CancerMHL: the database of integrating key DNA methylation, histone modifications and lncRNAs in cancer DOI Creative Commons

Pengyu Du,

Yingli Chen,

Qian‐Zhong Li

et al.

Database, Journal Year: 2024, Volume and Issue: 2024

Published: Jan. 1, 2024

The discovery of key epigenetic modifications in cancer is great significance for the study disease biomarkers. Through mining modification data relevant to cancer, some researches on are accumulating. In order make it easier integrate effects related cancers, we established CancerMHL (http://www.positionprediction.cn/), which provide DNA methylation, histone and lncRNAs as well effect these gene expression several cancers. To facilitate retrieval, offers flexible query options filters, allowing users access specific according their own needs. addition, based data, three online prediction tools had been offered users. will be a useful resource platform further exploring novel potential biomarkers therapeutic targets cancer. Database URL: http://www.positionprediction.cn/.

Language: Английский

DisGeNet: a disease-centric interaction database among diseases and various associated genes DOI Creative Commons
Yaxuan Hu, Xingli Guo,

Yun Yao

et al.

Database, Journal Year: 2025, Volume and Issue: 2025

Published: Jan. 1, 2025

The pathogenesis of complex diseases is intricately linked to various genes and network medicine has enhanced understanding diseases. However, most network-based approaches ignore interactions mediated by noncoding RNAs (ncRNAs) databases only focus on the association between Based mentioned questions, we have developed DisGeNet, a database focuses not disease-associated but also among genes. Here, associations genes, as well these are integrated into disease-centric network. As result, there total 502 688 interactions/associations involving 6697 diseases, 5780 lncRNAs (long RNAs), 16 135 protein-coding 2610 microRNAs stored in DisGeNet. These can be categorized protein-protein, lncRNA-disease, microRNA-gene, microRNA-disease, gene-disease, microRNA-lncRNA. Furthermore, users input name/ID diseases/genes for search, about search content browsed list or viewed local network-view. Database URL: https://disgenet.cn/.

Language: Английский

Citations

3
lncRNAlyzr: Enrichment Analysis for lncRNA Sets DOI
John Erol Evangelista,

Tahleel Ali-Nasser,

L Malek

et al.

Journal of Molecular Biology, Journal Year: 2025, Volume and Issue: unknown, P. 168938 - 168938

Published: Jan. 1, 2025

Language: Английский

Citations

2
Long Non-Coding RNAs in Humans: Classification, Genomic Organization and Function DOI Creative Commons

Barbara Chodurska,

Tanja Kunej

Non-coding RNA Research, Journal Year: 2025, Volume and Issue: 11, P. 313 - 327

Published: Jan. 14, 2025

Long non-coding RNAs (lncRNAs) regulate numerous biological functions in animals. Despite recent advances lncRNA research, their structural and functional annotation classification remain an ongoing challenge. This review provides a comprehensive overview of human lncRNAs, highlighting genomic organization, mode action role physiological pathological processes. Subgroups genes are discussed using representative examples visualizations organization. The HUGO Gene Nomenclature Committee (HGNC) categorizes lncRNAs into nine subgroups: (1) microRNA host genes, (2) small nucleolar RNA (3) long intergenic non-protein coding (LINC), (4) antisense RNAs, (5) overlapping transcripts, (6) intronic (7) divergent (8) with non-systematic symbols (9) FAM root systems. Circular (circRNAs) separate class that shares some characteristics divided exonic, intronic-exonic types. LncRNAs act as molecular signals, decoys, scaffolds sponges for microRNAs often function competing endogenous (ceRNAs). involved various processes, such cell differentiation, p53-mediated DNA damage response, glucose metabolism, inflammation immune functions. They associated several diseases, including types neoplasms, Alzheimer's disease autoimmune diseases. A clear system is essential understanding facilitating practical applications biomedical research. Future studies should focus on drug development biomarker discovery. As important regulators represent promising targets innovative therapies.

Language: Английский

Citations

2
A Survey of Deep Learning for Detecting miRNA- Disease Associations: Databases, Computational Methods, Challenges, and Future Directions DOI
Nan Sheng, Xuping Xie, Yan Wang

et al.

IEEE/ACM Transactions on Computational Biology and Bioinformatics, Journal Year: 2024, Volume and Issue: 21(3), P. 328 - 347

Published: Jan. 9, 2024

MicroRNAs (miRNAs) are an important class of non-coding RNAs that play essential role in the occurrence and development various diseases. Identifying potential miRNA-disease associations (MDAs) can be beneficial understanding disease pathogenesis. Traditional laboratory experiments expensive time-consuming. Computational models have enabled systematic large-scale prediction MDAs, greatly improving research efficiency. With recent advances deep learning, it has become attractive powerful technique for uncovering novel MDAs. Consequently, numerous MDA methods based on learning emerged. In this review, we first summarize publicly available databases related to miRNAs diseases prediction. Next, outline commonly used miRNA similarity calculation integration methods. Then, comprehensively review 48 existing learning-based computation methods, categorizing them into classical graph neural network-based techniques. Subsequently, investigate evaluation metrics frequently assess performance. Finally, discuss performance trends different computational point out some problems current research, propose 9 future directions. Data resources summarized GitHub repository https://github.com/sheng-n/DL-miRNA-disease-association-methods .

Language: Английский

Citations

9
Finding potential lncRNA–disease associations using a boosting-based ensemble learning model DOI Creative Commons
Liqian Zhou,

Xinhuai Peng,

Lijun Zeng

et al.

Frontiers in Genetics, Journal Year: 2024, Volume and Issue: 15

Published: March 1, 2024

Introduction: Long non-coding RNAs (lncRNAs) have been in the clinical use as potential prognostic biomarkers of various types cancer. Identifying associations between lncRNAs and diseases helps capture design efficient therapeutic options for diseases. Wet experiments identifying these are costly laborious. Methods: We developed LDA-SABC, a novel boosting-based framework lncRNA–disease association (LDA) prediction. LDA-SABC extracts LDA features based on singular value decomposition (SVD) classifies pairs (LDPs) by incorporating LightGBM AdaBoost into convolutional neural network. Results: The performance was evaluated under five-fold cross validations (CVs) lncRNAs, diseases, LDPs. It obviously outperformed four other classical inference methods (SDLDA, LDNFSGB, LDASR, IPCAF) through precision, recall, accuracy, F1 score, AUC, AUPR. Based accurate prediction we used it to find lncRNA lung results elucidated that 7SK HULC could relationship with non-small-cell cancer (NSCLC) adenocarcinoma (LUAD), respectively. Conclusion: hope our proposed method can help improve identification.

Language: Английский

Citations

7
Exploring the enigma: history, present, and future of long non-coding RNAs in cancer DOI Creative Commons
Qais Ahmad Naseer,

Abdul Malik,

Fengyuan Zhang

et al.

Discover Oncology, Journal Year: 2024, Volume and Issue: 15(1)

Published: June 7, 2024

Abstract Long noncoding RNAs (lncRNAs), which are more than 200 nucleotides in length and do not encode proteins, play crucial roles governing gene expression at both the transcriptional posttranscriptional levels. These molecules demonstrate specific patterns various tissues developmental stages, suggesting their involvement numerous processes diseases, notably cancer. Despite widespread acknowledgment growing enthusiasm surrounding potential as diagnostic prognostic biomarkers, precise mechanisms through lncRNAs function remain inadequately understood. A few have been studied depth, providing valuable insights into biological activities emerging functional themes mechanistic models. However, extent to mammalian genome is transcribed transcripts still a matter of debate. This review synthesizes our current understanding lncRNA biogenesis, genomic contexts, multifaceted tumorigenesis, highlighting cancer-targeted therapy. By exploring historical perspectives alongside recent breakthroughs, we aim illuminate diverse reflect on broader implications study for evolution function, well advancing clinical applications.

Language: Английский

Citations

7
lncRNA-disease association prediction based on optimizing measures of multi-graph regularized matrix factorization DOI
Bin Yao, Yunzhong Song

Computer Methods in Biomechanics & Biomedical Engineering, Journal Year: 2025, Volume and Issue: unknown, P. 1 - 16

Published: March 20, 2025

In this paper, we propose a novel lncRNA-disease association prediction algorithm based on optimizing measures of multi-graph regularized matrix factorization (OM-MGRMF). The method first calculates the semantic similarity diseases, functional lncRNAs, and Gaussian both. It then constructs new by using K-nearest-neighbor (KNN) algorithm. Finally, objective function is constructed through utilization ranking regularization constraints. This iteratively optimized an adaptive gradient descent experimental results OM-MGRMF outperform those classical methods in both K-fold cross-validation.

Language: Английский

Citations

1
The 2024 Nucleic Acids Research database issue and the online molecular biology database collection DOI Creative Commons
Daniel J. Rigden, Xosé M. Fernández

Nucleic Acids Research, Journal Year: 2023, Volume and Issue: 52(D1), P. D1 - D9

Published: Nov. 30, 2023

Abstract The 2024 Nucleic Acids Research database issue contains 180 papers from across biology and neighbouring disciplines. There are 90 reporting on new databases 83 updates resources previously published in the Issue. Updates most recently elsewhere account for a further seven. acid include NAKB structural information Genbank, ENA, GEO, Tarbase JASPAR. Issue's Breakthrough Article concerns NMPFamsDB novel prokaryotic protein families AlphaFold Protein Structure Database has an important update. Metabolism is covered by Reactome, Wikipathways Metabolights. Microbes RefSeq, UNITE, SPIRE P10K; viruses ViralZone PhageScope. Medically-oriented familiar COSMIC, Drugbank TTD. Genomics-related Ensembl, UCSC Genome Browser Monarch. New arrivals cover plant imaging (OPIA PlantPAD) crop plants (SoyMD, TCOD CropGS-Hub). entire Issue freely available online website (https://academic.oup.com/nar). Over last year NAR Molecular Biology Collection been updated, reviewing 1060 entries, adding 97 eliminating 388 discontinued URLs bringing current total to 1959 databases. It at http://www.oxfordjournals.org/nar/database/c/.

Language: Английский

Citations

11
LDAGM: prediction lncRNA-disease asociations by graph convolutional auto-encoder and multilayer perceptron based on multi-view heterogeneous networks DOI Creative Commons
Bing Zhang, H. Wang, Chao Ma

et al.

BMC Bioinformatics, Journal Year: 2024, Volume and Issue: 25(1)

Published: Oct. 15, 2024

Long non-coding RNAs (lncRNAs) can prevent, diagnose, and treat a variety of complex human diseases, it is crucial to establish method efficiently predict lncRNA-disease associations.

Language: Английский

Citations

4
ncRS: A resource of non-coding RNAs in sepsis DOI

Baocai Zhong,

Yongfang Dai,

Li Chen

et al.

Computers in Biology and Medicine, Journal Year: 2024, Volume and Issue: 172, P. 108256 - 108256

Published: March 11, 2024

Language: Английский

Citations

3