AIMC Topic: Drug Resistance, Neoplasm

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Drug sensitivity prediction from cell line-based pharmacogenomics data: guidelines for developing machine learning models.

Briefings in bioinformatics Nov 5, 2021
The goal of precision oncology is to tailor treatment for patients individually using the genomic profile of their tumors. Pharmacogenomics datasets such as cancer cell lines are among the most valuable resources for drug sensitivity prediction, a cr...
Cell Line, Tumor Datasets as Topic Drug Resistance, Neoplasm Humans Machine Learning Algorithms Pharmacogenetics
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Integration and interplay of machine learning and bioinformatics approach to identify genetic interaction related to ovarian cancer chemoresistance.

Briefings in bioinformatics Nov 5, 2021
Although chemotherapy is the first-line treatment for ovarian cancer (OCa) patients, chemoresistance (CR) decreases their progression-free survival. This paper investigates the genetic interaction (GI) related to OCa-CR. To decrease the complexity of...
Humans Machine Learning Female Ovarian Neoplasms Drug Resistance, Neoplasm Databases, Nucleic Acid Gene Expression Profiling Gene Regulatory Networks Gene Expression Regulation, Neoplastic
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Prediction of Chemosensitivity in Multiple Primary Cancer Patients Using Machine Learning.

Anticancer research May 1, 2021
BACKGROUND/AIM: Many cancer patients face multiple primary cancers. It is challenging to find an anticancer therapy that covers both cancer types in such patients. In personalized medicine, drug response is predicted using genomic information, which ...
Gene Expression Regulation, Neoplastic Drug Resistance, Neoplasm Algorithms Neoplasms Humans Antineoplastic Agents Machine Learning
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Deep neural networks identify signaling mechanisms of ErbB-family drug resistance from a continuous cell morphology space.

Cell reports Jan 19, 2021
It is well known that the development of drug resistance in cancer cells can lead to changes in cell morphology. Here, we describe the use of deep neural networks to analyze this relationship, demonstrating that complex cell morphologies can encode s...
Deep Learning Signal Transduction Drug Resistance, Neoplasm Humans Neural Networks, Computer Machine Learning ErbB Receptors
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Deep learning for drug response prediction in cancer.

Briefings in bioinformatics Jan 18, 2021
Predicting the sensitivity of tumors to specific anti-cancer treatments is a challenge of paramount importance for precision medicine. Machine learning(ML) algorithms can be trained on high-throughput screening data to develop models that are able to...
Genomics Gene Expression Regulation, Neoplastic Drug Resistance, Neoplasm Humans Deep Learning Pharmacogenomic Variants
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Ensembled machine learning framework for drug sensitivity prediction.

IET systems biology Feb 1, 2020
Drug sensitivity prediction is one of the critical tasks involved in drug designing and discovery. Recently several online databases and consortiums have contributed to providing open access to pharmacogenomic data. These databases have helped in dev...
Neoplasms Antineoplastic Agents Pharmacogenetics Humans Cell Line, Tumor Machine Learning Drug Resistance, Neoplasm Algorithms Databases, Pharmaceutical
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A Deep Learning Framework for Predicting Response to Therapy in Cancer.

Cell reports Dec 10, 2019
A major challenge in cancer treatment is predicting clinical response to anti-cancer drugs on a personalized basis. Using a pharmacogenomics database of 1,001 cancer cell lines, we trained deep neural networks for prediction of drug response and asse...
Cell Line, Tumor Survival Analysis Drug Resistance, Neoplasm Humans Precision Medicine Neoplasms Deep Learning
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Machine learning-based predictors for immune checkpoint inhibitor therapy of non-small-cell lung cancer.

Annals of oncology : official journal of the European Society for Medical Oncology Apr 1, 2019
Adult Biomarkers, Tumor Male Middle Aged Lung Lung Neoplasms Machine Learning Aged Aged, 80 and over Humans Programmed Cell Death 1 Receptor Drug Resistance, Neoplasm Predictive Value of Tests B7-H1 Antigen Mutation Models, Genetic Biopsy Carcinoma, Non-Small-Cell Lung
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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) Jan 10, 2019
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...
Neural Networks, Computer Stochastic Processes Machine Learning Leukemia, Myelogenous, Chronic, BCR-ABL Positive Humans Metabolomics Single-Cell Analysis Phenotype Drug Resistance, Neoplasm Logistic Models K562 Cells Mass Spectrometry
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PLATYPUS: A Multiple-View Learning Predictive Framework for Cancer Drug Sensitivity Prediction.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing Jan 1, 2019
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...
Antineoplastic Agents Patient-Specific Modeling Databases, Factual Cell Line, Tumor Pharmacogenomic Variants Precision Medicine Supervised Machine Learning Computational Biology Neoplasms Drug Resistance, Neoplasm Humans Information Storage and Retrieval Machine Learning Software
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