ArticleScientific reports2015
KATZLDA: KATZ measure for the lncRNA-disease association prediction.
Article in Scientific reports, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 98 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
98 citing papers in PubMed, 230 citations in OpenAlex.
- RGPA-GCN: Graph convolutional networks for rice gene-phenotype association prediction.Plant biology (Stuttgart, Germany) · 2026Article
- LDA-SCGB: inferring lncRNA-disease associations based on condensed gradient boosting.BMC bioinformatics · 2025Article
- Predicting lncRNA and disease associations with graph autoencoder and noise robust gradient boosting.Scientific reports · 2025Article
- Herb-disease association prediction model based on network consistency projection.Scientific reports · 2025Article
- A novel end-to-end learning framework for inferring lncRNA-disease associations based on convolution neural network.Frontiers in genetics · 2025Article
- PDDGCN: A Parasitic Disease-Drug Association Predictor Based on Multi-view Fusion Graph Convolutional Network.Interdisciplinary sciences, computational life sciences · 2024Article
- GCNFORMER: graph convolutional network and transformer for predicting lncRNA-disease associations.BMC bioinformatics · 2024Article
- Finding potential lncRNA-disease associations using a boosting-based ensemble learning model.Frontiers in genetics · 2024Article
- LDA-VGHB: identifying potential lncRNA-disease associations with singular value decomposition, variational graph auto-encoder and heterogeneous Newton boosting machine.Briefings in bioinformatics · 2023Article
- KATZNCP: a miRNA-disease association prediction model integrating KATZ algorithm and network consistency projection.BMC bioinformatics · 2023Article
- JSCSNCP-LMA: a method for predicting the association of lncRNA-miRNA.Scientific reports · 2022Article
- lncRNA-disease association prediction based on matrix decomposition of elastic network and collaborative filtering.Scientific reports · 2022Article
- High expression of lncRNA PELATON serves as a risk factor for the incidence and prognosis of acute coronary syndrome.Scientific reports · 2022Article
- Revealing key lncRNAs in cytogenetically normal acute myeloid leukemia by reconstruction of the lncRNA-miRNA-mRNA network.Scientific reports · 2022Article
- gGATLDA: lncRNA-disease association prediction based on graph-level graph attention network.BMC bioinformatics · 2022Article
- Geometric complement heterogeneous information and random forest for predicting lncRNA-disease associations.Frontiers in genetics · 2022Article
- Identifying lncRNA-disease association based on GAT multiple-operator aggregation and inductive matrix completion.Frontiers in genetics · 2022Article
- NCP-BiRW: A Hybrid Approach for Predicting Long Noncoding RNA-Disease Associations by Network Consistency Projection and Bi-Random Walk.Frontiers in genetics · 2022Article
- Screening potential lncRNA biomarkers for breast cancer and colorectal cancer combining random walk and logistic matrix factorization.Frontiers in genetics · 2022Article
- Inferring Latent Disease-lncRNA Associations by Label-Propagation Algorithm and Random Projection on a Heterogeneous Network.Frontiers in genetics · 2022Article
38 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Accumulating experimental studies have demonstrated important associations between alterations and dysregulations of lncRNAs and the development and progression of various complex human diseases. Developing effective computational models to integrate vast amount of heterogeneous biological data for the identification of potential disease-lncRNA associations has become a hot topic in the fields of human complex diseases and lncRNAs, which could benefit lncRNA biomarker detection for disease diagnosis, treatment, and prevention. Considering the limitations in previous computational methods, the model of KATZ measure for LncRNA-Disease Association prediction (KATZLDA) was developed to uncover potential lncRNA-disease associations by integrating known lncRNA-disease associations, lncRNA expression profiles, lncRNA functional similarity, disease semantic similarity, and Gaussian interaction profile kernel similarity. KATZLDA could work for diseases without known related lncRNAs and lncRNAs without known associated diseases. KATZLDA obtained reliable AUCs of 7175, 0.7886, 0.7719 in the local and global leave-one-out cross validation and 5-fold cross validation, respectively, significantly improving previous classical methods. Furthermore, case studies of colon, gastric, and renal cancer were implemented and 60% of top 10 predictions have been confirmed by recent biological experiments. It is anticipated that KATZLDA could be an important resource with potential values for biomedical researches.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.