Evidence map›Paper›PMID 40109372›Full record

ArticleProceedings. IEEE International Conference on Healthcare Informatics2024

Learning to Rank Complex Biomedical Hypotheses for Accelerating Scientific Discovery.

Juncheng Ding, Shailesh Dahal, Bijaya Adhikari, Kishlay Jha

Abstract read
In one paragraph

Article in Proceedings. IEEE International Conference on Healthcare Informatics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Article
  3. Semantic Knowledge Augmented Hypergraph Contrastive Representation Learning for Zero-Shot Biomedical Text Classification.Advances in knowledge discovery and data mining : ... Pacific-Asia Conference, PAKDD ..., proceedings. Pacific-Asia Conference on Knowledge Discovery and Data Mining · 2025
    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

4 authors.

Juncheng DingUniversity of North Texas, Denton, TX, USA.
Shailesh DahalUniversity of Iowa, Iowa City, IA, USA.
Bijaya AdhikariUniversity of Iowa, Iowa City, IA, USA.
Kishlay JhaUniversity of Iowa, Iowa City, IA, USA.

Funding

Continually Adaptive Machine Learning Platform for Personalized Biomedical Literature Curation and ExplorationR01LM014012 · NLM · UNIVERSITY OF VIRGINIA · PI TIMOTHY W CLARK, Nathan Sheffield · 2023 to 2026
$1.3M
NCEZID CDC HHS U01 CK000594NLM NIH HHS R01 LM014012
6 · The paper itself

Abstract

Hypothesis generation (HG) is a fundamental problem in biomedical text mining that uncovers plausible implicit links (

Indexed as

biomedical text mininggraph neural networkshypothesis generationself-supervised learning

Identifiers

PMID40109372
PMCPMC11920884

What OpenQuestion holds

Textmetadata
LicenceTDM
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.