AIMC Topic: Sequence Analysis, RNA

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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...

Usual Interstitial Pneumonia Can Be Detected in Transbronchial Biopsies Using Machine Learning.

Annals of the American Thoracic Society
RATIONALE: Usual interstitial pneumonia (UIP) is the histopathologic hallmark of idiopathic pulmonary fibrosis. Although UIP can be detected by high-resolution computed tomography of the chest, the results are frequently inconclusive, and pathology f...

Using neural networks for reducing the dimensions of single-cell RNA-Seq data.

Nucleic acids research
While only recently developed, the ability to profile expression data in single cells (scRNA-Seq) has already led to several important studies and findings. However, this technology has also raised several new computational challenges. These include ...

MetaSRA: normalized human sample-specific metadata for the Sequence Read Archive.

Bioinformatics (Oxford, England)
MOTIVATION: The NCBI's Sequence Read Archive (SRA) promises great biological insight if one could analyze the data in the aggregate; however, the data remain largely underutilized, in part, due to the poor structure of the metadata associated with ea...

An efficient graph kernel method for non-coding RNA functional prediction.

Bioinformatics (Oxford, England)
MOTIVATION: The importance of RNA protein-coding gene regulation is by now well appreciated. Non-coding RNAs (ncRNAs) are known to regulate gene expression at practically every stage, ranging from chromatin packaging to mRNA translation. However the ...

Removal of batch effects using distribution-matching residual networks.

Bioinformatics (Oxford, England)
MOTIVATION: Sources of variability in experimentally derived data include measurement error in addition to the physical phenomena of interest. This measurement error is a combination of systematic components, originating from the measuring instrument...

Identification of microRNA precursors using reduced and hybrid features.

Molecular bioSystems
MicroRNAs (also called miRNAs) are a group of short non-coding RNA molecules. They play a vital role in the gene expression of transcriptional and post-transcriptional processes. However, abnormality of their expression has been observed in cancer, h...

HIPred: an integrative approach to predicting haploinsufficient genes.

Bioinformatics (Oxford, England)
MOTIVATION: A major cause of autosomal dominant disease is haploinsufficiency, whereby a single copy of a gene is not sufficient to maintain the normal function of the gene. A large proportion of existing methods for predicting haploinsufficiency inc...

RED-ML: a novel, effective RNA editing detection method based on machine learning.

GigaScience
With the advancement of second generation sequencing techniques, our ability to detect and quantify RNA editing on a global scale has been vastly improved. As a result, RNA editing is now being studied under a growing number of biological conditions ...

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...