Analysis of temporal gene expression profiles using time-dependent MUSIC algorithm

2009 ◽  
Author(s):  
Nidhal Bouaynaya ◽  
Dan Schonfeld ◽  
Radhakrishnan Nagarajan
Keyword(s):  
Gene Expression ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Time Dependent ◽  
Music Algorithm ◽  
Temporal Gene Expression

scholarly journals Time Dependent Gene Expression Changes in the Liver of Mice Treated with Benzene

2008 ◽  
Vol 3 ◽  
pp. BMI.S590 ◽  
Author(s):  
Han-Jin Park ◽  
Jung Hwa Oh ◽  
Seokjoo Yoon ◽  
S.V.S. Rana
Keyword(s):  
Gene Expression ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
General Purpose ◽  
Time Dependent ◽  
Expression Data ◽  
Benzene Exposure ◽  
Microarray Analyses ◽  
First Time ◽  
Affymetrix Gene Chip

Benzene is used as a general purpose solvent. Benzene metabolism starts from phenol and ends with p-benzoquinone and o-benzoquinone. Liver injury inducted by benzene still remains a toxicologic problem. Tumor related genes and immune responsive genes have been studied in patients suffering from benzene exposure. However, gene expression profiles and pathways related to its hepatotoxicity are not known. This study reports the results obtained in the liver of BALB/C mice (SLC, Inc., Japan) administered 0.05 ml/100 g body weight of 2% benzene for six days. Serum, ALT, AST and ALP were determined using automated analyzer (Fuji., Japan). Histopathological observations were made to support gene expression data. c-DNA microarray analyses were performed using Affymetrix Gene-chip system. After six days of benzene exposure, twenty five genes were down regulated whereas nineteen genes were up-regulated. These gene expression changes were found to be related to pathways of biotransformation, detoxification, apoptosis, oxidative stress and cell cycle. It has been shown for the first time that genes corresponding to circadian rhythms are affected by benzene. Results suggest that gene expression profile might serve as potential biomarkers of hepatotoxicity during benzene exposure.

scholarly journals Temporal Gene Expression Profiles after Focal Cerebral Ischemia in Mice

2018 ◽  
Vol 9 (2) ◽  
pp. 249 ◽  
Author(s):  
Chengjie Zhang ◽  
Yanbing Zhu ◽  
Song Wang ◽  
Zheng Zachory Wei ◽  
Michael Qize Jiang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Cerebral Ischemia ◽  
Focal Cerebral Ischemia ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Temporal Gene Expression

Photo-caged agonists of the nuclear receptors RARγ and TRβ provide unique time-dependent gene expression profiles for light-activated gene patterning

2004 ◽  
Vol 12 (22) ◽  
pp. 5949-5959 ◽  
Author(s):  
Kristian H. Link ◽  
Federico G. Cruz ◽  
Hai-Fen Ye ◽  
Kathryn E. O’Reilly ◽  
Sarah Dowdell ◽  
...  
Keyword(s):  
Gene Expression ◽  
Nuclear Receptors ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Time Dependent ◽  
Gene Patterning

Abstract P744: Gene Transcript Clusters Distinguish Time-Dependent Expression Patterns in Monocytes, Neutrophils and Whole Blood After Ischemic Stroke Injury

Stroke ◽  
2021 ◽  
Vol 52 (Suppl_1) ◽  
Author(s):  
Paulina Carmona-Mora ◽  
Glen C Jickling ◽  
Xinhua Zhan ◽  
Marisa Hakoupian ◽  
Heather Hull ◽  
...  
Keyword(s):  
Gene Expression ◽  
Ischemic Stroke ◽  
Whole Blood ◽  
Temporal Dynamics ◽  
Expression Profiles ◽  
Expression Patterns ◽  
Gene Expression Profiles ◽  
Time Dependent ◽  
Gene Transcript ◽  
Time Points

Introduction: After ischemic stroke (IS), peripheral leukocytes infiltrate the damaged region and modulate the response to injury. We previously showed that peripheral blood cells display different gene expression profiles after IS and these transcriptional programs reflect the changes in immune processes in response to IS. Dissecting the temporal dynamics of gene expression after IS improves our understanding of the changes of molecular and cellular pathways involved in acute brain injury. Methods: We analyzed the transcriptomic profiles of 33 IS patients in isolated monocytes, neutrophils and whole blood. RNA-sequencing was performed on all the stroke samples as well as 12 controls with vascular risk factors (diabetes and/or hypertension and/or hypercholesterolemia). To identify differentially expressed genes, subjects were split into time points (TPs) from stroke onset (TP1= 0-24 h; TP2= 24-48 h; and TP3= > 48 h), and controls were assigned TP0. A linear regression model including time and the interaction of diagnosis x TP with cutoff of p<0.02 and fold-change>|1.2| was used. Time dependent changes were analyzed using artificial neural networks to identify clusters of genes that behave in a similar way across TPs. Results: Unique patterns of temporal expression were distinguished for the three sample types. These include genes not expressed in TP0 that peak only within the first 24 h, others that peak or decrease in TP2 and TP3, and more complex patterns. Genes that peak at TP1 in monocytes and neutrophils are related to cell adhesion and leukocyte differentiation/migration, respectively. Early peaks in whole blood occur in genes related to transcriptional regulation. In monocytes, interleukin pathways are enriched across all TPs, whereas there is a trend of suppression after 24 h in neutrophils. The inflammasome pathway is enriched in the earlier TPs in neutrophils, while not enriched in monocytes until over 48 hours. Conclusion: Our analyses on gene expression dynamics and cluster patterns allow identification of key genes and pathways at different time points following ischemic injury that are valuable as IS biomarkers and may be possible treatment targets.

scholarly journals Global Gene Expression Responses to Cadmium Toxicity in Escherichia coli

2005 ◽  
Vol 187 (9) ◽  
pp. 3259-3266 ◽  
Author(s):  
Anyou Wang ◽  
David E. Crowley
Keyword(s):  
Gene Expression ◽  
Escherichia Coli ◽  
Ribosomal Proteins ◽  
Molecular Mechanisms ◽  
Expression Profiles ◽  
Cadmium Toxicity ◽  
Gene Expression Profiles ◽  
Global Gene Expression ◽  
Temporal Gene Expression ◽  
Genes Encoding

ABSTRACT Genome-wide analysis of temporal gene expression profiles in Escherichia coli following exposure to cadmium revealed a shift to anaerobic metabolism and induction of several stress response systems. Disruption in the transcription of genes encoding ribosomal proteins and zinc-binding proteins may partially explain the molecular mechanisms of cadmium toxicity.

scholarly journals A Novel Coupled Reaction-Diffusion System for Explainable Gene Expression Profiling

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 2190
Author(s):  
Muhamed Wael Farouq ◽  
Wadii Boulila ◽  
Zain Hussain ◽  
Asrar Rashid ◽  
Moiz Shah ◽  
...  
Keyword(s):  
Gene Expression ◽  
Clinical Decision Making ◽  
Expression Profiles ◽  
Mathematical Formulation ◽  
Gene Expression Profiles ◽  
Reaction Diffusion ◽  
Clinical Decision ◽  
Treatment Pathways ◽  
Temporal Gene Expression ◽  
Coupled Reaction

Machine learning (ML)-based algorithms are playing an important role in cancer diagnosis and are increasingly being used to aid clinical decision-making. However, these commonly operate as ‘black boxes’ and it is unclear how decisions are derived. Recently, techniques have been applied to help us understand how specific ML models work and explain the rational for outputs. This study aims to determine why a given type of cancer has a certain phenotypic characteristic. Cancer results in cellular dysregulation and a thorough consideration of cancer regulators is required. This would increase our understanding of the nature of the disease and help discover more effective diagnostic, prognostic, and treatment methods for a variety of cancer types and stages. Our study proposes a novel explainable analysis of potential biomarkers denoting tumorigenesis in non-small cell lung cancer. A number of these biomarkers are known to appear following various treatment pathways. An enhanced analysis is enabled through a novel mathematical formulation for the regulators of mRNA, the regulators of ncRNA, and the coupled mRNA–ncRNA regulators. Temporal gene expression profiles are approximated in a two-dimensional spatial domain for the transition states before converging to the stationary state, using a system comprised of coupled-reaction partial differential equations. Simulation experiments demonstrate that the proposed mathematical gene-expression profile represents a best fit for the population abundance of these oncogenes. In future, our proposed solution can lead to the development of alternative interpretable approaches, through the application of ML models to discover unknown dynamics in gene regulatory systems.

scholarly journals Machine learning algorithms for the identification of potential non-syndromic intellectual disability associated miRNAs

2021 ◽  
Author(s):  
Julián González Betancur ◽  
José Guevara-Coto ◽  
Adarli Romero
Keyword(s):  
Gene Expression ◽  
Machine Learning ◽  
Developmental Disorders ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Machine Learning Algorithms ◽  
Stochastic Gradient Descent ◽  
Temporal Gene Expression ◽  
Interesting Approach ◽  
Spatio Temporal

Abstract Background: Intellectual disabilities (IDs) are a group of developmental disorders with high phenotypic and genotypic heterogeneity. Association of genetic elements to IDs has typically been empirically accomplished, however recently, machine learning (ML) has proved to be an excellent instrument to elucidate these associations. miRNAs are short non-coding molecules that participate in spatiotemporal gene regulation, making them relevant for the understanding ID causality. Methods: In this study we used the BrainSpan spatio-temporal expression database to develop a series of machine learning predictors: SVM, RF, FF-ANN, and Stochastic Gradient Descent Classifier. These models were capable of recognizing gene expression profiles. The best classifier was used to label miRNAs associated with NS-IDs using the BrainSpan expression profiles. Results: The model with the best performance was a FF-ANN with 0.78 of F1-score, 0.78 of weighted recall and 0.78 of weighted precision. We used this model to identify miRNAs with high probability to be associated with NS-IDs using the spatio-temporal gene expression profile in the human brain. Labeled miRNAs that were annotated were associated with processes related to either IDs and-or neurodevelopmental processes. Conclusions: The development of a machine learning framework that identified potential NS-ID miRNAs represents an interesting approach for the identification of a potential list of on genes that could be subject for further experimental validation. This study also reinforces the potential of machine learning frameworks in their discovery of potential biomarkers that could improve disease detection and management.

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