scholarly journals A computational method for drug repositioning using publicly available gene expression data

2015 ◽  
Vol 16 (S17) ◽  
Author(s):  
KM Shabana ◽  
KA Abdul Nazeer ◽  
Meeta Pradhan ◽  
Mathew Palakal
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Drug Repositioning ◽  
Computational Method ◽  
Expression Data

scholarly journals Robust Lineage Reconstruction from High-Dimensional Single-Cell Data

2016 ◽  
Author(s):  
Gregory Giecold ◽  
Eugenio Marco ◽  
Lorenzo Trippa ◽  
Guo-Cheng Yuan
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Gene Expression Data ◽  
Quantitative Estimate ◽  
Cell Lineage ◽  
Computational Method ◽  
Expression Data ◽  
Cell Gene Expression ◽  
Cell Data ◽  
Cell Gene

Single-cell gene expression data provide invaluable resources for systematic characterization of cellular hierarchy in multi-cellular organisms. However, cell lineage reconstruction is still often associated with significant uncertainty due to technological constraints. Such uncertainties have not been taken into account in current methods. We present ECLAIR, a novel computational method for the statistical inference of cell lineage relationships from single-cell gene expression data. ECLAIR uses an ensemble approach to improve the robustness of lineage predictions, and provides a quantitative estimate of the uncertainty of lineage branchings. We show that the application of ECLAIR to published datasets successfully reconstructs known lineage relationships and significantly improves the robustness of predictions. In conclusion, ECLAIR is a powerful bioinformatics tool for single-cell data analysis. It can be used for robust lineage reconstruction with quantitative estimate of prediction accuracy.

In silico drug repositioning for the treatment of Alzheimer's disease using molecular docking and gene expression data

RSC Advances ◽  
2016 ◽  
Vol 6 (100) ◽  
pp. 98080-98090 ◽  
Author(s):  
Hongbo Xie ◽  
Haixia Wen ◽  
Mingze Qin ◽  
Jie Xia ◽  
Denan Zhang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Molecular Docking ◽  
Gene Expression Data ◽  
In Silico ◽  
Drug Repositioning ◽  
Expression Data

We provided a computational drug repositioning method for the treatment of Alzheimer's disease.

scholarly journals A Novel Biomarker Identification Approach for Gastric Cancer Using Gene Expression and DNA Methylation Dataset

2021 ◽  
Vol 12 ◽  
Author(s):  
Ge Zhang ◽  
Zijing Xue ◽  
Chaokun Yan ◽  
Jianlin Wang ◽  
Huimin Luo
Keyword(s):  
Gene Expression ◽  
Gastric Cancer ◽  
Dna Methylation ◽  
Feature Selection ◽  
Gene Expression Data ◽  
Complex Disease ◽  
Biological Data ◽  
Computational Method ◽  
Superior Performance ◽  
Expression Data

As one type of complex disease, gastric cancer has high mortality rate, and there are few effective treatments for patients in advanced stage. With the development of biological technology, a large amount of multiple-omics data of gastric cancer are generated, which enables computational method to discover potential biomarkers of gastric cancer. That will be very important to detect gastric cancer at earlier stages and thus assist in providing timely treatment. However, most of biological data have the characteristics of high dimension and low sample size. It is hard to process directly without feature selection. Besides, only using some omic data, such as gene expression data, provides limited evidence to investigate gastric cancer associated biomarkers. In this research, gene expression data and DNA methylation data are integrated to analyze gastric cancer, and a feature selection approach is proposed to identify the possible biomarkers of gastric cancer. After the original data are pre-processed, the mutual information (MI) is applied to select some top genes. Then, fold change (FC) and T-test are adopted to identify differentially expressed genes (DEG). In particular, false discover rate (FDR) is introduced to revise p_value to further screen genes. For chosen genes, a deep neural network (DNN) model is utilized as the classifier to measure the quality of classification. The experimental results show that the approach can achieve superior performance in terms of accuracy and other metrics. Biological analysis for chosen genes further validates the effectiveness of the approach.

Drug repositioning based on gene expression data for human HER2-positive breast cancer

2021 ◽  
pp. 109043
Author(s):  
Farkhondeh Khanjani ◽  
Leila Jafari ◽  
Somayeh Azadiyan ◽  
Sahar Roozbehi ◽  
Cobra Moradian ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Gene Expression Data ◽  
Drug Repositioning ◽  
Expression Data ◽  
Her2 Positive ◽  
Her2 Positive Breast Cancer ◽  
Positive Breast Cancer

scholarly journals Minor Isozymes Tailor Yeast Metabolism to Carbon Availability

mSystems ◽  
2019 ◽  
Vol 4 (1) ◽  
Author(s):  
Patrick H. Bradley ◽  
Patrick A. Gibney ◽  
David Botstein ◽  
Olga G. Troyanskaya ◽  
Joshua D. Rabinowitz
Keyword(s):  
Gene Expression ◽  
Pyruvate Kinase ◽  
Gene Expression Data ◽  
Protein Function ◽  
Metabolic Regulation ◽  
Computational Method ◽  
Expression Data ◽  
Duplicated Genes ◽  
Carbon Availability ◽  
Central Carbon

ABSTRACTIsozymes are enzymes that differ in sequence but catalyze the same chemical reactions. Despite their apparent redundancy, isozymes are often retained over evolutionary time, suggesting that they contribute to fitness. We developed an unsupervised computational method for identifying environmental conditions under which isozymes are likely to make fitness contributions. This method analyzes published gene expression data to find specific experimental perturbations that induce differential isozyme expression. In yeast, we found that isozymes are strongly enriched in the pathways of central carbon metabolism and that many isozyme pairs show anticorrelated expression during the respirofermentative shift. Building on these observations, we assigned function to two minor central carbon isozymes, aconitase 2 (ACO2) and pyruvate kinase 2 (PYK2).ACO2is expressed during fermentation and proves advantageous when glucose is limiting.PYK2is expressed during respiration and proves advantageous for growth on three-carbon substrates.PYK2’s deletion can be rescued by expressing the major pyruvate kinase only if that enzyme carries mutations mirroringPYK2’s allosteric regulation. Thus, central carbon isozymes help to optimize allosteric metabolic regulation under a broad range of potential nutrient conditions while requiring only a small number of transcriptional states.IMPORTANCEGene duplication is one of the main evolutionary paths to new protein function. Typically, duplicated genes either accumulate mutations and degrade into pseudogenes or are retained and diverge in function. Some duplicated genes, however, show long-term persistence without apparently acquiring new function. An important class of isozymes consists of those that catalyze the same reaction in the same compartment, where knockout of one isozyme causes no known functional defect. Here we present an approach to assigning specific functional roles to seemingly redundant isozymes. First, gene expression data are analyzed computationally to identify conditions under which isozyme expression diverges. Then, knockouts are compared under those conditions. This approach revealed that the expression of many yeast isozymes diverges in response to carbon availability and that carbon source manipulations can induce fitness phenotypes for seemingly redundant isozymes. A driver of these fitness phenotypes is differential allosteric enzyme regulation, indicating isozyme divergence to achieve more-optimal control of metabolism.

scholarly journals Drug Repositioning Ketamine as a New Treatment for Bipolar Disorder Using Text Mining

BioChem ◽  
2021 ◽  
Vol 2 (1) ◽  
pp. 1-7
Author(s):  
Shivani Manikandan ◽  
Suchir Misra ◽  
Serena McCalla
Keyword(s):  
Gene Expression ◽  
Bipolar Disorder ◽  
Text Mining ◽  
Gene Expression Data ◽  
Drug Repositioning ◽  
Chronic Mental Illness ◽  
Expression Data ◽  
Main Gene ◽  
New Treatment ◽  
The Brain

Bipolar Disorder (BD), a chronic mental illness, does not have an ideal treatment, and patients with BD have a higher chance of being diagnosed with alcohol abuse, liver disease, and diabetes. The goal of treatment is to prevent a relapse in BD episodes and find a new treatment. The research here looks at the genetics of BD and ignores environmental factors, as they are subjective. Therapy treats known environmental triggers and stressors and explores methods to reduce them. However, therapy alone cannot fully alleviate the symptoms of BD. My research employs text-mining as a primary strategy to obtain relevant genes and drugs pertaining to BD. The main gene involved is the Brain-Derived Neurotrophic Factor (BDNF). Popular drugs currently used for treatment of BD are Lithium and Carbamazepine. Using CMapPy to look at gene expression data, one sees a relationship between the two drug therapies and BDNF. Lithium fails to treat mania and Carbamazepine fails to treat depression, relatively speaking. When comparing gene expression data of Lithium and Carbamazepine with Ketamine, a newer therapy for BD, Ketamine, raises the BDNF level, keeps it elevated, and effectively controls BD episodes. Ketamine does not have the shortcomings that Lithium and Carbamazepine have. Next steps would include conducting a clinical trial with the hopeful application of Ketamine as a new treatment for BD.

A computational method using differential gene expression to predict altered metabolism of multicellular organisms

2017 ◽  
Vol 13 (11) ◽  
pp. 2418-2427 ◽  
Author(s):  
Lvxing Zhu ◽  
Haoran Zheng ◽  
Xinying Hu ◽  
Yang Xu
Keyword(s):  
Gene Expression ◽  
Differential Gene Expression ◽  
Gene Expression Data ◽  
Differential Method ◽  
Computational Approach ◽  
Computational Method ◽  
Expression Data ◽  
Differential Gene ◽  
Altered Metabolism ◽  
Multicellular Organisms

The differential method provides a computational approach to predict altered metabolism between pairs of conditions by integrating gene expression data.

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