AIMC Topic: Chromosome Mapping

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Optical topometry and machine learning to rapidly phenotype stomatal patterning traits for maize QTL mapping.

Plant physiology
Stomata are adjustable pores on leaf surfaces that regulate the tradeoff of CO2 uptake with water vapor loss, thus having critical roles in controlling photosynthetic carbon gain and plant water use. The lack of easy, rapid methods for phenotyping ep...

Machine Learning to Identify Gene Interactions from High-Throughput Mutant Crosses.

Methods in molecular biology (Clifton, N.J.)
Advances in molecular genetics through high-throughput gene mutagenesis and genetic crossing have enabled gene interaction mapping across whole genomes. Detecting gene interactions in even small microbial genomes relies on measuring growth phenotypes...

UniRule: a unified rule resource for automatic annotation in the UniProt Knowledgebase.

Bioinformatics (Oxford, England)
MOTIVATION: The number of protein records in the UniProt Knowledgebase (UniProtKB: https://www.uniprot.org) continues to grow rapidly as a result of genome sequencing and the prediction of protein-coding genes. Providing functional annotation for the...

Statistical and Machine Learning Methods for eQTL Analysis.

Methods in molecular biology (Clifton, N.J.)
An immense amount of observable diversity exists for all traits and across global populations. In the post-genomic era, equipped with efficient sequencing capabilities and better genotyping methods, we are now able to more fully appreciate how regula...

Design of Knowledge Bases for Plant Gene Regulatory Networks.

Methods in molecular biology (Clifton, N.J.)
Developing a knowledge base that contains all the information necessary for the researcher studying gene regulation in a particular organism can be accomplished in four stages. This begins with defining the data scope. We describe here the necessary ...

Genome-wide discovery of miRNAs using ensembles of machine learning algorithms and logistic regression.

International journal of data mining and bioinformatics
In silico prediction of novel miRNAs from genomic sequences remains a challenging problem. This study presents a genome-wide miRNA discovery software package called GenoScan and evaluates two hairpin classification methods. These methods, one ensembl...