Evidence map›Paper›PMID 36869839›Full record

ArticleDatabase : the journal of biological databases and curation2023

lncHUB2: aggregated and inferred knowledge about human and mouse lncRNAs.

Giacomo B Marino, Megan L Wojciechowicz, Daniel J B Clarke, Maxim V Kuleshov, Zhuorui Xie, Minji Jeon, Alexander Lachmann, Avi Ma'ayan

Abstract read
In one paragraph

Article in Database : the journal of biological databases and curation, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

8 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. lncRNAlyzr: Enrichment Analysis for lncRNA Sets.Journal of molecular biology · 2025
    Article
  5. Article
  6. Article
  7. Article
  8. Frontiers in immunology · 2024
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Giacomo B MarinoDepartment of Pharmacological Sciences, Department of Artificial Intelligence and Human Health, Mount Sinai Center for Bioinformatics, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, Box 1603, New York, NY 10029, USA.
Megan L WojciechowiczDepartment of Pharmacological Sciences, Department of Artificial Intelligence and Human Health, Mount Sinai Center for Bioinformatics, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, Box 1603, New York, NY 10029, USA.
Daniel J B ClarkeDepartment of Pharmacological Sciences, Department of Artificial Intelligence and Human Health, Mount Sinai Center for Bioinformatics, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, Box 1603, New York, NY 10029, USA.
Maxim V KuleshovDepartment of Pharmacological Sciences, Department of Artificial Intelligence and Human Health, Mount Sinai Center for Bioinformatics, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, Box 1603, New York, NY 10029, USA.ORCID 0000-0002-7812-7752
Zhuorui XieDepartment of Pharmacological Sciences, Department of Artificial Intelligence and Human Health, Mount Sinai Center for Bioinformatics, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, Box 1603, New York, NY 10029, USA.
Minji JeonDepartment of Pharmacological Sciences, Department of Artificial Intelligence and Human Health, Mount Sinai Center for Bioinformatics, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, Box 1603, New York, NY 10029, USA.
Alexander LachmannDepartment of Pharmacological Sciences, Department of Artificial Intelligence and Human Health, Mount Sinai Center for Bioinformatics, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, Box 1603, New York, NY 10029, USA.ORCID 0000-0002-1982-7652
Avi Ma'ayanDepartment of Pharmacological Sciences, Department of Artificial Intelligence and Human Health, Mount Sinai Center for Bioinformatics, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, Box 1603, New York, NY 10029, USA.

Funding

Teaching biomedical and pharmacological trainees to produce FAIR data for AI & ML applicationsT32GM062754 · NIGMS · MOUNT SINAI SCHOOL OF MEDICINE OF NYU · PI SCHLESSINGER, AVNER · 2001 to 2023
$6.2M
Elucidating the Molecular Mechanisms that Mediate DKD Progression in Patients Living with HIVR01DK131525 · NIDDK · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · PI John Cijiang He, Avi Ma'ayan · 2022 to 2026
$4.2M
ARCHS4: Massive Mining of Publicly Available RNA Sequencing DataU24CA264250 · NCI · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · PI Avi Ma'ayan · 2022 to 2026
$4.1M
The LINCS DCIC Engagement Plan with the CFDEOT2OD030160 · OD · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · PI MA'AYAN, AVI · 2020 to 2024
$3.4M
Diabetes Data and Hypothesis Hub (D2H2)RC2DK131995 · NIDDK · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · PI ATTIE, ALAN D, MA'AYAN, AVI · 2022 to 2023
$2.1M
Knowledge Management Center for Illuminating the Druggable GenomeU24CA224260 · NCI · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · PI MA'AYAN, AVI · 2018 to 2023
$1.5M
NCI NIH HHS U24 CA224260NCI NIH HHS U24 CA264250NIDDK NIH HHS R01 DK131525NIDDK NIH HHS RC2 DK131995NIGMS NIH HHS T32 GM062754NIH HHS OT2 OD030160NIH HHS OT2-OD030160NIH HHS R01-DK131525NIH HHS RC2-DK131995NIH HHS T32-GM062754NIH HHS U24-CA224260NIH HHS U24-CA264250
6 · The paper itself

Abstract

Long non-coding ribonucleic acids (lncRNAs) account for the largest group of non-coding RNAs. However, knowledge about their function and regulation is limited. lncHUB2 is a web server database that provides known and inferred knowledge about the function of 18 705 human and 11 274 mouse lncRNAs. lncHUB2 produces reports that contain the secondary structure fold of the lncRNA, related publications, the most correlated coding genes, the most correlated lncRNAs, a network that visualizes the most correlated genes, predicted mouse phenotypes, predicted membership in biological processes and pathways, predicted upstream transcription factor regulators, and predicted disease associations. In addition, the reports include subcellular localization information; expression across tissues, cell types, and cell lines, and predicted small molecules and CRISPR knockout (CRISPR-KO) genes prioritized based on their likelihood to up- or downregulate the expression of the lncRNA. Overall, lncHUB2 is a database with rich information about human and mouse lncRNAs and as such it can facilitate hypothesis generation for many future studies. The lncHUB2 database is available at https://maayanlab.cloud/lncHUB2. Database URL: https://maayanlab.cloud/lncHUB2.

Indexed as

RNA, Long NoncodingAnimalsCell LineClustered Regularly Interspaced Short Palindromic RepeatsDatabases, FactualHumansKnowledgeMiceRNA, Long Noncoding

Identifiers

PMID36869839
PMCPMC9985331

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.