AIMC Topic: RNA, Long Noncoding
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Feature extraction approaches for biological sequences: a comparative study of mathematical features.
Briefings in bioinformatics
Sep 2, 2021
As consequence of the various genomic sequencing projects, an increasing volume of biological sequence data is being produced. Although machine learning algorithms have been successfully applied to a large number of genomic sequence-related problems,...
GAERF: predicting lncRNA-disease associations by graph auto-encoder and random forest.
Briefings in bioinformatics
Sep 2, 2021
Predicting disease-related long non-coding RNAs (lncRNAs) is beneficial to finding of new biomarkers for prevention, diagnosis and treatment of complex human diseases. In this paper, we proposed a machine learning techniques-based classification appr...
Classification and gene selection of triple-negative breast cancer subtype embedding gene connectivity matrix in deep neural network.
Briefings in bioinformatics
Sep 2, 2021
Triple-negative breast cancer (TNBC) has been a challenging breast cancer subtype for oncological therapy. Normally, it can be classified into different molecular subtypes. Accurate and stable classification of the six subtypes is essential for perso...
SubLocEP: a novel ensemble predictor of subcellular localization of eukaryotic mRNA based on machine learning.
Briefings in bioinformatics
Sep 2, 2021
MOTIVATION: mRNA location corresponds to the location of protein translation and contributes to precise spatial and temporal management of the protein function. However, current assignment of subcellular localization of eukaryotic mRNA reveals import...
A comprehensive survey on computational methods of non-coding RNA and disease association prediction.
Briefings in bioinformatics
Jul 20, 2021
The studies on relationships between non-coding RNAs and diseases are widely carried out in recent years. A large number of experimental methods and technologies of producing biological data have also been developed. However, due to their high labor ...
Predicting the interaction biomolecule types for lncRNA: an ensemble deep learning approach.
Briefings in bioinformatics
Jul 20, 2021
Long noncoding RNAs (lncRNAs) play significant roles in various physiological and pathological processes via their interactions with biomolecules like DNA, RNA and protein. The existing in silico methods used for predicting the functions of lncRNA ma...
Predicting microRNA-disease associations from lncRNA-microRNA interactions via Multiview Multitask Learning.
Briefings in bioinformatics
May 20, 2021
MOTIVATION: Identifying microRNAs that are associated with different diseases as biomarkers is a problem of great medical significance. Existing computational methods for uncovering such microRNA-diseases associations (MDAs) are mostly developed unde...
MLCDForest: multi-label classification with deep forest in disease prediction for long non-coding RNAs.
Briefings in bioinformatics
May 20, 2021
The long non-coding RNAs (lncRNAs) are subject of intensive recent studies due to its association with various human diseases. It is desirable to build the artificial intelligence-based models for prediction of diseases or tissues based on the lncRNA...
DeepCPP: a deep neural network based on nucleotide bias information and minimum distribution similarity feature selection for RNA coding potential prediction.
Briefings in bioinformatics
Mar 22, 2021
The development of deep sequencing technologies has led to the discovery of novel transcripts. Many in silico methods have been developed to assess the coding potential of these transcripts to further investigate their functions. Existing methods per...
m6A-Atlas: a comprehensive knowledgebase for unraveling the N6-methyladenosine (m6A) epitranscriptome.
Nucleic acids research
Jan 8, 2021
N 6-Methyladenosine (m6A) is the most prevalent RNA modification on mRNAs and lncRNAs. It plays a pivotal role during various biological processes and disease pathogenesis. We present here a comprehensive knowledgebase, m6A-Atlas, for unraveling the ...
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