In one paragraphArticle in Biomedicines, 2024. 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, top 97% of its field
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 registryThe 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 literatureWho cites it
0 citing papers in PubMed, 0 citations in OpenAlex.
No citing paper in PubMed yet.
4 · The recordCorrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
5 · Who and what moneyAuthors and funding
9 authors at 4 institutions in 1 country.
Iván SalgadoMedical Robotics and Biosignals Laboratory, Centro de Innovación y Desarrollo Tecnológico en Cómputo, Instituto Politécnico Nacional (IPN), Mexico City 07700, Mexico.ORCID 0000-0002-3854-7031 Ernesto Prado Montes de OcaRegulatory SNPs Laboratory, Personalized Medicine National Laboratory (LAMPER), Guadalajara Unit, Medical and Pharmaceutical Biotechnology Department, Research Center in Technology and Design Assistance of Jalisco State (CIATEJ), National Council of Science and Technology (CONACYT), Guadalajara 44270, Jalisco, Mexico.ORCID 0000-0002-0617-4752 Isaac ChairezTecnologico de Monterrey, Institute of Advanced Materials for Sustainable Manufacturing, Monterrey 64849, Jalisco, Mexico.ORCID 0000-0002-7157-2052 Luis Figueroa-YáñezIndustrial Biotechnology Unit, Center for Research and Assistance in Technology and Design of the State of Jalisco, A.C. (CIATEJ), Guadalajara 44270, Jalisco, Mexico.ORCID 0000-0002-7283-8035 Alejandro Pereira-SantanaIndustrial Biotechnology Unit, Center for Research and Assistance in Technology and Design of the State of Jalisco, A.C. (CIATEJ), Guadalajara 44270, Jalisco, Mexico.ORCID 0000-0001-7714-9485 Andrés Rivera ChávezRegulatory SNPs Laboratory, Personalized Medicine National Laboratory (LAMPER), Guadalajara Unit, Medical and Pharmaceutical Biotechnology Department, Research Center in Technology and Design Assistance of Jalisco State (CIATEJ), National Council of Science and Technology (CONACYT), Guadalajara 44270, Jalisco, Mexico.
Jesús Bernardino Velázquez-FernandezConsejo Nacional de Ciencia y Tecnología (CONACYT), Av. Insurgentes sur 1582, Alcaldía Benito Juárez, Mexico City 03940, Mexico.ORCID 0000-0002-4747-812X Teresa Alvarado ParraRegulatory SNPs Laboratory, Personalized Medicine National Laboratory (LAMPER), Guadalajara Unit, Medical and Pharmaceutical Biotechnology Department, Research Center in Technology and Design Assistance of Jalisco State (CIATEJ), National Council of Science and Technology (CONACYT), Guadalajara 44270, Jalisco, Mexico.ORCID 0000-0002-7802-4040 Adriana VallejoUnidad de Biotecnología Médica y Farmacéutica, CONACYT-Centro de Investigación y Asistencia en Tecnologia y Diseño del Estado de Jalisco AC, Av. Normalistas 800, Colinas de la Normal, Guadalajara 44270, Jalisco, Mexico.ORCID 0000-0002-4568-3787 Secretaría de Ciencia, Humanidades, Tecnología e Innovación · MXSecretaría de Salud de Jalisco · MXInstituto Politécnico Nacional · MXTecnológico de Monterrey · MX
Funding
Consejo Nacional de Humanidades, Ciencia y Tecnología (México) 320792202
6 · The paper itselfAbstract
The molecular explanation about why some pancreatic cancer (PaCa) patients die early and others die later is poorly understood. This study aimed to discover potential novel markers and drug targets that could be useful to stratify and extend expected survival in prospective early-death patients. We deployed a deep learning algorithm and analyzed the gene copy number, gene expression, and protein expression data of death versus alive PaCa patients from the GDC cohort. The genes with higher relative amplification (copy number >4 times in the dead compared with the alive group) were
Indexed as
biomarkersdeep learninggene copy numbergene expressionlethalitypancreatic cancer
Identifiers
PMID38397997
PMCPMC11154313
OpenAlexW4391642858
What OpenQuestion holds
Textmetadata
LicenceCC BY
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