Identification of long non-coding transcripts with feature selection: a comparative study

Background The unveiling of long non-coding RNAs as important gene regulators in many biological contexts has increased the demand for efficient and robust computational methods to identify novel long non-coding RNAs from transcripts assembled with high throughput RNA-seq data. Several classes of se...

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Veröffentlicht in:	BMC bioinformatics Jg. 18; H. 1; S. 187
Hauptverfasser:	Ventola, Giovanna M. M., Noviello, Teresa M. R., D’Aniello, Salvatore, Spagnuolo, Antonietta, Ceccarelli, Michele, Cerulo, Luigi
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	London BioMed Central 23.03.2017 Springer Nature B.V
Schlagworte:	Algorithms Annotations Art exhibits Bioinformatics Biomedical and Life Sciences Comparative studies Computational Biology/Bioinformatics Computer Appl. in Life Sciences Computer applications Danio rerio Feature extraction Feature selection Gene expression Genomes Humans Identification methods Learning algorithms Life Sciences Machine learning Microarrays Non-coding RNA Nucleotide sequence Nucleotides Predictions Protein structure Proteins Proteins - genetics Reading Research Article Ribonucleic acid RNA RNA, Long Noncoding - genetics Secondary structure Signatures Species Stability analysis Support vector machines Transcriptome analysis Zebrafish Feature selection lncRNA Classification
ISSN:	1471-2105, 1471-2105
Online-Zugang:	Volltext
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