AIMC Topic: MicroRNAs

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Predicting miRNA-lncRNA interactions and recognizing their regulatory roles in stress response of plants.

Mathematical biosciences
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...

Exon level machine learning analyses elucidate novel candidate miRNA targets in an avian model of fetal alcohol spectrum disorder.

PLoS computational biology
Gestational alcohol exposure causes fetal alcohol spectrum disorder (FASD) and is a prominent cause of neurodevelopmental disability. Whole transcriptome sequencing (RNA-Seq) offer insights into mechanisms underlying FASD, but gene-level analysis pro...

Multilayer network analysis of miRNA and protein expression profiles in breast cancer patients.

PloS one
MiRNAs and proteins play important roles in different stages of breast tumor development and serve as biomarkers for the early diagnosis of breast cancer. A new algorithm that combines machine learning algorithms and multilayer complex network analys...

Adaptive multi-view multi-label learning for identifying disease-associated candidate miRNAs.

PLoS computational biology
Increasing evidence has indicated that microRNAs(miRNAs) play vital roles in various pathological processes and thus are closely related with many complex human diseases. The identification of potential disease-related miRNAs offers new opportunities...

PMAMCA: prediction of microRNA-disease association utilizing a matrix completion approach.

BMC systems biology
BACKGROUND: Numerous experimental results have indicated that microRNAs (miRNAs) play a vital role in biological processes, as well as outbreaks of diseases at the molecular level. Despite their important role in biological processes, knowledge regar...

Using Machine Learning to Predict Sensorineural Hearing Loss Based on Perilymph Micro RNA Expression Profile.

Scientific reports
Hearing loss (HL) is the most common neurodegenerative disease worldwide. Despite its prevalence, clinical testing does not yield a cell or molecular based identification of the underlying etiology of hearing loss making development of pharmacologica...

Significant improvement of miRNA target prediction accuracy in large datasets using meta-strategy based on comprehensive voting and artificial neural networks.

BMC genomics
BACKGROUND: Identifying mRNA targets of miRNAs is critical for studying gene expression regulation at the whole-genome level. Multiple computational tools have been developed to predict miRNA:mRNA interactions. Nonetheless, many of these tools are de...

Adaboost-SVM-based probability algorithm for the prediction of all mature miRNA sites based on structured-sequence features.

Scientific reports
The significant role of microRNAs (miRNAs) in various biological processes and diseases has been widely studied and reported in recent years. Several computational methods associated with mature miRNA identification suffer various limitations involvi...

Nucleotide-level Convolutional Neural Networks for Pre-miRNA Classification.

Scientific reports
Due to the biogenesis difference, miRNAs can be divided into canonical microRNAs and mirtrons. Compared to canonical microRNAs, mirtrons are less conserved and hard to be identified. Except stringent annotations based on experiments, many in silico c...

Discovering functional impacts of miRNAs in cancers using a causal deep learning model.

BMC medical genomics
BACKGROUND: Micro-RNAs (miRNAs) play a significant role in regulating gene expression under physiological and pathological conditions such as cancers. However, it remains a challenging problem to discover the target messenger RNAs (mRNAs) of a miRNA ...