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Drug Resistance, Neoplasm

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Towards rapid prediction of drug-resistant cancer cell phenotypes: single cell mass spectrometry combined with machine learning.

Chemical communications (Cambridge, England)
Combined single cell mass spectrometry and machine learning methods is demonstrated for the first time to achieve rapid and reliable prediction of the phenotype of unknown single cells based on their metabolomic profiles, with experimental validation...

PLATYPUS: A Multiple-View Learning Predictive Framework for Cancer Drug Sensitivity Prediction.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
Cancer is a complex collection of diseases that are to some degree unique to each patient. Precision oncology aims to identify the best drug treatment regime using molecular data on tumor samples. While omics-level data is becoming more widely availa...

PANOPLY: Omics-Guided Drug Prioritization Method Tailored to an Individual Patient.

JCO clinical cancer informatics
PURPOSE: The majority of patients with cancer receive treatments that are minimally informed by omics data. We propose a precision medicine computational framework, PANOPLY (Precision Cancer Genomic Report: Single Sample Inventory), to identify and p...

Machine learning identifies a core gene set predictive of acquired resistance to EGFR tyrosine kinase inhibitor.

Journal of cancer research and clinical oncology
PURPOSE: Acquired resistance (AR) to epidermal growth factor receptor tyrosine kinase inhibitors (EGFR-TKIs) is a major issue worldwide, for both patients and healthcare providers. However, precise prediction is currently infeasible due to the lack o...

A 29-gene and cytogenetic score for the prediction of resistance to induction treatment in acute myeloid leukemia.

Haematologica
Primary therapy resistance is a major problem in acute myeloid leukemia treatment. We set out to develop a powerful and robust predictor for therapy resistance for intensively treated adult patients. We used two large gene expression data sets (n=856...

A machine learning approach to integrate big data for precision medicine in acute myeloid leukemia.

Nature communications
Cancers that appear pathologically similar often respond differently to the same drug regimens. Methods to better match patients to drugs are in high demand. We demonstrate a promising approach to identify robust molecular markers for targeted treatm...

MRI to MGMT: predicting methylation status in glioblastoma patients using convolutional recurrent neural networks.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
Glioblastoma Multiforme (GBM), a malignant brain tumor, is among the most lethal of all cancers. Temozolomide is the primary chemotherapy treatment for patients diagnosed with GBM. The methylation status of the promoter or the enhancer regions of the...