Evidence map›Paper›PMID 37370938›Full record

ArticleDiagnostics (Basel, Switzerland)2023

NeuPD-A Neural Network-Based Approach to Predict Antineoplastic Drug Response.

Muhammad Shahzad, Muhammad Atif Tahir, Musaed Alhussein, Ansharah Mobin, Rauf Ahmed Shams Malick, Muhammad Shahid Anwar

Abstract read
In one paragraph

Article in Diagnostics (Basel, Switzerland), 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. 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

6 authors.

Muhammad ShahzadFAST School of Computing, National University of Computer and Emerging Sciences (NUCES-FAST), Karachi 75030, Pakistan.ORCID 0000-0002-1031-552X
Muhammad Atif TahirFAST School of Computing, National University of Computer and Emerging Sciences (NUCES-FAST), Karachi 75030, Pakistan.ORCID 0000-0003-1366-8408
Musaed AlhusseinDepartment of Computer Engineering, College of Computer and Information Sciences, King Saud University, P.O. Box 51178, Riyadh 11543, Saudi Arabia.
Ansharah MobinFAST School of Computing, National University of Computer and Emerging Sciences (NUCES-FAST), Karachi 75030, Pakistan.
Rauf Ahmed Shams MalickFAST School of Computing, National University of Computer and Emerging Sciences (NUCES-FAST), Karachi 75030, Pakistan.ORCID 0000-0002-0844-4883
Muhammad Shahid AnwarDepartment of AI and Software, Gachon University, Seongnam-si 13120, Republic of Korea.ORCID 0000-0001-8093-6690

Funding

King Saud University RSPD2023R553
6 · The paper itself

Abstract

With the beginning of the high-throughput screening, in silico-based drug response analysis has opened lots of research avenues in the field of personalized medicine. For a decade, many different predicting techniques have been recommended for the antineoplastic (anti-cancer) drug response, but still, there is a need for improvements in drug sensitivity prediction. The intent of this research study is to propose a framework, namely

Indexed as

cell linesdrug response predictiongene expressionmachine learningNeuPDneural networksXGBoost

Identifiers

PMID37370938
PMCPMC10297062

What OpenQuestion holds

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LicenceCC BY
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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.