AI Medical Compendium Topic

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Geminivirus data warehouse: a database enriched with machine learning approaches.

BMC bioinformatics
BACKGROUND: The Geminiviridae family encompasses a group of single-stranded DNA viruses with twinned and quasi-isometric virions, which infect a wide range of dicotyledonous and monocotyledonous plants and are responsible for significant economic los...

TITER: predicting translation initiation sites by deep learning.

Bioinformatics (Oxford, England)
MOTIVATION: Translation initiation is a key step in the regulation of gene expression. In addition to the annotated translation initiation sites (TISs), the translation process may also start at multiple alternative TISs (including both AUG and non-A...

Fangorn Forest (F2): a machine learning approach to classify genes and genera in the family Geminiviridae.

BMC bioinformatics
BACKGROUND: Geminiviruses infect a broad range of cultivated and non-cultivated plants, causing significant economic losses worldwide. The studies of the diversity of species, taxonomy, mechanisms of evolution, geographic distribution, and mechanisms...

A Support Vector Machine based method to distinguish long non-coding RNAs from protein coding transcripts.

BMC genomics
BACKGROUND: In recent years, a rapidly increasing number of RNA transcripts has been generated by thousands of sequencing projects around the world, creating enormous volumes of transcript data to be analyzed. An important problem to be addressed whe...

Improved ontology for eukaryotic single-exon coding sequences in biological databases.

Database : the journal of biological databases and curation
Efficient extraction of knowledge from biological data requires the development of structured vocabularies to unambiguously define biological terms. This paper proposes descriptions and definitions to disambiguate the term 'single-exon gene'. Eukaryo...

Prediction of plant lncRNA by ensemble machine learning classifiers.

BMC genomics
BACKGROUND: In plants, long non-protein coding RNAs are believed to have essential roles in development and stress responses. However, relative to advances on discerning biological roles for long non-protein coding RNAs in animal systems, this RNA cl...

A deep recurrent neural network discovers complex biological rules to decipher RNA protein-coding potential.

Nucleic acids research
The current deluge of newly identified RNA transcripts presents a singular opportunity for improved assessment of coding potential, a cornerstone of genome annotation, and for machine-driven discovery of biological knowledge. While traditional, featu...

LncRNAnet: long non-coding RNA identification using deep learning.

Bioinformatics (Oxford, England)
MOTIVATION: Long non-coding RNAs (lncRNAs) are important regulatory elements in biological processes. LncRNAs share similar sequence characteristics with messenger RNAs, but they play completely different roles, thus providing novel insights for biol...

MiPepid: MicroPeptide identification tool using machine learning.

BMC bioinformatics
BACKGROUND: Micropeptides are small proteins with length < = 100 amino acids. Short open reading frames that could produces micropeptides were traditionally ignored due to technical difficulties, as few small peptides had been experimentally confirme...

The hunt for sORFs: A multidisciplinary strategy.

Experimental cell research
Growing evidence illustrates the shortcomings on the current understanding of the full complexity of the proteome. Previously overlooked small open reading frames (sORFs) and their encoded microproteins have filled important gaps, exerting their func...