AIMC Topic: MicroRNAs

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Exploring microRNA Regulation of Cancer with Context-Aware Deep Cancer Classifier.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
BACKGROUND: MicroRNAs (miRNAs) are small, non-coding RNA that regulate gene expression through post-transcriptional silencing. Differential expression observed in miRNAs, combined with advancements in deep learning (DL), have the potential to improve...

DeepMirTar: a deep-learning approach for predicting human miRNA targets.

Bioinformatics (Oxford, England)
MOTIVATION: MicroRNAs (miRNAs) are small non-coding RNAs that function in RNA silencing and post-transcriptional regulation of gene expression by targeting messenger RNAs (mRNAs). Because the underlying mechanisms associated with miRNA binding to mRN...

Convolutional neural networks for classification of alignments of non-coding RNA sequences.

Bioinformatics (Oxford, England)
MOTIVATION: The convolutional neural network (CNN) has been applied to the classification problem of DNA sequences, with the additional purpose of motif discovery. The training of CNNs with distributed representations of four nucleotides has successf...

Machine Learning Identifies Stemness Features Associated with Oncogenic Dedifferentiation.

Cell
Cancer progression involves the gradual loss of a differentiated phenotype and acquisition of progenitor and stem-cell-like features. Here, we provide novel stemness indices for assessing the degree of oncogenic dedifferentiation. We used an innovati...

microRPM: a microRNA prediction model based only on plant small RNA sequencing data.

Bioinformatics (Oxford, England)
MOTIVATION: MicroRNAs (miRNAs) are endogenous non-coding small RNAs (of about 22 nucleotides), which play an important role in the post-transcriptional regulation of gene expression via either mRNA cleavage or translation inhibition. Several machine ...

Genome-wide pre-miRNA discovery from few labeled examples.

Bioinformatics (Oxford, England)
MOTIVATION: Although many machine learning techniques have been proposed for distinguishing miRNA hairpins from other stem-loop sequences, most of the current methods use supervised learning, which requires a very good set of positive and negative ex...

BioMuta and BioXpress: mutation and expression knowledgebases for cancer biomarker discovery.

Nucleic acids research
Single-nucleotide variation and gene expression of disease samples represent important resources for biomarker discovery. Many databases have been built to host and make available such data to the community, but these databases are frequently limited...

miRandola 2017: a curated knowledge base of non-invasive biomarkers.

Nucleic acids research
miRandola (http://mirandola.iit.cnr.it/) is a database of extracellular non-coding RNAs (ncRNAs) that was initially published in 2012, foreseeing the relevance of ncRNAs as non-invasive biomarkers. An increasing amount of experimental evidence shows ...

Mirnovo: genome-free prediction of microRNAs from small RNA sequencing data and single-cells using decision forests.

Nucleic acids research
The discovery of microRNAs (miRNAs) remains an important problem, particularly given the growth of high-throughput sequencing, cell sorting and single cell biology. While a large number of miRNAs have already been annotated, there may well be large n...