Attentional multi-level representation encoding based on convolutional and variance autoencoders for lncRNA-disease association prediction
Sign Up to like & getrecommendations! Published in 2021 at "Briefings in bioinformatics"
DOI: 10.1093/bib/bbaa067
Abstract: As the abnormalities of long non-coding RNAs (lncRNAs) are closely related to various human diseases, identifying disease-related lncRNAs is important for understanding the pathogenesis of complex diseases. Most of current data-driven methods for disease-related lncRNA… read more here.
Keywords: lncrna disease; multi level; disease; topology ... See more keywords
GAERF: predicting lncRNA-disease associations by graph auto-encoder and random forest
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DOI: 10.1093/bib/bbaa391
Abstract: 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 approach to identify… read more here.
Keywords: auto encoder; lncrna disease; disease associations; disease ... See more keywords
LDAformer: predicting lncRNA-disease associations based on topological feature extraction and Transformer encoder
Sign Up to like & getrecommendations! Published in 2022 at "Briefings in bioinformatics"
DOI: 10.1093/bib/bbac370
Abstract: The identification of long noncoding RNA (lncRNA)-disease associations is of great value for disease diagnosis and treatment, and it is now commonly used to predict potential lncRNA-disease associations with computational methods. However, the existing methods… read more here.
Keywords: disease; lncrna disease; feature extraction; topological feature ... See more keywords
LncRNA-disease association identification using graph auto-encoder and learning to rank
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DOI: 10.1093/bib/bbac539
Abstract: Discovering the relationships between long non-coding RNAs (lncRNAs) and diseases is significant in the treatment, diagnosis and prevention of diseases. However, current identified lncRNA-disease associations are not enough because of the expensive and heavy workload… read more here.
Keywords: graltr lda; disease; lncrna disease; disease associations ... See more keywords
Multi-view contrastive heterogeneous graph attention network for lncRNA-disease association prediction
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DOI: 10.1093/bib/bbac548
Abstract: MOTIVATION Exploring the potential long noncoding RNA (lncRNA)-disease associations (LDAs) plays a critical role for understanding disease etiology and pathogenesis. Given the high cost of biological experiments, developing a computational method is a practical necessity… read more here.
Keywords: topology; network; view; disease ... See more keywords
Prediction of LncRNA-Disease Associations Based on Network Consistency Projection
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DOI: 10.1109/access.2019.2914533
Abstract: A growing body of research has uncovered the role of long noncoding RNAs (lncRNAs) in multiple biological processes and tumorigenesis. Predicting novel interactions between diseases and lncRNAs could help decipher disease pathology and discover new… read more here.
Keywords: similarity lncrna; consistency projection; similarity; lncrna disease ... See more keywords
ILDMSF: Inferring Associations Between Long Non-Coding RNA and Disease Based on Multi-Similarity Fusion
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DOI: 10.1109/tcbb.2019.2936476
Abstract: The dysregulation and mutation of long non-coding RNAs (lncRNAs) have been proved to result in a variety of human diseases. Identifying potential disease-related lncRNAs may benefit disease diagnosis, treatment and prognosis. A number of methods… read more here.
Keywords: ildmsf inferring; similarity; lncrna disease; disease ... See more keywords
iLncRNAdis-FB: Identify lncRNA-Disease Associations by Fusing Biological Feature Blocks Through Deep Neural Network
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DOI: 10.1109/tcbb.2020.2964221
Abstract: Identification of lncRNA-disease associations is not only important for exploring the disease mechanism, but will also facilitate the molecular targeting drug discovery. Fusing multiple biological information is able to generate a more comprehensive view of… read more here.
Keywords: neural network; feature blocks; feature; lncrna disease ... See more keywords
Semantic Meta-Path Enhanced Global and Local Topology Learning for lncRNA-Disease Association Prediction
Sign Up to like & getrecommendations! Published in 2022 at "IEEE/ACM Transactions on Computational Biology and Bioinformatics"
DOI: 10.1109/tcbb.2022.3209571
Abstract: Since abnormal expression of long non-coding RNAs (lncRNAs) is associated with various human diseases, identifying disease-related lncRNAs helps reveal the pathogenesis of diseases. Existing methods for lncRNA-disease association prediction mainly focus on multi-sourced data related… read more here.
Keywords: topology; semantics; disease; lncrna disease ... See more keywords
A learning-based method to predict LncRNA-disease associations by combining CNN and ELM
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DOI: 10.1186/s12859-022-04611-3
Abstract: Background lncRNAs play a critical role in numerous biological processes and life activities, especially diseases. Considering that traditional wet experiments for identifying uncovered lncRNA-disease associations is limited in terms of time consumption and labor cost.… read more here.
Keywords: disease associations; cnn; biology; lncrna disease ... See more keywords
BRWLDA: bi-random walks for predicting lncRNA-disease associations
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DOI: 10.18632/oncotarget.19588
Abstract: Increasing efforts have been done to figure out the association between lncRNAs and complex diseases. Many computational models construct various lncRNA similarity networks, disease similarity networks, along with known lncRNA-disease associations to infer novel associations.… read more here.
Keywords: network; lncrna disease; disease associations; associations brwlda ... See more keywords