Evidence map›Paper›PMID 32710376›Full record

ArticleMedical & biological engineering & computing2020

BIOINTMED: integrated biomedical knowledge base with ontologies and clinical trials.

Ankita Saha, Jayanta Mukhopadhyay, Sudeshna Sarkar, Mahanandeeshwar Gattu

Abstract read
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In one paragraph

Article in Medical & biological engineering & computing, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Ankita SahaAdvanced Technology Development Centre, Indian Institute of Technology Kharagpur, Kharagpur, India. ankitasaha@iitkgp.ac.in.ORCID http://orcid.org/0000-0003-3925-9370
Jayanta MukhopadhyayDepartment of Computer Science and Engineering, Indian Institute of Technology Kharagpur, Kharagpur, India.
Sudeshna SarkarDepartment of Computer Science and Engineering, Indian Institute of Technology Kharagpur, Kharagpur, India.
Mahanandeeshwar GattuExcelra Knowledge Solutions Pvt Ltd, Hyderabad, India.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Biomedical data are complex and heterogeneous. An ample reliable quantity of data is important for understanding and exploring the domain. The work aims to integrate biomedical data from various heterogeneous sources like dictionaries or corpus and amalgamate them into a uniform format for easier access by the end-user like biologist, pharmacist, and data scientist. The proposed integrated biomedical knowledge base, BIOINTMED, has 11,299, 12,981, 4428, 61,491, 48,663, and 13,146 unique entities for drugs, diseases, targets, genes, biomedical pathways, and adverse events, respectively. The uniform aggregated collection is also explored to study the interaction among these entity pairs. Finally, a complete statistical analysis of the consolidated biomedical entities is provided.

Indexed as

Clinical Trials as TopicDrug-Related Side Effects and Adverse ReactionsKnowledge BasesBiological OntologiesDatabases, ChemicalDatabases, PharmaceuticalDatabases, ProteinDrug InteractionsHumansModels, StatisticalToxicogeneticsBiomedical integrated systemBiomedical knowledge baseClinical trialsData integrationDictionaries

Identifiers

PMID32710376

What OpenQuestion holds

Textmetadata
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.