Evidence map›Paper›PMID 37768281›Full record

ArticleDatabase : the journal of biological databases and curation2023

Building a large gene expression-cancer knowledge base with limited human annotations.

Stefano Marchesin, Laura Menotti, Fabio Giachelle, Gianmaria Silvello, Omar Alonso

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 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. Building a large gene expression-cancer knowledge base with limited human annotations.Database : the journal of biological databases and curation · 2023
    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

5 authors.

Stefano MarchesinDepartment of Information Engineering, University of Padova, Via G. Gradenigo 6b, Padova 35131, Italy.ORCID 0000-0003-0362-5893
Laura MenottiDepartment of Information Engineering, University of Padova, Via G. Gradenigo 6b, Padova 35131, Italy.ORCID 0000-0002-0676-682X
Fabio GiachelleDepartment of Information Engineering, University of Padova, Via G. Gradenigo 6b, Padova 35131, Italy.ORCID 0000-0001-5015-5498
Gianmaria SilvelloDepartment of Information Engineering, University of Padova, Via G. Gradenigo 6b, Padova 35131, Italy.ORCID 0000-0003-4970-4554
Omar AlonsoApplied Science, Amazon, 3075 Olcott St., Santa Clara, California 95054, USA.ORCID 0009-0009-2515-4771

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Cancer prevention is one of the most pressing challenges that public health needs to face. In this regard, data-driven research is central to assist medical solutions targeting cancer. To fully harness the power of data-driven research, it is imperative to have well-organized machine-readable facts into a knowledge base (KB). Motivated by this urgent need, we introduce the Collaborative Oriented Relation Extraction (CORE) system for building KBs with limited manual annotations. CORE is based on the combination of distant supervision and active learning paradigms and offers a seamless, transparent, modular architecture equipped for large-scale processing. We focus on precision medicine and build the largest KB on 'fine-grained' gene expression-cancer associations-a key to complement and validate experimental data for cancer research. We show the robustness of CORE and discuss the usefulness of the provided KB. Database URL https://zenodo.org/record/7577127.

Indexed as

NeoplasmsDatabases, FactualGene ExpressionHumansKnowledge BasesPrecision Medicine

Identifiers

PMID37768281
PMCPMC10533344

What Socratic 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 Socratic graph.