AIMC Topic: Arabidopsis
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A Deep Learning-Based Approach for High-Throughput Hypocotyl Phenotyping.
Plant physiology
Oct 21, 2019
Hypocotyl length determination is a widely used method to phenotype young seedlings. The measurement itself has advanced from using rulers and millimeter papers to assessing digitized images but remains a labor-intensive, monotonous, and time-consumi...
QTG-Finder: A Machine-Learning Based Algorithm To Prioritize Causal Genes of Quantitative Trait Loci in Arabidopsis and Rice.
G3 (Bethesda, Md.)
Oct 7, 2019
Linkage mapping is one of the most commonly used methods to identify genetic loci that determine a trait. However, the loci identified by linkage mapping may contain hundreds of candidate genes and require a time-consuming and labor-intensive fine ma...
Enabling full-length evolutionary profiles based deep convolutional neural network for predicting DNA-binding proteins from sequence.
Proteins
Jul 8, 2019
Sequence based DNA-binding protein (DBP) prediction is a widely studied biological problem. Sliding windows on position specific substitution matrices (PSSMs) rows predict DNA-binding residues well on known DBPs but the same models cannot be applied ...
Predicting miRNA-lncRNA interactions and recognizing their regulatory roles in stress response of plants.
Mathematical biosciences
Apr 26, 2019
It has been found that each non-coding RNA (ncRNA) can act not only through its target gene, but also interact with each other to act on biological traits, and this interaction is more common. Many studies focus mainly on the analysis of microRNA(miR...
Deep learning for DNase I hypersensitive sites identification.
BMC genomics
Dec 31, 2018
BACKGROUND: The DNase I hypersensitive sites (DHSs) are associated with the cis-regulatory DNA elements. An efficient method of identifying DHSs can enhance the understanding on the accessibility of chromatin. Despite a multitude of resources availab...
NMFGO: Gene Function Prediction via Nonnegative Matrix Factorization with Gene Ontology.
IEEE/ACM transactions on computational biology and bioinformatics
Jul 30, 2018
Gene Ontology (GO) is a controlled vocabulary of terms that describe molecule function, biological roles, and cellular locations of gene products (i.e., proteins and RNAs), it hierarchically organizes more than 43,000 GO terms via the direct acyclic ...
RNA-seq assistant: machine learning based methods to identify more transcriptional regulated genes.
BMC genomics
Jul 20, 2018
BACKGROUND: Although different quality controls have been applied at different stages of the sample preparation and data analysis to ensure both reproducibility and reliability of RNA-seq results, there are still limitations and bias on the detectabi...
Phenotype Extraction Based on Word Embedding to Sentence Embedding Cascaded Approach.
IEEE transactions on nanobioscience
May 18, 2018
As a significant determinant in the development of named entity recognition, phenotypic descriptions are normally presented differently in biomedical literature with the use of complicated semantics. In this paper, a novel approach has been proposed ...
Consistent prediction of GO protein localization.
Scientific reports
May 17, 2018
The GO-Cellular Component (GO-CC) ontology provides a controlled vocabulary for the consistent description of the subcellular compartments or macromolecular complexes where proteins may act. Current machine learning-based methods used for the automat...
Optimizing the Use of a Liquid Handling Robot to Conduct a High Throughput Forward Chemical Genetics Screen of Arabidopsis thaliana.
Journal of visualized experiments : JoVE
Apr 30, 2018
Chemical genetics is increasingly being employed to decode traits in plants that may be recalcitrant to traditional genetics due to gene redundancy or lethality. However, the probability of a synthetic small molecule being bioactive is low; therefore...
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