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Genome, Fungal

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Machine Learning of Protein Interactions in Fungal Secretory Pathways.

PloS one
In this paper we apply machine learning methods for predicting protein interactions in fungal secretion pathways. We assume an inter-species transfer setting, where training data is obtained from a single species and the objective is to predict prote...

Identification of New Fungal Peroxisomal Matrix Proteins and Revision of the PTS1 Consensus.

Traffic (Copenhagen, Denmark)
The peroxisomal targeting signal type 1 (PTS1) is a seemingly simple peptide sequence at the C-terminal end of most peroxisomal matrix proteins. PTS1 can be described as a tripeptide with the consensus motif [S/A/C] [K/R/H] L. However, this descripti...

Victors: a web-based knowledge base of virulence factors in human and animal pathogens.

Nucleic acids research
Virulence factors (VFs) are molecules that allow microbial pathogens to overcome host defense mechanisms and cause disease in a host. It is critical to study VFs for better understanding microbial pathogenesis and host defense mechanisms. Victors (ht...

The pan-genome of Saccharomyces cerevisiae.

FEMS yeast research
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...

iMethyl-Deep: N6 Methyladenosine Identification of Yeast Genome with Automatic Feature Extraction Technique by Using Deep Learning Algorithm.

Genes
One of the most common and well studied post-transcription modifications in RNAs is N6-methyladenosine (m6A) which has been involved with a wide range of biological processes. Over the past decades, N6-methyladenosine produced some positive consequen...

Term Matrix: a novel Gene Ontology annotation quality control system based on ontology term co-annotation patterns.

Open biology
Biological processes are accomplished by the coordinated action of gene products. Gene products often participate in multiple processes, and can therefore be annotated to multiple Gene Ontology (GO) terms. Nevertheless, processes that are functionall...

Computational approach for identification, characterization, three-dimensional structure modelling and machine learning-based thermostability prediction of xylanases from the genome of Aspergillus fumigatus.

Computational biology and chemistry
Identification of thermostable and alkaline xylanases from different fungal and bacterial species have gained an interest for the researchers because of its biotechnological relevance in many industries, such as pulp, paper, and bioethanol. In this s...

Machine learning enables identification of an alternative yeast galactose utilization pathway.

Proceedings of the National Academy of Sciences of the United States of America
How genomic differences contribute to phenotypic differences is a major question in biology. The recently characterized genomes, isolation environments, and qualitative patterns of growth on 122 sources and conditions of 1,154 strains from 1,049 fung...

Construction of an enzyme-constrained metabolic network model for Myceliophthora thermophila using machine learning-based k data.

Microbial cell factories
BACKGROUND: Genome-scale metabolic models (GEMs) serve as effective tools for understanding cellular phenotypes and predicting engineering targets in the development of industrial strain. Enzyme-constrained genome-scale metabolic models (ecGEMs) have...

Utilizing Deep Neural Networks to Fill Gaps in Small Genomes.

International journal of molecular sciences
With the widespread adoption of next-generation sequencing technologies, the speed and convenience of genome sequencing have significantly improved, and many biological genomes have been sequenced. However, during the assembly of small genomes, we st...