Evidence map›Paper›PMID 38031782›Full record

ReviewCombinatorial chemistry & high throughput screening2024

Computational Approaches: A New Frontier in Cancer Research.

Shubham Srivastava, Pushpendra Jain

Abstract readReview
PubMed Publisher
In one paragraph

Review in Combinatorial chemistry & high throughput screening, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Review
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.

Shubham SrivastavaDepartment of Pharmacy, IIMT College of Pharmacy, Uttar Pradesh, 201310, India.ORCID 0000-0001-6331-0450
Pushpendra JainDepartment of Pharmacy, IIMT College of Pharmacy, Uttar Pradesh, 201310, India.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Cancer is a broad category of disease that can start in virtually any organ or tissue of the body when aberrant cells assault surrounding organs and proliferate uncontrollably. According to the most recent statistics, cancer will be the cause of 10 million deaths worldwide in 2020, accounting for one death out of every six worldwide. The typical approach used in anti-cancer research is highly time-consuming and expensive, and the outcomes are not particularly encouraging. Computational techniques have been employed in anti-cancer research to advance our understanding. Recent years have seen a significant and exceptional impact on anticancer research due to the rapid development of computational tools for novel drug discovery, drug design, genetic studies, genome characterization, cancer imaging and detection, radiotherapy, cancer metabolomics, and novel therapeutic approaches. In this paper, we examined the various subfields of contemporary computational techniques, including molecular docking, artificial intelligence, bioinformatics, virtual screening, and QSAR, and their applications in the study of cancer.

Indexed as

Antineoplastic AgentsComputational BiologyNeoplasmsArtificial IntelligenceDrug DiscoveryHumansMolecular Docking SimulationQuantitative Structure-Activity RelationshipAntineoplastic Agentsartificial intelligencebioinformaticscancercancer diagnosis.Computational approachmachine learningQSARvirtual screening

Identifiers

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.