Analysis of Microarray Data using Artificial Intelligence Based Techniques

Raza, Khalid

doi:10.4018/978-1-5225-0427-6.ch011

Cited by 18 publications

(9 citation statements)

References 85 publications

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“…2 . Genes with a logFC ≥1.0 were considered to be upregulated and those with logFC ≤-1.0 were considered to be downregulated in BPH tissues ( 32 , 33 ). Following conventional rules, a threshold of a two-fold change in gene expression (i.e., -1.0 ≤logFC ≥1.0), and P≤0.05 (5% significance level) were used to short-list DEGs in BPH.…”

Section: Resultsmentioning

confidence: 99%

Discovering pathways in benign prostate hyperplasia: A functional genomics pilot study

Chen¹,

2021

Exp Ther Med

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Benign prostate hyperplasia (BPH) is one of the well-known urological neoplasms common in males with an increasing number of associated deaths in aging males. It causes uncomfortable urinary symptoms, including urine flow blockage, and may cause bladder, urinary tract or kidney problems. The histopathological and clinical knowledge regarding BPH is limited. In the present study, an in silico approach was applied that uses genome-scale microarray expression data to discover a wide range of protein-protein interactions in addition to focusing on specific genes responsible for BPH to develop prognostic biomarkers. Various genes that were differentially expressed in BPH were identified. Gene and functional annotation clusters were determined and an interaction analysis with disease phenotypes of BPH was performed, as well as an RNA tissue specificity analysis. Furthermore, a molecular docking study of certain short-listed gene biomarkers, namely anterior gradient 2 (AGR2; PDB ID: 2LNT), steroid 5α-reductase 2 (PDB ID: 6OQX), zinc finger protein 3 (PDB ID: 5T00) and collagen type XII α1 chain (PDB ID: 1U5M), was performed in order to identify alternative Chinese herbal agents for the treatment of BPH. Data from the present study revealed that AGR2 receptor (PDB ID: 2LNT) and berberine (Huang Bo) form the most stable complex and therefore may be assessed in further pharmacological studies for the treatment of BPH.

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Section: Resultsmentioning

confidence: 99%

Discovering pathways in benign prostate hyperplasia: A functional genomics pilot study

Chen¹,

2021

Exp Ther Med

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“…are very complex which behave in a fuzzy manner. Fuzzy logic describing and analyzing biological systems (Raza, 2016a) ach for GRN inference.…”

Section: Nference Methodsmentioning

confidence: 99%

Fuzzy logic based approaches for gene regulatory network inference

Raza

2019

Artificial Intelligence in Medicine

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The rapid advancement in high-throughput techniques has fuelled the generation of large volume of biological data rapidly with low cost. Some of these techniques are microarray and next generation sequencing which provides genome level insight of living cells. As a result, the size of most of the biological databases, such as NCBI-GEO, NCBI-SRA, is exponentially growing. These biological data are analyzed using computational techniques for knowledge discovery -which is one of the objectives of bioinformatics research. Gene regulatory network (GRN) is a gene-gene interaction network which plays pivotal role in understanding gene regulation process and disease studies. From the last couple of decades, the researchers are interested in developing computational algorithms for GRN inference (GRNI) using high-throughput experimental data. Several computational approaches have been applied for inferring GRN from gene expression data including statistical techniques (correlation coefficient), information theory (mutual information), regression based approaches, probabilistic approaches (Bayesian networks, naïve byes), artificial neural networks, and fuzzy logic. The fuzzy logic, along with its hybridization with other intelligent approach, is well studied in GRNI due to its several advantages. In this paper, we present a consolidated review on fuzzy logic and its hybrid approaches for GRNI developed during last two decades.

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“…For the RNA-Seq based classification of disease, it is important to identify relevant features before we apply an appropriate machine learning classifiers. In the transcriptomic analysis, the identification of differentially expressed genes (DEGs) is an important task which helps to find out the gene biomarkers or gene responsible for the occurrence of a particular disease [1,2]. Hence, DEGs can be utilized as a feature vector in order to the classification of RNA-Seq data.…”

Section: Differentially Expressed Genes (Features) Identificationmentioning

confidence: 99%

“…These technologies have made the field of biology a data-rich science, with lots of scope of big data analytics. RNA sequencing (RNA-Seq), a better alternative to Microarray technology [1], enables the researchers to assess the expression degrees of genome-wide transcripts simultaneously. These gene expression profiles are nowadays considered as a rising technique for disease classification, diagnosis, and identification of potential disease biomarkers [2].…”

Section: Introductionmentioning

confidence: 99%

A Framework for the RNA-Seq Based Classification and Prediction of Disease

Iqbal¹,

Kumar²

2019

Preprint

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Disease classification based on biological data is an important area in bioinformatics and biomedical research. It helps the doctors and medical practitioners for the early detection of disease and support them as a computer-aided diagnostic tool for accurate diagnosis, prognosis, and treatment of disease. Earlier Microarray gene expression data have wide application for the classification of disease, but now Next-generation sequencing (NGS) has replaced the Microarray technology. From the last few years, RNA sequence (RNA-Seq) data are widely used for the transcriptomic analysis. Hence, RNA-Seq based classification of disease is in its infancy. In this article, we present a general framework for the classification of disease constructed on RNA-Seq data. This framework will guide the researchers to process RNA-Seq, extract relevant features and apply the appropriate classifier to classify any kind of disease.

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Analysis of Microarray Data using Artificial Intelligence Based Techniques

Cited by 18 publications

References 85 publications

Discovering pathways in benign prostate hyperplasia: A functional genomics pilot study

Discovering pathways in benign prostate hyperplasia: A functional genomics pilot study

Fuzzy logic based approaches for gene regulatory network inference

A Framework for the RNA-Seq Based Classification and Prediction of Disease

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