AIMC Topic: Sequence Analysis, RNA

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Cancer Type Prediction and Classification Based on RNA-sequencing Data.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Pan-cancer analysis is a significant research topic in the past few years. Due to many advancing sequencing technologies, researchers possess more resources and knowledge to identify the key factors that could trigger cancer. Furthermore, since The C...

Discriminating early- and late-stage cancers using multiple kernel learning on gene sets.

Bioinformatics (Oxford, England)
MOTIVATION: Identifying molecular mechanisms that drive cancers from early to late stages is highly important to develop new preventive and therapeutic strategies. Standard machine learning algorithms could be used to discriminate early- and late-sta...

COSSMO: predicting competitive alternative splice site selection using deep learning.

Bioinformatics (Oxford, England)
MOTIVATION: Alternative splice site selection is inherently competitive and the probability of a given splice site to be used also depends on the strength of neighboring sites. Here, we present a new model named the competitive splice site model (COS...

Feature specific quantile normalization enables cross-platform classification of molecular subtypes using gene expression data.

Bioinformatics (Oxford, England)
MOTIVATION: Molecular subtypes of cancers and autoimmune disease, defined by transcriptomic profiling, have provided insight into disease pathogenesis, molecular heterogeneity and therapeutic responses. However, technical biases inherent to different...

Modeling positional effects of regulatory sequences with spline transformations increases prediction accuracy of deep neural networks.

Bioinformatics (Oxford, England)
MOTIVATION: Regulatory sequences are not solely defined by their nucleic acid sequence but also by their relative distances to genomic landmarks such as transcription start site, exon boundaries or polyadenylation site. Deep learning has become the a...

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...

SIMLR: A Tool for Large-Scale Genomic Analyses by Multi-Kernel Learning.

Proteomics
SIMLR (Single-cell Interpretation via Multi-kernel LeaRning), an open-source tool that implements a novel framework to learn a sample-to-sample similarity measure from expression data observed for heterogenous samples, is presented here. SIMLR can be...

Deep learning of the splicing (epi)genetic code reveals a novel candidate mechanism linking histone modifications to ESC fate decision.

Nucleic acids research
Alternative splicing (AS) is a genetically and epigenetically regulated pre-mRNA processing to increase transcriptome and proteome diversity. Comprehensively decoding these regulatory mechanisms holds promise in getting deeper insights into a variety...