Evidence map›Paper›PMID 35524844›Full record

ArticleMedical & biological engineering & computing2022

Non-swarm-based computational approach for mining cancer drug target modules in protein interaction network.

R Gowri, R Rathipriya

Abstract read
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In one paragraph

Article in Medical & biological engineering & computing, 2022. 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

2 authors.

R GowriDepartment of Computer Science, Periyar University, Tamilnadu, Salem, India.
R RathipriyaDepartment of Computer Science, Periyar University, Tamilnadu, Salem, India. rathi_priyar@periyaruniversity.ac.in.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Cancer is a lethal disease that drew the entire world over the past decades. Currently, numerous researches focused on these cancer treatments. Most familiar among them is the targeted therapy; a customized treatment type depends on the cancer drug targets. Further, the selection of targets is a quite sensitive task. The computational approaches are lagging in this field. This paper is intended to propose an optimized multi-functional score-based co-clustering with MapReduce (MR-CoC

Indexed as

Antineoplastic AgentsNeoplasmsAlgorithmsCluster AnalysisComputational BiologyProtein Interaction MappingProtein Interaction MapsAntineoplastic AgentsBWSCancer DTMFunctional moduleMRCoCProtein complex coverage

Identifiers

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Registered trials

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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.