AIMC Topic: Gene Expression Regulation, Fungal

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The pan-genome of Saccharomyces cerevisiae.

FEMS yeast research Nov 1, 2019
Understanding genotype-phenotype relationship is fundamental in biology. With the benefit from next-generation sequencing and high-throughput phenotyping methodologies, there have been generated much genome and phenome data for Saccharomyces cerevisi...
Machine Learning Phenotype Genotype High-Throughput Nucleotide Sequencing Saccharomyces cerevisiae Gene Expression Regulation, Fungal Genome, Fungal
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Optimization of the Production of Extracellular Polysaccharide from the Shiitake Medicinal Mushroom Lentinus edodes (Agaricomycetes) Using Mutation and a Genetic Algorithm-Coupled Artificial Neural Network (GA-ANN).

International journal of medicinal mushrooms Jan 1, 2016
Exopolysaccharide (EPS) production by a strain of Lentinus edodes was studied via the effects of treatments with ultraviolet (UV) irradiation and acridine orange. Furthermore, optimization of EPS production was studied using a genetic algorithm coupl...
Neural Networks, Computer Mutation Biphenyl Compounds Picrates Free Radical Scavengers Gene Expression Regulation, Fungal Fungal Polysaccharides Shiitake Mushrooms
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Semi-supervised prediction of gene regulatory networks using machine learning algorithms.

Journal of biosciences Oct 1, 2015
Use of computational methods to predict gene regulatory networks (GRNs) from gene expression data is a challenging task. Many studies have been conducted using unsupervised methods to fulfill the task; however, such methods usually yield low predicti...
Gene Regulatory Networks Computational Biology Transcription Factors Support Vector Machine Escherichia coli Datasets as Topic Escherichia coli Proteins Saccharomyces cerevisiae Gene Expression Regulation, Bacterial Gene Expression Regulation, Fungal Saccharomyces cerevisiae Proteins
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