Machine learning and integrative analysis of biomedical big data
Recent developments in high-throughput technologies have accelerated the accumulation
of massive amounts of omics data from multiple sources: genome, epigenome …
of massive amounts of omics data from multiple sources: genome, epigenome …
[HTML][HTML] AI applications in functional genomics
We review the current applications of artificial intelligence (AI) in functional genomics. The
recent explosion of AI follows the remarkable achievements made possible by “deep …
recent explosion of AI follows the remarkable achievements made possible by “deep …
RNAsamba: neural network-based assessment of the protein-coding potential of RNA sequences
AP Camargo, V Sourkov, GAG Pereira… - NAR genomics and …, 2020 - academic.oup.com
The advent of high-throughput sequencing technologies made it possible to obtain large
volumes of genetic information, quickly and inexpensively. Thus, many efforts are devoted to …
volumes of genetic information, quickly and inexpensively. Thus, many efforts are devoted to …
CPPred: coding potential prediction based on the global description of RNA sequence
X Tong, S Liu - Nucleic acids research, 2019 - academic.oup.com
The rapid and accurate approach to distinguish between coding RNAs and ncRNAs has
been playing a critical role in analyzing thousands of novel transcripts, which have been …
been playing a critical role in analyzing thousands of novel transcripts, which have been …
LncRNAnet: long non-coding RNA identification using deep learning
Abstract Motivation Long non-coding RNAs (lncRNAs) are important regulatory elements in
biological processes. LncRNAs share similar sequence characteristics with messenger …
biological processes. LncRNAs share similar sequence characteristics with messenger …
The role of micropeptides in biology
Micropeptides are small polypeptides coded by small open-reading frames. Progress in
computational biology and the analyses of large-scale transcriptomes and proteomes have …
computational biology and the analyses of large-scale transcriptomes and proteomes have …
Feature extraction approaches for biological sequences: a comparative study of mathematical features
RP Bonidia, LDH Sampaio… - Briefings in …, 2021 - academic.oup.com
As consequence of the various genomic sequencing projects, an increasing volume of
biological sequence data is being produced. Although machine learning algorithms have …
biological sequence data is being produced. Although machine learning algorithms have …
A support vector machine based method to distinguish long non-coding RNAs from protein coding transcripts
HW Schneider, T Raiol, MM Brigido, MEMT Walter… - BMC genomics, 2017 - Springer
Background In recent years, a rapidly increasing number of RNA transcripts has been
generated by thousands of sequencing projects around the world, creating enormous …
generated by thousands of sequencing projects around the world, creating enormous …
Long noncoding RNA identification: comparing machine learning based tools for long noncoding transcripts discrimination
Long noncoding RNA (lncRNA) is a kind of noncoding RNA with length more than 200
nucleotides, which aroused interest of people in recent years. Lots of studies have confirmed …
nucleotides, which aroused interest of people in recent years. Lots of studies have confirmed …
Common features in lncRNA annotation and classification: a survey
Long non-coding RNAs (lncRNAs) are widely recognized as important regulators of gene
expression. Their molecular functions range from miRNA sponging to chromatin-associated …
expression. Their molecular functions range from miRNA sponging to chromatin-associated …