A machine learning-based gene signature of response to the novel alkylating agent LP-184 distinguishes its potential tumor indications

A machine learning-based gene signature of response to the novel alkylating agent LP-184 distinguishes its potential tumor indications

Background: Non-targeted cytotoxics with anticancer activity are often developed through preclinical stages using response criteria observed in cell lines and xenografts. A panel of the NCI-60 cell lines is frequently the first line to define tumor types that are optimally responsive. Open data on t...

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Bibliographic Details
Main Authors: Bhatia, K. (Author), Biyani, N. (Author), Carr, P. (Author), Kathad, U. (Author), Kulkarni, A. (Author), McDermott, J.R (Author), Modali, R. (Author), Richard, J.-P (Author), Sharma, P. (Author), Wegner, J. (Author)
Format: Article
Language:English
Published: BioMed Central Ltd 2021
Subjects:
Acylfulvene
alkylating agent
Alkylating agents
Alkylating Agents
Anticancer activities
antineoplastic agent
Antineoplastic Agents
biological marker
Biomarker
Biomarkers
Cancer
Cell culture
Cell Line, Tumor
Drug products
Gene expression
Gene Expression Data
Gene expression patterns
Gene signature
human
Humans
LP-184
machine learning
Machine learning
Machine Learning
Mechanism of action
Mutagens
neoplasm
Neoplasms
Open Data
Performance measure
Preclinical development
PTGR1
Regression analysis
Response prediction
Response signatures
tumor cell line
Tumors
Turing machines
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