AIMC Topic: Arabidopsis
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EDeepSSP: Explainable deep neural networks for exact splice sites prediction.
Journal of bioinformatics and computational biology
Jul 22, 2020
Splice site prediction is crucial for understanding underlying gene regulation, gene function for better genome annotation. Many computational methods exist for recognizing the splice sites. Although most of the methods achieve a competent performanc...
Ab initio GO-based mining for non-tandem-duplicated functional clusters in three model plant diploid genomes.
PloS one
Jun 19, 2020
A functional Non-Tandem Duplicated Cluster (FNTDC) is a group of non-tandem-duplicated genes that are located closer than expected by mere chance and have a role in the same biological function. The identification of secondary-compounds-related FNTDC...
SemanticGO: a tool for gene functional similarity analysis in Arabidopsis thaliana and rice.
Plant science : an international journal of experimental plant biology
May 23, 2020
Gene or pathway functional similarities are important information for researchers. However, these similarities are often described sparsely and qualitatively. The latent semantic analysis of Arabidopsis thaliana (Arabidopsis) Gene Ontology (GO) data ...
Multi-feature fusion for deep learning to predict plant lncRNA-protein interaction.
Genomics
May 11, 2020
Long non-coding RNAs (lncRNAs) play key roles in regulating cellular biological processes through diverse molecular mechanisms including binding to RNA binding proteins. The majority of plant lncRNAs are functionally uncharacterized, thus, accurate p...
GOMCL: a toolkit to cluster, evaluate, and extract non-redundant associations of Gene Ontology-based functions.
BMC bioinformatics
Apr 10, 2020
BACKGROUND: Functional enrichment of genes and pathways based on Gene Ontology (GO) has been widely used to describe the results of various -omics analyses. GO terms statistically overrepresented within a set of a large number of genes are typically ...
DDBJ Data Analysis Challenge: a machine learning competition to predict Arabidopsis chromatin feature annotations from DNA sequences.
Genes & genetic systems
Mar 26, 2020
Recently, the prospect of applying machine learning tools for automating the process of annotation analysis of large-scale sequences from next-generation sequencers has raised the interest of researchers. However, finding research collaborators with ...
Machine learning improves our knowledge about miRNA functions towards plant abiotic stresses.
Scientific reports
Feb 20, 2020
During the last two decades, human has increased his knowledge about the role of miRNAs and their target genes in plant stress response. Biotic and abiotic stresses result in simultaneous tissue-specific up/down-regulation of several miRNAs. In this ...
StressGenePred: a twin prediction model architecture for classifying the stress types of samples and discovering stress-related genes in arabidopsis.
BMC genomics
Dec 20, 2019
BACKGROUND: Recently, a number of studies have been conducted to investigate how plants respond to stress at the cellular molecular level by measuring gene expression profiles over time. As a result, a set of time-series gene expression data for the ...
TSLRF: Two-Stage Algorithm Based on Least Angle Regression and Random Forest in genome-wide association studies.
Scientific reports
Dec 2, 2019
One of the most important tasks in genome-wide association analysis (GWAS) is the detection of single-nucleotide polymorphisms (SNPs) which are related to target traits. With the development of sequencing technology, traditional statistical methods a...
DeepTetrad: high-throughput image analysis of meiotic tetrads by deep learning in Arabidopsis thaliana.
The Plant journal : for cell and molecular biology
Oct 22, 2019
Meiotic crossovers facilitate chromosome segregation and create new combinations of alleles in gametes. Crossover frequency varies along chromosomes and crossover interference limits the coincidence of closely spaced crossovers. Crossovers can be mea...
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