorithm was used to analyze the immune infiltration of synovial tissue between OA and normal controls. Results A total of 106 differentially expressed genes, including 68 downregulated genes and 38 upregulated genes, were detected. The PPI network was assessed, and the most significant module containing 14 hub genes was identified. Gene Ontology analysis revealed that the hub genes were significantly enriched in immune cell chemotaxis and cytokine activity. KEGG pathway analysis showed that the hub genes were significantly enriched in the rheumatoid arthritis signaling pathway, IL-17 signaling pathway and cytokine-cytokine receptor interaction signaling pathway. The immune infiltration profiles varied significantly between osteoarthritis and normal controls. Compared with normal tissue, OA synovial tissue contained a higher proportion of memory B cells, naive CD4+ T cells, regulatory T cells, resting dendritic cells and resting mast cells, while naive CD4+ T cells, activated NK cells, activated mast cells and eosinophils contributed to a relatively lower portion (P > 0.05). Finally, the expression levels of 11 hub genes were confirmed by RT-PCR. Conclusion The hub genes and the difference in immune infiltration in synovial tissue between osteoarthritis and normal controls might provide new insight for understanding OA development.

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Section: Introductionmentioning

confidence: 99%

Identification of key biomarkers and immune infiltration in the synovial tissue of osteoarthritis by bioinformatics analysis

Cai

Zhang

et al. 2020

PeerJ

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“…Gene expression assists the cells to react appropriately to the changing environment. The gene expression involves two critical steps in manufacturing the proteins, Transcription and Translation ( Raut et al, 2010 ).…”

Section: Introductionmentioning

confidence: 99%

“…There are many techniques available to capture the gene expressions such as Northern blot, RNA protection assay, Reverse Transcription – Polymerase Chain Reaction (RT - PCR), Serial Analysis of Gene Expression (SAGE), Subtractive Hybridization, DNA Microarrays, Second Generation Sequencing (NGS) and many others. Among these, the most widely used these days is DNA Microarray ( Raut et al, 2010 ; Wang and van der Laan, 2011 ). The DNA microarray technology manages to capture gene expressions of thousands of genes simultaneously.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Based Computational Gene Selection Models: A Survey, Performance Evaluation, Open Issues, and Future Research Directions

et al. 2020

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Gene Expression is the process of determining the physical characteristics of living beings by generating the necessary proteins. Gene Expression takes place in two steps, translation and transcription. It is the flow of information from DNA to RNA with enzymes’ help, and the end product is proteins and other biochemical molecules. Many technologies can capture Gene Expression from the DNA or RNA. One such technique is Microarray DNA. Other than being expensive, the main issue with Microarray DNA is that it generates high-dimensional data with minimal sample size. The issue in handling such a heavyweight dataset is that the learning model will be over-fitted. This problem should be addressed by reducing the dimension of the data source to a considerable amount. In recent years, Machine Learning has gained popularity in the field of genomic studies. In the literature, many Machine Learning-based Gene Selection approaches have been discussed, which were proposed to improve dimensionality reduction precision. This paper does an extensive review of the various works done on Machine Learning-based gene selection in recent years, along with its performance analysis. The study categorizes various feature selection algorithms under Supervised, Unsupervised, and Semi-supervised learning. The works done in recent years to reduce the features for diagnosing tumors are discussed in detail. Furthermore, the performance of several discussed methods in the literature is analyzed. This study also lists out and briefly discusses the open issues in handling the high-dimension and less sample size data.

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“…As per central dogma of life, two steps are required to convert DNA to complex protein structure [1], [2]. This final complex protein structure is origin for functional activities of organisms cells.…”

Section: Introductionmentioning

confidence: 99%

A Modified Fastmap K-Means Clustering Algorithm for Large Scale Gene Expression Datasets

Raut¹,

Sathe²

2011

IJBBB

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Abstract-Clustering is an important tool of data mining to extract the context or meaningful patterns from the datasets. To extract the patterns from gene expression datasets, cluster analysis is used. From last two to three years, there are noticeably increase in public datasets, like geo, arrayexpress, genebank, are noted. All these increase in public datasets are not only in numbers but also in dimensions. To analyze single experiment, 1000's of genes and 100's of samples are available. To handle these large dimensions, we have to moderate or modify the traditional clustering algorithms.K-means clustering algorithm is one of the most used and tested clustering algorithm not only for gene expression datasets but also for various different datasets. But, as the dimension goes on increasing, CPU time requirement and memory requirement also increasing. Here, we try to increase the speed of K-means algorithm by adding additional phase (by using moderate FastMap) before implementation of k-means algorithm on the datasets. So, Modified FastMap K-means Clustering Algorithm, is a two phase algorithm, which try to reduced CPU time and memory requirements as compared to tradition K-means requirements. We have shown tabular results for three datasets, which are downloaded from public repository, NCBI, geo. The algorithm can successfully generate good results for large as well as small datasets.Index Terms-Gene expression analysis, MFKCA, Cluster analysis.

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Bioinformatics: Trends in gene expression analysis

Cited by 16 publications

References 10 publications

Identification of key biomarkers and immune infiltration in the synovial tissue of osteoarthritis by bioinformatics analysis

Identification of key biomarkers and immune infiltration in the synovial tissue of osteoarthritis by bioinformatics analysis

Machine Learning Based Computational Gene Selection Models: A Survey, Performance Evaluation, Open Issues, and Future Research Directions

A Modified Fastmap K-Means Clustering Algorithm for Large Scale Gene Expression Datasets

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