Bayesian State Space Models for Inferring and Predicting Temporal Gene Expression Profiles

2007 ◽  
Vol 49 (6) ◽  
pp. 801-814 ◽  
Author(s):  
Yulan Liang ◽  
Arpad Kelemen
Keyword(s):  
Gene Expression ◽  
State Space ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
State Space Models ◽  
Temporal Gene Expression

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

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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