Unraveling the complexity of the senescence-associated secretory phenotype in adamantinomatous craniopharyngioma using multimodal machine learning analysis

Prince, E. W., Apps, J. R., Jeang, J., Chee, K., Medlin, S., Jackson, E. M., Dudley, R., Limbrick, D., Naftel, R., Johnston, J., Feldstein, N., Prolo, L. M., Ginn, K., Niazi, T., Smith, A., Kilburn, L., Chern, J., Leonard, J., Lam, S., … Hankinson, T. C. (2024). Unraveling the complexity of the senescence-associated secretory phenotype in adamantinomatous craniopharyngioma using multimodal machine learning analysis. Neuro-Oncology, 26(6), 1109–1123. https://doi.org/10.1093/neuonc/noae015
Authors:
Eric W Prince
John R Apps
John Jeang
Keanu Chee
Stephen Medlin
Eric M Jackson
Roy Dudley
David Limbrick
Robert Naftel
James Johnston
Neil Feldstein
Laura M Prolo
Kevin Ginn
Toba Niazi
Amy Smith
Lindsay Kilburn
Joshua Chern
Jeffrey Leonard
Sandi Lam
David S Hersh
Jose Mario Gonzalez-Meljem
Vladimir Amani
Andrew M Donson
Siddhartha S Mitra
Pratiti Bandopadhayay
Juan Pedro Martinez-Barbera
Todd C Hankinson
Affiliated Authors:
Neil Feldstein
Columbia Affiliation:
Subjects:
Craniopharyngioma (MeSH)
Pituitary Neoplasms (MeSH)
Cellular Senescence (MeSH)
Machine Learning (MeSH)
Author Keywords:
adamantinomatous craniopharyngioma (acp)
machine learning
next generation sequencing
pediatric neuro-oncology
senescence-associated secretory phenotype (sasp)
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Publication Type:
Article
Unique ID:
10.1093/neuonc/noae015
PMID:
38334125
DOI:
10.1093/neuonc/noae015
Journal:
Neuro-Oncology
Publication Date:
Data Source:
PubMed
Source Link:
View PubMed Record
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Record Created: