Prediction of Drug-Gene Interaction by Using Biomedical Subgraph Patterns

2022 ◽  
pp. 159-168
Author(s):  
Guangjin Zhao ◽  
Meijing Li ◽  
Yingying Jiang
Keyword(s):  
Gene Interaction

Identifying Alzheimer’s Disease-related miRNA Based on Semi-clustering

2019 ◽  
Vol 19 (4) ◽  
pp. 216-223 ◽  
Author(s):  
Tianyi Zhao ◽  
Donghua Wang ◽  
Yang Hu ◽  
Ningyi Zhang ◽  
Tianyi Zang ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Drug Targets ◽  
Molecular Mechanisms ◽  
Feature Vector ◽  
Mirna Gene ◽  
Interaction Network ◽  
Gene Interaction ◽  
Proteinprotein Interaction ◽  
Synaptic Structures

Background: More and more scholars are trying to use it as a specific biomarker for Alzheimer’s Disease (AD) and mild cognitive impairment (MCI). Multiple studies have indicated that miRNAs are associated with poor axonal growth and loss of synaptic structures, both of which are early events in AD. The overall loss of miRNA may be associated with aging, increasing the incidence of AD, and may also be involved in the disease through some specific molecular mechanisms. Objective: Identifying Alzheimer’s disease-related miRNA can help us find new drug targets, early diagnosis. Materials and Methods: We used genes as a bridge to connect AD and miRNAs. Firstly, proteinprotein interaction network is used to find more AD-related genes by known AD-related genes. Then, each miRNA’s correlation with these genes is obtained by miRNA-gene interaction. Finally, each miRNA could get a feature vector representing its correlation with AD. Unlike other studies, we do not generate negative samples randomly with using classification method to identify AD-related miRNAs. Here we use a semi-clustering method ‘one-class SVM’. AD-related miRNAs are considered as outliers and our aim is to identify the miRNAs that are similar to known AD-related miRNAs (outliers). Results and Conclusion: We identified 257 novel AD-related miRNAs and compare our method with SVM which is applied by generating negative samples. The AUC of our method is much higher than SVM and we did case studies to prove that our results are reliable.

A Mechanism of Gene Interaction

1955 ◽  
Vol 89 (846) ◽  
pp. 141-150 ◽  
Author(s):  
Bernard S. Strauss
Keyword(s):  
Gene Interaction

scholarly journals Recent Findings Unravel Genes and Genetic Factors Underlying Leptosphaeria maculans Resistance in Brassica napus and Its Relatives

2020 ◽  
Vol 22 (1) ◽  
pp. 313
Author(s):  
Aldrin Y. Cantila ◽  
Nur Shuhadah Mohd Saad ◽  
Junrey C. Amas ◽  
David Edwards ◽  
Jacqueline Batley
Keyword(s):  
Brassica Napus ◽  
Genetic Factors ◽  
Quantitative Resistance ◽  
Genetic Resistance ◽  
Leptosphaeria Maculans ◽  
Gene Interaction ◽  
R Gene ◽  
R Genes ◽  
Blackleg Resistance ◽  
Avr Gene

Among the Brassica oilseeds, canola (Brassica napus) is the most economically significant globally. However, its production can be limited by blackleg disease, caused by the fungal pathogen Lepstosphaeria maculans. The deployment of resistance genes has been implemented as one of the key strategies to manage the disease. Genetic resistance against blackleg comes in two forms: qualitative resistance, controlled by a single, major resistance gene (R gene), and quantitative resistance (QR), controlled by numerous, small effect loci. R-gene-mediated blackleg resistance has been extensively studied, wherein several genomic regions harbouring R genes against L. maculans have been identified and three of these genes were cloned. These studies advance our understanding of the mechanism of R gene and pathogen avirulence (Avr) gene interaction. Notably, these studies revealed a more complex interaction than originally thought. Advances in genomics help unravel these complexities, providing insights into the genes and genetic factors towards improving blackleg resistance. Here, we aim to discuss the existing R-gene-mediated resistance, make a summary of candidate R genes against the disease, and emphasise the role of players involved in the pathogenicity and resistance. The comprehensive result will allow breeders to improve resistance to L. maculans, thereby increasing yield.

Integration of gene interaction information into a reweighted Lasso-Cox model for accurate survival prediction

2020 ◽  
Author(s):  
Wei Wang ◽  
Wei Liu
Keyword(s):  
Cox Model ◽  
Gene Interaction ◽  
Supplementary Information ◽  
Survival Prediction ◽  
Gene Interaction Network ◽  
Clinical Cancer Research ◽  
Prognostic Performance ◽  
Interaction Information ◽  
Cancer Types ◽  
Risk Of Cancer

Abstract Motivation Accurately predicting the risk of cancer patients is a central challenge for clinical cancer research. For high-dimensional gene expression data, Cox proportional hazard model with the least absolute shrinkage and selection operator for variable selection (Lasso-Cox) is one of the most popular feature selection and risk prediction algorithms. However, the Lasso-Cox model treats all genes equally, ignoring the biological characteristics of the genes themselves. This often encounters the problem of poor prognostic performance on independent datasets. Results Here, we propose a Reweighted Lasso-Cox (RLasso-Cox) model to ameliorate this problem by integrating gene interaction information. It is based on the hypothesis that topologically important genes in the gene interaction network tend to have stable expression changes. We used random walk to evaluate the topological weight of genes, and then highlighted topologically important genes to improve the generalization ability of the RLasso-Cox model. Experiments on datasets of three cancer types showed that the RLasso-Cox model improves the prognostic accuracy and robustness compared with the Lasso-Cox model and several existing network-based methods. More importantly, the RLasso-Cox model has the advantage of identifying small gene sets with high prognostic performance on independent datasets, which may play an important role in identifying robust survival biomarkers for various cancer types. Availability and implementation http://bioconductor.org/packages/devel/bioc/html/RLassoCox.html Supplementary information Supplementary data are available at Bioinformatics online.

Evidence of gene‐gene interaction in Hidradenitis suppurativa – A nationwide register study of Danish twins

2021 ◽  
Author(s):  
R. Kjærsgaard Andersen ◽  
S.B. Clemmensen ◽  
L.A. Larsen ◽  
J.v.B. Hjelmborg ◽  
N. Ødum ◽  
...  
Keyword(s):  
Hidradenitis Suppurativa ◽  
Gene Interaction ◽  
Register Study ◽  
Nationwide Register Study

scholarly journals Interactions between Gene Variants within the COL1A1 and COL5A1 Genes and Musculoskeletal Injuries in Physically Active Caucasian

Genes ◽  
2021 ◽  
Vol 12 (7) ◽  
pp. 1056
Author(s):  
Katarzyna Leźnicka ◽  
Ewelina Żyźniewska-Banaszak ◽  
Magdalena Gębska ◽  
Anna Machoy-Mokrzyńska ◽  
Anna Krajewska-Pędzik ◽  
...  
Keyword(s):  
Significant Interaction ◽  
Interaction Analysis ◽  
Gene Interaction ◽  
Musculoskeletal Injury ◽  
Musculoskeletal Injuries ◽  
Caucasian Population ◽  
Muscle Area ◽  
Physically Active ◽  
Haplotype Combination ◽  
Injury Status

The COL1A1 and COL5A1 variants have been associated with the risk of musculoskeletal injuries. Therefore, the main aim of the study was to investigate the association between three polymorphisms within two genes (rs1800012 in COL1A1, as well as rs12722 and rs13946 in COL5A1) and the reported, yet rarely described in the literature, injuries of the joint and muscle area in a physically active Caucasian population. Polish students (n = 114) were recruited and divided into the following two groups: students with (n = 53) and without (n = 61) injures. Genotyping was carried out using real-time PCR. The results obtained revealed a statistically significant association between rs1800012 COL1A1 and injury under an overdominant model. Specifically, when adjusted for age and sex, the GT heterozygotes had a 2.2 times higher chance of being injured compared with both homozygotes (TT and GG, 95% CI 0.59–5.07, p = 0.040). However, no significant interaction between the COL5A1 variants, either individually or in haplotype combination, and susceptibility to injury were found. In addition, the gene–gene interaction analysis did not reveal important relationships with the musculoskeletal injury status. It was demonstrated that rs1800012 COL1A1 may be positively associated with physical activity-related injuries in a Caucasian population. Harboring the specific GT genotype may be linked to a higher risk of being injured.

scholarly journals A showcase study on personalized in silico drug response prediction based on the genetic landscape of muscle invasive bladder cancer

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Friedemann Krentel ◽  
Franziska Singer ◽  
María Lourdes Rosano-Gonzalez ◽  
Ewan A. Gibb ◽  
Yang Liu ◽  
...  
Keyword(s):  
Bladder Cancer ◽  
In Silico ◽  
Drug Response ◽  
Gene Interaction ◽  
Response Prediction ◽  
Manual Curation ◽  
Genetic Landscape ◽  
Promising Treatment ◽  
Muscle Invasive ◽  
The Individual

AbstractImproved and cheaper molecular diagnostics allow the shift from “one size fits all” therapies to personalised treatments targeting the individual tumor. However, the wealth of potential targets based on comprehensive sequencing remains a yet unsolved challenge that prevents its routine use in clinical practice. Thus, we designed a workflow that selects the most promising treatment targets based on multi-omics sequencing and in silico drug prediction. In this study we demonstrate the workflow with focus on bladder cancer (BLCA), as there are, to date, no reliable diagnostics available to predict the potential benefit of a therapeutic approach. Within the TCGA-BLCA cohort, our workflow identified a panel of 21 genes and 72 drugs that suggested personalized treatment for 95% of patients—including five genes not yet reported as prognostic markers for clinical testing in BLCA. The automated predictions were complemented by manually curated data, thus allowing for accurate sensitivity- or resistance-directed drug response predictions. We discuss potential improvements of drug-gene interaction databases on the basis of pitfalls that were identified during manual curation.

scholarly journals Wormicloud: a new text summarization tool based on word clouds to explore the C. elegans literature

Database ◽  
2021 ◽  
Vol 2021 ◽  
Author(s):  
Valerio Arnaboldi ◽  
Jaehyoung Cho ◽  
Paul W Sternberg
Keyword(s):  
Search Engines ◽  
Complex Structure ◽  
Model Organism ◽  
Gene Interaction ◽  
Relevant Information ◽  
Biomedical Literature ◽  
C Elegans ◽  
Word Clouds ◽  
Scientific Papers ◽  
Model Organism Databases

Abstract Finding relevant information from newly published scientific papers is becoming increasingly difficult due to the pace at which articles are published every year as well as the increasing amount of information per paper. Biocuration and model organism databases provide a map for researchers to navigate through the complex structure of the biomedical literature by distilling knowledge into curated and standardized information. In addition, scientific search engines such as PubMed and text-mining tools such as Textpresso allow researchers to easily search for specific biological aspects from newly published papers, facilitating knowledge transfer. However, digesting the information returned by these systems—often a large number of documents—still requires considerable effort. In this paper, we present Wormicloud, a new tool that summarizes scientific articles in a graphical way through word clouds. This tool is aimed at facilitating the discovery of new experimental results not yet curated by model organism databases and is designed for both researchers and biocurators. Wormicloud is customized for the Caenorhabditis  elegans literature and provides several advantages over existing solutions, including being able to perform full-text searches through Textpresso, which provides more accurate results than other existing literature search engines. Wormicloud is integrated through direct links from gene interaction pages in WormBase. Additionally, it allows analysis on the gene sets obtained from literature searches with other WormBase tools such as SimpleMine and Gene Set Enrichment. Database URL: https://wormicloud.textpressolab.com

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