AIMC Topic: Antineoplastic Agents
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Deep generative neural network for accurate drug response imputation.
Nature communications
Mar 19, 2021
Drug response differs substantially in cancer patients due to inter- and intra-tumor heterogeneity. Particularly, transcriptome context, especially tumor microenvironment, has been shown playing a significant role in shaping the actual treatment outc...
A machine learning-based gene signature of response to the novel alkylating agent LP-184 distinguishes its potential tumor indications.
BMC bioinformatics
Mar 2, 2021
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 t...
Artificial intelligence, machine learning, and drug repurposing in cancer.
Expert opinion on drug discovery
Feb 12, 2021
: Drug repurposing provides a cost-effective strategy to re-use approved drugs for new medical indications. Several machine learning (ML) and artificial intelligence (AI) approaches have been developed for systematic identification of drug repurposin...
TranSynergy: Mechanism-driven interpretable deep neural network for the synergistic prediction and pathway deconvolution of drug combinations.
PLoS computational biology
Feb 12, 2021
Drug combinations have demonstrated great potential in cancer treatments. They alleviate drug resistance and improve therapeutic efficacy. The fast-growing number of anti-cancer drugs has caused the experimental investigation of all drug combinations...
Robotic chemotherapy compounding: A multicenter productivity approach.
Journal of oncology pharmacy practice : official publication of the International Society of Oncology Pharmacy Practitioners
Feb 11, 2021
INTRODUCTION: The aim of this study is to compare productivity of the KIRO Oncology compounding robot in three hospital pharmacy departments and identify the key factors to predict and optimize automatic compounding time.
Artificial immune system features added to breast cancer clinical data for machine learning (ML) applications.
Bio Systems
Jan 19, 2021
We here propose a new method of combining a mathematical model that describes a chemotherapy treatment for breast cancer with a machine-learning (ML) algorithm to increase performance in predicting tumor size using a five-step procedure. The first st...
Network-based drug sensitivity prediction.
BMC medical genomics
Dec 28, 2020
BACKGROUND: Drug sensitivity prediction and drug responsive biomarker selection on high-throughput genomic data is a critical step in drug discovery. Many computational methods have been developed to serve this purpose including several deep neural n...
Assessing Drug Development Risk Using Big Data and Machine Learning.
Cancer research
Dec 22, 2020
Identifying new drug targets and developing safe and effective drugs is both challenging and risky. Furthermore, characterizing drug development risk, the probability that a drug will eventually receive regulatory approval, has been notoriously hard ...
Development and Validation of a Machine Learning Model to Explore Tyrosine Kinase Inhibitor Response in Patients With Stage IV EGFR Variant-Positive Non-Small Cell Lung Cancer.
JAMA network open
Dec 1, 2020
IMPORTANCE: An end-to-end efficacy evaluation approach for identifying progression risk after epidermal growth factor receptor (EGFR)-tyrosine kinase inhibitor (TKI) therapy in patients with stage IV EGFR variant-positive non-small cell lung cancer (...
Leveraging multi-way interactions for systematic prediction of pre-clinical drug combination effects.
Nature communications
Dec 1, 2020
We present comboFM, a machine learning framework for predicting the responses of drug combinations in pre-clinical studies, such as those based on cell lines or patient-derived cells. comboFM models the cell context-specific drug interactions through...
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