AIMC Topic: Genome, Plant
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Evolutionarily informed machine learning enhances the power of predictive gene-to-phenotype relationships.
Nature communications
Sep 24, 2021
Inferring phenotypic outcomes from genomic features is both a promise and challenge for systems biology. Using gene expression data to predict phenotypic outcomes, and functionally validating the genes with predictive powers are two challenges we add...
Multitrait machine- and deep-learning models for genomic selection using spectral information in a wheat breeding program.
The plant genome
Sep 5, 2021
Prediction of breeding values is central to plant breeding and has been revolutionized by the adoption of genomic selection (GS). Use of machine- and deep-learning algorithms applied to complex traits in plants can improve prediction accuracies. Beca...
iDNA6mA-Rice-DL: A local web server for identifying DNA N6-methyladenine sites in rice genome by deep learning method.
Journal of bioinformatics and computational biology
Jul 21, 2021
Accurate detection of N6-methyladenine (6mA) sites by biochemical experiments will help to reveal their biological functions, still, these wet experiments are laborious and expensive. Therefore, it is necessary to introduce a powerful computational m...
A convolution based computational approach towards DNA N6-methyladenine site identification and motif extraction in rice genome.
Scientific reports
May 14, 2021
DNA N6-methylation (6mA) in Adenine nucleotide is a post replication modification responsible for many biological functions. Automated and accurate computational methods can help to identify 6mA sites in long genomes saving significant time and money...
iPTT(2 L)-CNN: A Two-Layer Predictor for Identifying Promoters and Their Types in Plant Genomes by Convolutional Neural Network.
Computational and mathematical methods in medicine
Jan 5, 2021
A promoter is a short DNA sequence near to the start codon, responsible for initiating transcription of a specific gene in genome. The accurate recognition of promoters has great significance for a better understanding of the transcriptional regulati...
Machine learning approaches for crop improvement: Leveraging phenotypic and genotypic big data.
Journal of plant physiology
Dec 29, 2020
Highly efficient and accurate selection of elite genotypes can lead to dramatic shortening of the breeding cycle in major crops relevant for sustaining present demands for food, feed, and fuel. In contrast to classical approaches that emphasize the n...
Machine learning approaches reveal genomic regions associated with sugarcane brown rust resistance.
Scientific reports
Nov 18, 2020
Sugarcane is an economically important crop, but its genomic complexity has hindered advances in molecular approaches for genetic breeding. New cultivars are released based on the identification of interesting traits, and for sugarcane, brown rust re...
DNC4mC-Deep: Identification and Analysis of DNA N4-Methylcytosine Sites Based on Different Encoding Schemes By Using Deep Learning.
Cells
Jul 22, 2020
N4-methylcytosine as one kind of modification of DNA has a critical role which alters genetic performance such as protein interactions, conformation, stability in DNA as well as the regulation of gene expression same cell developmental and genomic im...
SOMmelier-Intuitive Visualization of the Topology of Grapevine Genome Landscapes Using Artificial Neural Networks.
Genes
Jul 17, 2020
BACKGROUND: Whole-genome studies of vine cultivars have brought novel knowledge about the diversity, geographical relatedness, historical origin and dissemination, phenotype associations and genetic markers.
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...
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