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Machine Learning Reduced Gene/Non-Coding RNA Features That Classify Schizophrenia Patients Accurately and Highlight Insightful Gene Clusters.

International journal of molecular sciences
RNA-seq has been a powerful method to detect the differentially expressed genes/long non-coding RNAs (lncRNAs) in schizophrenia (SCZ) patients; however, due to overfitting problems differentially expressed targets (DETs) cannot be used properly as bi...

EDLMFC: an ensemble deep learning framework with multi-scale features combination for ncRNA-protein interaction prediction.

BMC bioinformatics
BACKGROUND: Non-coding RNA (ncRNA) and protein interactions play essential roles in various physiological and pathological processes. The experimental methods used for predicting ncRNA-protein interactions are time-consuming and labor-intensive. Ther...

Role of Regulatory Non-Coding RNAs in Aggressive Thyroid Cancer: Prospective Applications of Neural Network Analysis.

Molecules (Basel, Switzerland)
Thyroid cancer (TC) is the most common endocrine malignancy. Most TCs have a favorable prognosis, whereas anaplastic thyroid carcinoma (ATC) is a lethal form of cancer. Different genetic and epigenetic alterations have been identified in aggressive f...

ncRDense: A novel computational approach for classification of non-coding RNA family by deep learning.

Genomics
With the rapidly growing importance of biological research, non-coding RNAs (ncRNA) attract more attention in biology and bioinformatics. They play vital roles in biological processes such as transcription and translation. Classification of ncRNAs is...

NPI-GNN: Predicting ncRNA-protein interactions with deep graph neural networks.

Briefings in bioinformatics
Noncoding RNAs (ncRNAs) play crucial roles in many biological processes. Experimental methods for identifying ncRNA-protein interactions (NPIs) are always costly and time-consuming. Many computational approaches have been developed as alternative way...

WEVar: a novel statistical learning framework for predicting noncoding regulatory variants.

Briefings in bioinformatics
Understanding the functional consequence of noncoding variants is of great interest. Though genome-wide association studies or quantitative trait locus analyses have identified variants associated with traits or molecular phenotypes, most of them are...

FexRNA: Exploratory Data Analysis and Feature Selection of Non-Coding RNA.

IEEE/ACM transactions on computational biology and bioinformatics
Non-coding RNA (ncRNA) is involved in many biological processes and diseases in all species. Many ncRNA datasets exist that provide ncRNA data in FASTA format which is well suited for biomedical purposes. However, for ncRNA analysis and classificatio...

Graph Neural Network with Self-Supervised Learning for Noncoding RNA-Drug Resistance Association Prediction.

Journal of chemical information and modeling
Noncoding RNA(ncRNA) is closely related to drug resistance. Identifying the association between ncRNA and drug resistance is of great significance for drug development. Methods based on biological experiments are often time-consuming and small-scale....

Inverse folding based pre-training for the reliable identification of intrinsic transcription terminators.

PLoS computational biology
It is well-established that neural networks can predict or identify structural motifs of non-coding RNAs (ncRNAs). Yet, the neural network based identification of RNA structural motifs is limited by the availability of training data that are often in...

Predicting ncRNA-protein interactions based on dual graph convolutional network and pairwise learning.

Briefings in bioinformatics
Noncoding RNAs (ncRNAs) have recently attracted considerable attention due to their key roles in biology. The ncRNA-proteins interaction (NPI) is often explored to reveal some biological activities that ncRNA may affect, such as biological traits, di...