AIMC Topic: Saccharomyces cerevisiae
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GRNUlar: A Deep Learning Framework for Recovering Single-Cell Gene Regulatory Networks.
Journal of computational biology : a journal of computational molecular cell biology
Jan 1, 2022
We propose GRNUlar, a novel deep learning framework for supervised learning of gene regulatory networks (GRNs) from single-cell RNA-Sequencing (scRNA-Seq) data. Our framework incorporates two intertwined models. First, we leverage the expressive abil...
AI-based forecasting of ethanol fermentation using yeast morphological data.
Bioscience, biotechnology, and biochemistry
Dec 22, 2021
Several industries require getting information of products as soon as possible during fermentation. However, the trade-off between sensing speed and data quantity presents challenges for forecasting fermentation product yields. In this study, we trie...
A survey on computational models for predicting protein-protein interactions.
Briefings in bioinformatics
Sep 2, 2021
Proteins interact with each other to play critical roles in many biological processes in cells. Although promising, laboratory experiments usually suffer from the disadvantages of being time-consuming and labor-intensive. The results obtained are oft...
Bound2Learn: a machine learning approach for classification of DNA-bound proteins from single-molecule tracking experiments.
Nucleic acids research
Aug 20, 2021
DNA-bound proteins are essential elements for the maintenance, regulation, and use of the genome. The time they spend bound to DNA provides useful information on their stability within protein complexes and insight into the understanding of biologica...
The Gene Ontology resource: enriching a GOld mine.
Nucleic acids research
Jan 8, 2021
The Gene Ontology Consortium (GOC) provides the most comprehensive resource currently available for computable knowledge regarding the functions of genes and gene products. Here, we report the advances of the consortium over the past two years. The n...
m6A-Atlas: a comprehensive knowledgebase for unraveling the N6-methyladenosine (m6A) epitranscriptome.
Nucleic acids research
Jan 8, 2021
N 6-Methyladenosine (m6A) is the most prevalent RNA modification on mRNAs and lncRNAs. It plays a pivotal role during various biological processes and disease pathogenesis. We present here a comprehensive knowledgebase, m6A-Atlas, for unraveling the ...
[Identification of nucleosome positioning using support vector machine method based on comprehensive DNA sequence feature].
Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi
Jun 25, 2020
In this article, based on z-curve theory and position weight matrix (PWM), a model for nucleosome sequences was constructed. Nucleosome sequence dataset was transformed into three-dimensional coordinates, PWM of the nucleosome sequences was calculate...
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...
DeepMRMP: A new predictor for multiple types of RNA modification sites using deep learning.
Mathematical biosciences and engineering : MBE
Jul 4, 2019
RNA modification plays an indispensable role in the regulation of organisms. RNA modification site prediction offers an insight into diverse cellular processing. Regarding different types of RNA modification site prediction, it is difficult to tell t...
Onto2Vec: joint vector-based representation of biological entities and their ontology-based annotations.
Bioinformatics (Oxford, England)
Jul 1, 2018
MOTIVATION: Biological knowledge is widely represented in the form of ontology-based annotations: ontologies describe the phenomena assumed to exist within a domain, and the annotations associate a (kind of) biological entity with a set of phenomena ...
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