Cited by 359MicroRNA in disease regulationPlant Molecular Biology ResearchCancer-related molecular mechanisms research

The DOI matches its Crossref record; 49 of the 49 references it cites were found in a registry or an index.

TransmiR v2.0: an updated transcription factor-microRNA regulation database

Open access

Tong, Zhan, Cui, Qinghua, Wang, Juan, Zhou, Yuan·2019·Nucleic Acids Research

Open this paper in CoChat — free account

Abstract

MicroRNAs (miRNAs) are important post-transcriptional regulators of gene expression and play vital roles in various biological processes. It has been reported that aberrant regulation of miRNAs was associated with the development and progression of various diseases, but the underlying mechanisms are not fully deciphered. Here, we described our updated TransmiR v2.0 database for more comprehensive information about transcription factor (TF)-miRNA regulations. 3730 TF-miRNA regulations among 19 species from 1349 reports were manually curated by surveying >8000 publications, and more than 1.7 million tissue-specific TF-miRNA regulations were further incorporated based on ChIP-seq data. Besides, we constructed a 'Predict' module to query the predicted TF-miRNA regulations in human based on binding motifs of TFs. To facilitate the community, we provided a 'Network' module to visualize TF-miRNA regulations for each TF and miRNA, or for a specific disease. An 'Enrichment analysis' module was also included to predict TFs that are likely to regulate a miRNA list of interest. In conclusion, with improved data coverage and webserver functionalities, TransmiR v2.0 would be a useful resource for investigating the regulation of miRNAs. TransmiR v2.0 is freely accessible at http://www.cuilab.cn/transmir.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

Integrity signals

Registry

Crossref · 10.1093/nar/gky1023

Cites

49 of 49 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Untitled work
Bartel (2004) · doi:10.1016/s0092-8674(04)00045-5 · Cited by 34,714
BEDTools: a flexible suite of utilities for comparing genomic features
Quinlan, A. R., & Hall, I. M. (2010) · Bioinformatics · doi:10.1093/bioinformatics/btq033 · Cited by 27,437
Oncomirs—MicroRNAs with a role in cancer
Esquela-Kerscher, A., & Slack, F. J. (2006) · Nature Reviews Cancer · doi:10.1038/nrc1840 · Cited by 5,884
The nuclear RNase III Drosha initiates microRNA processing
Lee, Y., Ahn, C., Han, J., Choi, H., Kim, J., Yim, J., Lee, J., Provost, P., Rådmark, O., Kim, S., et al. (2003) · Nature · doi:10.1038/nature01957 · Cited by 5,299
miRBase: annotating high confidence microRNAs using deep sequencing data
Kozomara, A., & Griffiths-Jones, S. (2014) · Nucleic Acids Research · doi:10.1093/nar/gkt1181 · Cited by 5,073
MicroRNA therapeutics: towards a new era for the management of cancer and other diseases
Rupaimoole, R., & Slack, F. J. (2017) · Nature Reviews Drug Discovery · doi:10.1038/nrd.2016.246 · Cited by 4,741 · cited 1×
“In recent years, abundant evidence has shown that the deregulation of specific miRNA is associated with various diseases like cancers (2,4), cardiovascular diseases (5,6), and psychiatric disorders (7,8).”
Background
GREAT improves functional interpretation of cis-regulatory regions
McLean, C. Y., Bristor, D., Hiller, M., Clarke, S. L., Schaar, B. T., Lowe, C. B., Wenger, A. M., & Bejerano, G. (2010) · Nature Biotechnology · doi:10.1038/nbt.1630 · Cited by 4,502
A pathology atlas of the human cancer transcriptome
Uhlen, M., Zhang, C., Lee, S., Sjöstedt, E., Fagerberg, L., Bidkhori, G., Benfeitas, R., Arif, M., Liu, Z., Edfors, F., et al. (2017) · Science · doi:10.1126/science.aan2507 · Cited by 3,371
DisGeNET: a comprehensive platform integrating information on human disease-associated genes and variants
Piñero, J., Bravo, À., Queralt-Rosinach, N., Gutiérrez-Sacristán, A., Deu-Pons, J., Centeno, E., García-García, J., Sanz, F., & Furlong, L. I. (2017) · Nucleic Acids Research · doi:10.1093/nar/gkw943 · Cited by 2,680
A promoter-level mammalian expression atlas
The FANTOM Consortium and the RIKEN PMI and CLST (DGT) (2014) · Nature · doi:10.1038/nature13182 · Cited by 1,982

Flagged first, then by citations

Show all 49 references

Sorted by citations

Items 301–359 of 359

Identification of Potential miRNA-mRNA Regulatory Network in Denervated Muscular Atrophy by Bioinformatics AnalysisPreprint
Wang, J., Liu, Y., Zhang, Y., Liu, B., & Wei, Z. (2021) · Research Square · doi:10.21203/rs.3.rs-864617/v2
Self-citation
Peer Review #2 of "Identification of hub genes and transcription factor-miRNA-mRNA pathways in mice and human renal ischemia-reperfusion injury (v0.2)"
(2021) · PeerJ · doi:10.7287/peerj.12375v0.2/reviews/2
Peer Review #1 of "Identification of hub genes and transcription factor-miRNA-mRNA pathways in mice and human renal ischemia-reperfusion injury (v0.1)"
(2021) · PeerJ · doi:10.7287/peerj.12375v0.1/reviews/1
Peer Review #2 of "Identification of hub genes and transcription factor-miRNA-mRNA pathways in mice and human renal ischemia-reperfusion injury (v0.1)"
(2021) · PeerJ · doi:10.7287/peerj.12375v0.1/reviews/2
Identification and Functional Validation of Differentially Expressed microRNAs in Ascites-derived Ovarian Cancer Cells Compared With Primary Tumour TissuePreprint
Jiang, Y., Lyu, T., Liu, H., Shen, L., Shi, Y., Zhou, T., & Feng, W. (2020) · Research Square · doi:10.21203/rs.3.rs-51079/v2
Benchmarking of protein interaction databases for integration with manually reconstructed signaling network modelsPreprint
de Graaf, M. W. V., Eggertsen, T. G., Zeigler, A. C., Tan, P. M., & Saucerman, J. J. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.10.25.513640
Regulatory mechanisms of miRNA-21 in the progression of ischemia/reperfusion induced acute kidney injury to chronic kidney diseasePreprint
Fan, X., Su, J., Han, X., Zhao, J., Wang, Y., Zhang, L., & Zhou, X. (2022) · Research Square · doi:10.21203/rs.3.rs-2276033/v1
A novel seven-immune-genes-based model to improve prognosis prediction of clear cell renal cell carcinoma
Su, Q., Chen, P., Guo, H., You, Z., & Zhuang, W. (2022) · Archives of Medical Science · doi:10.5114/aoms/158531
Regulatory mechanisms of miRNA-21 in the progression of ischemia/reperfusion induced acute kidney injury to chronic kidney diseasePreprint
Fan, X., Su, J., Han, X., Zhao, J., Wang, Y., Zhang, L., & Zhou, X. (2023) · Research Square · doi:10.21203/rs.3.rs-2276033/v2
Identification of hsa-miR-4770 as characteristic microRNA of gallbladder carcinoma and exploration on the ceRNA regulation mechanism via integrated bioinformatics analysisPreprint
Shao, H., Zhu, J., Zhu, Y., Liu, L., Zhao, S., Kang, Q., Liu, Y., & Zou, H. (2023) · Research Square · doi:10.21203/rs.3.rs-2530466/v1
Combinatorial network of transcriptional and post-transcriptional regulation in amyotrophic lateral sclerosis
Kumar, R., Gauba, P., & Haider, S. (2024) · Journal of Applied Biology & Biotechnology · doi:10.7324/jabb.2024.156624
Identification of Ferroptosis-related Regulatory Network and Validation of the Expression of miRNA-326–IL-1β in Spinal Cord InjuryPreprint
Xu, S., Li, X., Gui, Y., Yang, Y., Chen, J., Jiang, Y., Pan, Q., & Xu, J. (2024) · Research Square · doi:10.21203/rs.3.rs-3867139/v1
Additional file 1 of Muscle miRNAs are influenced by sex at baseline and in response to exercise
Hiam, D., Landen, S., Jacques, M., Voisin, S. A. C., Lamon, S., & Eynon, N. (2023) · Figshare · doi:10.6084/m9.figshare.24647098
Predictive and Mechanistic Analysis of Dynamic 5-Node ceRNA Regulatory Network in Lung Adenocarcinoma
刘, 梦. (2024) · Hans Journal of Biomedicine · doi:10.12677/hjbm.2024.142030
Identification of miRNA-Target Gene-Transcription Factor Regulatory Network as Functional Motifs Involved in Glomerular Diabetic Nephropathy
Nuoroozi, G., Zareie, E., Yarizadeh, M., Ghadermarzi, P., Zali, H., & Molavi, Z. (2024) · Russian Journal of Genetics · doi:10.1134/s1022795424700261
Construction of Potential Transcription Factor-miRNA-mRNA Regulatory Network in Obesogenic Memory
Guan, B., Zhang, T., Chen, Y., & Yang, J. (2024) · Bariatric Surgical Practice and Patient Care · doi:10.1089/bari.2023.0042
Mechanistic Modeling the Role of MicroRNAs and Transcription Factors in Disease Progression
Bhat, G. S., & Mohammad, A. F. S. (2024) · Methods in molecular biology · doi:10.1007/978-1-0716-4290-0_9
A Regulatory Circuits Analysis Tool, “miRCuit,” Helps Reveal Breast Cancer Pathways: Toward Systems Medicine in Oncology
Karaoglu, B., & Dedeoğlu, B. G. (2025) · OMICS A Journal of Integrative Biology · doi:10.1089/omi.2024.0201
Identification of gene signatures and transcription factors associated with colorectal cancer through computational based approach
Mubarak, S. J., & Vedagiri, H. (2025) · Human Gene · doi:10.1016/j.humgen.2025.201405
Exploring molecular and modular insights into space ionizing radiation effects through heterogeneous gene regulatory networks
Yuan, M., Zhang, T., Liu, H., Min, L., Wang, Q., & Jiang, W. (2025) · npj Microgravity · doi:10.1038/s41526-025-00508-6 · Cited by 1
A Simultaneous Inhibition of ID1 and ID3 Protects Against Pulmonary FibrosisPreprint
Antar, S. A., Mensah, E., Dahlka, J., Aziz, M. M. A., Halouani, A., Imani, S., Parashar, A., Raslan, A., Benezra, R., Fraidenraich, D., et al. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.07.24.665373
Development of an EMT-related exosomal miRNA signature that can predict prognosis in hepatocellular carcinomaPreprint
Missaghimamaghani, O., Nehri, L. N., Bakhshi, S., Karaosmanoğlu, O., Sivas, H., Acar, A. C., & Banerjee, S. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.09.06.674651
Sex-specific microRNA regulators of Parkinson’s disease: insights from cohort-stratified simulations of compensatory pathway dynamicsPreprint
Hemedan, A. A., Ostaszewski, M., Rauschenberger, A., Pavelka, L., Klemmer, P., Glaab, E., Schneider, R., Krüger, R., & Satagopam, V. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.09.29.679145
From Genomics Alterations to Expression Dynamics: A Hierarchical Multi-Omics Integration Framework with gINTomicsPreprint
Velle, A., Patanè, F., & Romualdi, C. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.10.07.680960
ncFN: a comprehensive non-coding RNA function annotation framework based on a global and heterogeneous biomolecular network
Min, L., Liu, H., Yuan, M., Zhou, X., Zhang, T., Wang, Q., & Jiang, W. (2025) · Non-coding RNA Research · doi:10.1016/j.ncrna.2025.09.007
Identification of key regulators in Duchenne muscular dystrophy combinatorial network: a systems biology approach
Shashikala, S., Rani, V., & Haider, S. (2025) · Network Modeling Analysis in Health Informatics and Bioinformatics · doi:10.1007/s13721-025-00611-4
Interplay of Molecular Subtypes Associated with Butyrate Metabolism Elucidates Clinical Characteristics and Tumor Microenvironment in Prostate Cancer
Hao, G., Zan, Y., Zhou, X., Jia, Z., Wang, X., Yang, J., Dai, J., & Xiao, X. (2025) · Applied Biochemistry and Biotechnology · doi:10.1007/s12010-025-05476-x
Additional file 1 of nSARS-Cov-2, pulmonary edema and thrombosis: possible molecular insights using miRNA-gene circuits in regulatory networks
Khurana, P., Gupta, A., Sugadev, R., Sharma, Y. K., Varshney, R., Ganju, L., & Kumar, B. (2020) · Figshare · doi:10.6084/m9.figshare.13172969
Additional file 4 of A SRC-slug-TGFβ2 signaling axis drives poor outcomes in triple-negative breast cancers
Angel, C. Z., Beattie, S., Hanif, E. A. M., Ryan, M. P., Guerra Liberal, F. D. C., Zhang, S.-D., Monteith, S., Buckley, N. E., Parker, E., Haynes, S. J., et al. (2024) · Figshare · doi:10.6084/m9.figshare.27116040
Additional file 1 of Network analysis of synovial RNA sequencing identifies gene-gene interactions predictive of response in rheumatoid arthritis
Sciacca, E., Surace, A. E. A., Alaimo, S., Pulvirenti, A., Rivellese, F., Goldmann, K., Ferro, A., Latora, V., Pitzalis, C., & Lewis, M. (2022) · Figshare · doi:10.6084/m9.figshare.20295829
Additional file 10 of A SRC-slug-TGFβ2 signaling axis drives poor outcomes in triple-negative breast cancers
Angel, C. Z., Beattie, S., Hanif, E. A. M., Ryan, M. P., Guerra Liberal, F. D. C., Zhang, S.-D., Monteith, S., Buckley, N. E., Parker, E., Haynes, S. J., et al. (2024) · Figshare · doi:10.6084/m9.figshare.27116061
Additional file 7 of A SRC-slug-TGFβ2 signaling axis drives poor outcomes in triple-negative breast cancers
Angel, C. Z., Beattie, S., Hanif, E. A. M., Ryan, M. P., Guerra Liberal, F. D. C., Zhang, S.-D., Monteith, S., Buckley, N. E., Parker, E., Haynes, S. J., et al. (2024) · Figshare · doi:10.6084/m9.figshare.27116049
Additional file 3 of Muscle miRNAs are influenced by sex at baseline and in response to exercise
Hiam, D., Landen, S., Jacques, M., Voisin, S. A. C., Lamon, S., & Eynon, N. (2024) · Figshare · doi:10.6084/m9.figshare.26645845
Additional file 9 of A SRC-slug-TGFβ2 signaling axis drives poor outcomes in triple-negative breast cancers
Angel, C. Z., Beattie, S., Hanif, E. A. M., Ryan, M. P., Guerra Liberal, F. D. C., Zhang, S.-D., Monteith, S., Buckley, N. E., Parker, E., Haynes, S. J., et al. (2024) · Figshare · doi:10.6084/m9.figshare.27116058
Additional file 1 of A SRC-slug-TGFβ2 signaling axis drives poor outcomes in triple-negative breast cancers
Angel, C. Z., Beattie, S., Hanif, E. A. M., Ryan, M. P., Guerra Liberal, F. D. C., Zhang, S.-D., Monteith, S., Buckley, N. E., Parker, E., Haynes, S. J., et al. (2024) · Figshare · doi:10.6084/m9.figshare.27116028
Additional file 5 of A SRC-slug-TGFβ2 signaling axis drives poor outcomes in triple-negative breast cancers
Angel, C. Z., Beattie, S., Hanif, E. A. M., Ryan, M. P., Guerra Liberal, F. D. C., Zhang, S.-D., Monteith, S., Buckley, N. E., Parker, E., Haynes, S. J., et al. (2024) · Figshare · doi:10.6084/m9.figshare.27116043
Additional file 3 of A SRC-slug-TGFβ2 signaling axis drives poor outcomes in triple-negative breast cancers
Angel, C. Z., Beattie, S., Hanif, E. A. M., Ryan, M. P., Guerra Liberal, F. D. C., Zhang, S.-D., Monteith, S., Buckley, N. E., Parker, E., Haynes, S. J., et al. (2024) · Figshare · doi:10.6084/m9.figshare.27116037
Additional file 2 of Muscle miRNAs are influenced by sex at baseline and in response to exercise
Hiam, D., Landen, S., Jacques, M., Voisin, S. A. C., Lamon, S., & Eynon, N. (2023) · Figshare · doi:10.6084/m9.figshare.24647348
Additional file 8 of A SRC-slug-TGFβ2 signaling axis drives poor outcomes in triple-negative breast cancers
Angel, C. Z., Beattie, S., Hanif, E. A. M., Ryan, M. P., Guerra Liberal, F. D. C., Zhang, S.-D., Monteith, S., Buckley, N. E., Parker, E., Haynes, S. J., et al. (2024) · Figshare · doi:10.6084/m9.figshare.27116052
Additional file 1 of Identification of miRNA biomarkers for breast cancer by combining ensemble regularized multinomial logistic regression and Cox regression
Li, J., Zhang, H., & Gao, F. (2022) · Figshare · doi:10.6084/m9.figshare.21358173
Additional file 1 of DNI-MDCAP: improvement of causal MiRNA-disease association prediction based on deep network imputation
Han, Y., Zhou, Q., Liu, L., Li, J., & Zhou, Y. (2024) · Figshare · doi:10.6084/m9.figshare.24993640
Self-citation
Additional file 6 of A SRC-slug-TGFβ2 signaling axis drives poor outcomes in triple-negative breast cancers
Angel, C. Z., Beattie, S., Hanif, E. A. M., Ryan, M. P., Guerra Liberal, F. D. C., Zhang, S.-D., Monteith, S., Buckley, N. E., Parker, E., Haynes, S. J., et al. (2024) · Figshare · doi:10.6084/m9.figshare.27116046
Additional file 1 of In-silico engineering of RNA nanoplatforms to promote the diabetic wound healing
Beheshtizadeh, N., Salimi, A., Golmohammadi, M., Ansari, J. M., & Azami, M. (2024) · Figshare · doi:10.6084/m9.figshare.26595887
Additional file 2 of A SRC-slug-TGFβ2 signaling axis drives poor outcomes in triple-negative breast cancers
Angel, C. Z., Beattie, S., Hanif, E. A. M., Ryan, M. P., Guerra Liberal, F. D. C., Zhang, S.-D., Monteith, S., Buckley, N. E., Parker, E., Haynes, S. J., et al. (2024) · Figshare · doi:10.6084/m9.figshare.27116034
A Comprehensive Analysis of Transcription Factor-microRNA Network in Six Different Major Cancers: Uncovering the Regulatory Backstages of Cancer
Ramakrishnan, K., Ravi, V., Raju, R., Das, D., & Rehman, N. (2026) · OMICS A Journal of Integrative Biology · doi:10.1177/15578100251408262
Identification of Glycosylation-Related Biomarkers in COPD and IPF Through Integrated Machine Learning and WGCNA Analysis
Yin, X. L., Zhai, Y., & Wang, L. (2026) · Journal of Inflammation Research · doi:10.2147/jir.s537967
Graph neural network–based prediction of direct reprogramming factors using gene regulatory networks with microRNA-mediated regulationPreprint
Kawasaki, R., Takemoto, K., & Hamano, M. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.01.28.702229
WITHDRAWN: A Comprehensive Analysis of the Electrolytic Hydrogen Water Mechanism via a Feedforward Loop and its Functional Role in Intestinal Cells In VitroPreprint
LI, J. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.02.21.707209
Variation génétique et plasmatique des microARNs : impact sur les paramètres biologiques de l’hémostase
Thibord, F. (2019) · theses.fr (ABES) · doi:10.70675/01efeca0z30c7z4e8bza07cz61f3b56ea5cc
Diagnostic and Prognostic Utility of microRNA Dysregulation in Kidney Cancer
Mandal, S. D., Roy, S. S., Mukherjee, S., & Ghosh, B. (2026) · In Silico Research in Biomedicine · doi:10.1016/j.insi.2026.100309
Elucidation of putative key genes involved in the regulation of triple negative breast cancer development and progressionPreprint
Kumar, A., Upadhyay, G. S., Kashif, M., Malik, M. Z., Subbarao, N., & Rajala, M. S. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.04.15.718835
miRNA regulation, antitumor activity, and molecular modeling by Naproxen-1,2,4-triazole compound in human Breast cancer
Basaran, G. S., Sezer, G., Yıldırım, M., Aktaş, B., Amleh, A., & Han, M. İ. (2026) · Journal of Research in Pharmacy · doi:10.12991/jrespharm.1706713
Integrative multi-omics and experimental validation reveal UBE2C as a central hub gene and prognostic biomarker in hepatocellular carcinoma
Su, L., Zhang, O., Wang, Y., Lv, J., Liu, Q., Ma, M., Xu, L., & Pan, T. (2026) · International Immunopharmacology · doi:10.1016/j.intimp.2026.116866
Deciphering the Molecular Basis of Systemic Manifestations in Cutaneous Leishmaniasis Using Integrated Bioinformatics Analysis
Dehghan, Z., Zarei‐Behjani, Z., Rezaei, M., Arabi, H., Taherimoghadam, M., & Hatam, G. (2026) · Acta Parasitologica · doi:10.1007/s11686-026-01308-y
Construction and Analysis of miRNA Regulatory Networks
Avesani, S., Caligola, S., & Giugno, R. (2026) · Methods in molecular biology · doi:10.1007/978-1-0716-5392-0_7
Integrative AOP-Based Mechanistic Profiling of Organic UV Filters Reveals Shared Endocrine-Metabolic and Stress-Response Pathways
Accardi, C., Al-Abdulraheem, Z., Schaffert, A., Federico, A., del Giudice, G., Serra, A., & Greco, D. (2026) · Toxicology · doi:10.1016/j.tox.2026.154583
Temporal Dynamics of Dorsal-Striatal Protein Networks During Radial-Arm Maze Training
Gutman, S., Borovok, N., Kirby, M. L., Levin, Y., & Michaelevski, I. (2026) · Neuroinformatics · doi:10.1007/s12021-026-09819-9
Targeting the miR-3156-3p/K+ channel axis suppresses viral release and paralysis in enterovirus D68 infection
Lin, Y.-T., Kung, Y.-A., Gong, Y., Chiang, H.-J., Hsiung, K. C., Lee, H.-C., Tang, W.-F., Huang, C.-G., Horng, J., & Shih, S. (2026) · Molecular Therapy · doi:10.1016/j.ymthe.2026.09.038
miR-127-5p Exacerbates Corneal Epithelial Inflammation and Oxidative Stress by Targeting FBXW11: Novel Insights into the Pathogenesis of Dry Eye Disease
Xiang, D., Gu, R., Zhou, L., & Wang, D. (2026) · Experimental Eye Research · doi:10.1016/j.exer.2026.111296

Authors: Tong, Z., Cui, Q., Wang, J., & Zhou, Y.
Affiliations: Peking University (CN)
Corresponding author: Wang, Juan
Zhou, Yuan
ORCID: Tong, Zhan
Cui, Qinghua
Wang, Juan
Zhou, Yuan
Venue: Nucleic Acids Research
Publisher: Oxford University Press (OUP)
Published: 2018-10-29
Volume: 47
Issue: D1
Pages: D253-D258
ISSN: 0305-1048 (ISSN-L)
1362-4962
DOI: 10.1093/nar/gky1023
PMC: PMC6323981
OpenAlex: W2899510892
Cited by: 356
References: 49
Citations by year: 2026: 17 · 2025: 37 · 2024: 56 · 2023: 52 · 2022: 60 · 2021: 66 · 2020: 49 · 2019: 18
FWCI: 10.05
Type: journal article
Language: en
Open access: gold
doi.org
Licence: cc-by
creativecommons.org/licenses/by/4.0/ (vor)
Locations: Nucleic Acids Research (publishedVersion, cc-by)
Europe PMC (PubMed Central) (publishedVersion, cc-by)
PubMed Central (publishedVersion, cc-by)
PubMed (publishedVersion)
Funders: National Natural Science Foundation of China (81473236)
Special Project on Precision Medicine under the National Key R&D Program (2016YFC0903000)
Fundamental Research Funds for Central Universities of China (BMU2017YJ004)
Topics: Life Sciences › Biochemistry, Genetics and Molecular Biology › Cancer Research › MicroRNA in disease regulation
Life Sciences › Agricultural and Biological Sciences › Plant Science › Plant Molecular Biology Research
Life Sciences › Biochemistry, Genetics and Molecular Biology › Cancer Research › Cancer-related molecular mechanisms research
Keywords: microRNA regulation, transcription factors, post-transcriptional regulation, tissue-specific gene regulation, ChIP-seq, enrichment analysis
MeSH terms: Gene Expression Regulation, Humans, Software, Software Design, Transcription Factors, User-Computer Interface, Computational Biology, Genomics, Databases, Genetic, MicroRNAs

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

TransmiR v2.0: an updated transcription factor-microRNA regulation database

Abstract

Overview

Integrity signals

Verification

Claims the paper makes

Claims other papers make about this one

Self-check

Citations

Cites

Cited by

Related

Metadata

Record

About these checks