Using bioinformatics and genome analysis for new therapeutic interventions

Mount, David W.; Pandey, Ritu

doi:10.1158/1535-7163.mct-05-0150

Cited by 31 publications

(5 citation statements)

References 71 publications

(57 reference statements)

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“…However, many large-scale surveys of diversity have provided numerous data on covariance. A picture is started from the driving forces in human evolution and population diversity that is emerged ( Siavelis et al, 2016 ; Mathur, 2018 ; Raza, 2012 ; Wodehouse, 2006 ; Mount & Pandey, 2005 ; Barnes, 2010 ).…”

Section: A Genetic Analysis Of Complex Brain Diseasesmentioning

confidence: 99%

Genetic variations analysis for complex brain disease diagnosis using machine learning techniques: opportunities and hurdles

Ahmed

Alarabi

El–Sappagh

et al. 2021

PeerJ Computer Science

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Background and Objectives This paper presents an in-depth review of the state-of-the-art genetic variations analysis to discover complex genes associated with the brain’s genetic disorders. We first introduce the genetic analysis of complex brain diseases, genetic variation, and DNA microarrays. Then, the review focuses on available machine learning methods used for complex brain disease classification. Therein, we discuss the various datasets, preprocessing, feature selection and extraction, and classification strategies. In particular, we concentrate on studying single nucleotide polymorphisms (SNP) that support the highest resolution for genomic fingerprinting for tracking disease genes. Subsequently, the study provides an overview of the applications for some specific diseases, including autism spectrum disorder, brain cancer, and Alzheimer’s disease (AD). The study argues that despite the significant recent developments in the analysis and treatment of genetic disorders, there are considerable challenges to elucidate causative mutations, especially from the viewpoint of implementing genetic analysis in clinical practice. The review finally provides a critical discussion on the applicability of genetic variations analysis for complex brain disease identification highlighting the future challenges. Methods We used a methodology for literature surveys to obtain data from academic databases. Criteria were defined for inclusion and exclusion. The selection of articles was followed by three stages. In addition, the principal methods for machine learning to classify the disease were presented in each stage in more detail. Results It was revealed that machine learning based on SNP was widely utilized to solve problems of genetic variation for complex diseases related to genes. Conclusions Despite significant developments in genetic diseases in the past two decades of the diagnosis and treatment, there is still a large percentage in which the causative mutation cannot be determined, and a final genetic diagnosis remains elusive. So, we need to detect the variations of the genes related to brain disorders in the early disease stages.

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Section: A Genetic Analysis Of Complex Brain Diseasesmentioning

confidence: 99%

Genetic variations analysis for complex brain disease diagnosis using machine learning techniques: opportunities and hurdles

Ahmed

Alarabi

El–Sappagh

et al. 2021

PeerJ Computer Science

View full text Add to dashboard Cite

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“…Analyzing and mapping SNPs in a publicly available genome have been an effective method to discover potential drug targets for genetic diseases [Botstein and Risch, 2003;Iida and Nakamura, 2005]. The general approach for SNP analysis is to first choose a candidate gene, screen for SNPs, and then to determine haplotypes, haplotype frequencies, and risk (disease association or drug response) associated with each haplotype [Mount and Pandey, 2005]. In contract with previous research, Balasubramanian et al [2005] proposed a unique method of assessing the disease-causing potential SNPs based on a set of sequencebased features.…”

Section: Genetics Analysis Reveals Clues To Gpcrs Identificationmentioning

confidence: 99%

Computational identification and analysis of G protein‐coupled receptor targets

Feng

Jiang

2006

Drug Development Research

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The completion of the human genome sequence has provided a large pool of potential drug targets for disease therapy. G protein–coupled receptors (GPCRs), which are central to signaling networks that regulate basic cellular processes, represent the most important known class of therapeutic targets for multiple disease states. Bioinformatics approaches can be applied to facilitate the identification of novel GPCRs, understanding their physiological and pathological roles, and screening for drug discovery. The present review summarizes current bioinformatics approaches that can be used to identify and analyze GPCR targets. In addition, the limitations of these technologies with the intention of setting reasonable expectations are also discussed together with some potential avenues for GPCR research. Drug Dev. Res. 67:771–780, 2006. © 2007 Wiley‐Liss, Inc.

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“…Currently, the availability of information about the human genome and proteome, especially those that assist in the development of new anti-cancer agents, is largely dependent on advances in bioinformatics. As an enabling technology, bioinformatics bridges the gap between sequence information and clinical practice, and it has evolved into multiple ways to enable us not only to identify “driver” and “passenger” genes toward neoplasia, but also to comprehend genetic alterations and mechanisms in cancer ( Mount and Pandey, 2005 ).…”

Section: Introductionmentioning

confidence: 99%

Identification and validation of novel prognostic biomarkers and therapeutic targets for non-small cell lung cancer

et al. 2023

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Background: Despite the significant survival benefits of anti-PD-1/PD-L1 immunotherapy, non-small cell lung cancer (NSCLC) remains one of the most common tumors and major causes of cancer-related deaths worldwide. Thus, there is an urgent need to identify new therapeutic targets for this refractory disease.Methods: In this study, microarray datasets GSE27262, GSE75037, GSE102287, and GSE21933 were integrated by Venn diagram. We performed functional clustering and pathway enrichment analyses using R. Through the STRING database and Cytoscape, we conducted protein-protein interaction (PPI) network analysis and identified the key genes, which were verified by the GEPIA2 and UALCAN portal. Validation of actin-binding protein anillin (ANLN) was performed by quantitative real-time polymerase chain reaction and Western blotting. Additionally, Kaplan-Meier methods were used to compute the survival analyses.Results: In total, 126 differentially expressed genes were identified, which were enriched in mitotic nuclear division, mitotic cell cycle G2/M transition, vasculogenesis, spindle, and peroxisome proliferator-activated receptor signaling pathway. 12 central node genes were identified in the PPI network complex. The survival analysis revealed that high transcriptional levels were associated with inferior survival in NSCLC patients. The clinical implication of ANLN was further explored; its protein expression showed a gradually increasing trend from grade I to III.Conclusion: These Key genes may be involved in the carcinogenesis and progression of NSCLC, which may serve as useful targets for NSCLC diagnosis and treatment.

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Using bioinformatics and genome analysis for new therapeutic interventions

Cited by 31 publications

References 71 publications

Genetic variations analysis for complex brain disease diagnosis using machine learning techniques: opportunities and hurdles

Genetic variations analysis for complex brain disease diagnosis using machine learning techniques: opportunities and hurdles

Computational identification and analysis of G protein‐coupled receptor targets

Identification and validation of novel prognostic biomarkers and therapeutic targets for non-small cell lung cancer

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