Machine Learning


deepEA a containerized web server for interactive analysis of epitranscriptome sequencing data

deepEA: a containerized web server for interactive analysis of epitranscriptome sequencing data

RNA molecules are decorated with a variety of chemical modifications, which make up an epitranscriptome that extensively regulates gene expression and biological processes. With the recent development of high-throughput experimental technologies, …


A bioinformatics framework for comprehensive assembly and functional annotation

CAFU: a Galaxy framework for exploring unmapped RNA-Seq data

A widely used approach in transcriptome analysis is the alignment of short reads to a reference genome. However, owing to the deficiencies of specially designed analytical systems, short reads unmapped to the genome sequence are usually ignored, …

miRLocator: A Python Implementation and Web Server for Predicting miRNAs from Pre-miRNA Sequences

microRNAs (miRNAs) are short, noncoding regulatory RNAs derived from hairpin precursors (pre-miRNAs). In synergy with experimental approaches, computational approaches have become an invaluable tool for identifying miRNAs at the genome scale. We have …

A deep convolutional neural network approach for predicting phenotypes from genotypes

Deep learning is a promising technology to accurately select individuals with high phenotypic values based on genotypic data. Genomic selection (GS) is a promising breeding strategy by which the phenotypes of plant individuals are usually predicted …


Deep learning-based genomic selection


Bioinformatics approaches for epitranscriptome analysis and their applications


Machine learning-based m5C prediction

Transcriptome-Wide Annotation of m5C RNA Modifications Using Machine Learning

The emergence of epitranscriptome opened a new chapter in gene regulation. 5-methylcytosine (m5C), as an important post-transcriptional modification, has been identified to be involved in a variety of biological processes such as subcellular …

A Meta-Analysis Based Method for Prioritizing Candidate Genes Involved in a Pre-specific Function

The identification of genes associated with a given biological function in plants remains a challenge, although network-based gene prioritization algorithms have been developed for Arabidopsis thaliana and many non-model plant species. Nevertheless, …


Gene prioritization for a pre-specific function

Application of Machine Learning-Based Classification to Genomic Selection and Performance Improvement

Genomic selection (GS) is a novel breeding strategy that selects individuals with high breeding value using computer programs. Although GS has long been practiced in the field of animal breeding, its application is still challenging in crops with …


mature miRNAs predictions from pre-miRNA sequences

miRLocator: Machine Learning-Based Prediction of Mature MicroRNAs within Plant Pre-miRNA Sequences

MicroRNAs (miRNAs) are a class of short, non-coding RNA that play regulatory roles in a wide variety of biological processes, such as plant growth and abiotic stress responses. Although several computational tools have been developed to identify …


Artificial intelligence (AI)-based Big-data analysis approaches and their applications

Machine Learning for Big Data Analytics in Plants

Rapid advances in high-throughput genomic technology have enabled biology to enter the era of 'Big Data' (large datasets). The plant science community not only needs to build its own Big-Data-compatible parallel computing and data management …