AIMC Topic: Antineoplastic Agents
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Designing Anticancer Peptides by Constructive Machine Learning.
ChemMedChem
May 29, 2018
Constructive (generative) machine learning enables the automated generation of novel chemical structures without the need for explicit molecular design rules. This study presents the experimental application of such a deep machine learning model to d...
Affinity network fusion and semi-supervised learning for cancer patient clustering.
Methods (San Diego, Calif.)
May 26, 2018
Defining subtypes of complex diseases such as cancer and stratifying patient groups with the same disease but different subtypes for targeted treatments is important for personalized and precision medicine. Approaches that incorporate multi-omic data...
Identifying cytokine predictors of cognitive functioning in breast cancer survivors up to 10 years post chemotherapy using machine learning.
Journal of neuroimmunology
Apr 19, 2018
INTRODUCTION: The purpose of this study is to explore 13 cytokine predictors of chemotherapy-related cognitive impairment (CRCI) in breast cancer survivors (BCS) 6 months to 10 years after chemotherapy completion using a multivariate, non-parametric ...
Towards precision informatics of pharmacovigilance: OAE-CTCAE mapping and OAE-based representation and analysis of adverse events in patients treated with cancer drugs.
AMIA ... Annual Symposium proceedings. AMIA Symposium
Apr 16, 2018
A critical issue in the usage of cancer drugs is its association with various adverse events (AEs) in some, but not all, patients. The National Cancer Institute (NCI) Common Terminology Criteria for Adverse Events (CTCAE) is a controlled terminology ...
ANTENNA, a Multi-Rank, Multi-Layered Recommender System for Inferring Reliable Drug-Gene-Disease Associations: Repurposing Diazoxide as a Targeted Anti-Cancer Therapy.
IEEE/ACM transactions on computational biology and bioinformatics
Mar 16, 2018
Existing drug discovery processes follow a reductionist model of "one-drug-one-gene-one-disease," which is inadequate to tackle complex diseases involving multiple malfunctioned genes. The availability of big omics data offers opportunities to transf...
Predicting Treatment Response to Intra-arterial Therapies for Hepatocellular Carcinoma with the Use of Supervised Machine Learning-An Artificial Intelligence Concept.
Journal of vascular and interventional radiology : JVIR
Mar 14, 2018
PURPOSE: To use magnetic resonance (MR) imaging and clinical patient data to create an artificial intelligence (AI) framework for the prediction of therapeutic outcomes of transarterial chemoembolization by applying machine learning (ML) techniques.
Neoplasm Staging
Doxorubicin
Chemoembolization, Therapeutic
Ethiodized Oil
Treatment Outcome
Contrast Media
Magnetic Resonance Imaging
Predictive Value of Tests
Antineoplastic Agents
Retrospective Studies
Liver Neoplasms
Female
Carcinoma, Hepatocellular
Humans
Aged
Adult
Sensitivity and Specificity
Machine Learning
Male
Middle Aged
Big Data Toolsets to Pharmacometrics: Application of Machine Learning for Time-to-Event Analysis.
Clinical and translational science
Mar 13, 2018
Additional value can be potentially created by applying big data tools to address pharmacometric problems. The performances of machine learning (ML) methods and the Cox regression model were evaluated based on simulated time-to-event data synthesized...
Repurposing High-Throughput Image Assays Enables Biological Activity Prediction for Drug Discovery.
Cell chemical biology
Mar 1, 2018
In both academia and the pharmaceutical industry, large-scale assays for drug discovery are expensive and often impractical, particularly for the increasingly important physiologically relevant model systems that require primary cells, organoids, who...
Machine learning prioritizes synthesis of primaquine ureidoamides with high antimalarial activity and attenuated cytotoxicity.
European journal of medicinal chemistry
Jan 31, 2018
Primaquine (PQ) is a commonly used drug that can prevent the transmission of Plasmodium falciparum malaria, however toxicity limits its use. We prepared five groups of PQ derivatives: amides 1a-k, ureas 2a-k, semicarbazides 3a,b, acylsemicarbazides 4...
A method of gene expression data transfer from cell lines to cancer patients for machine-learning prediction of drug efficiency.
Cell cycle (Georgetown, Tex.)
Jan 17, 2018
Personalized medicine implies that distinct treatment methods are prescribed to individual patients according several features that may be obtained from, e.g., gene expression profile. The majority of machine learning methods suffer from the deficien...
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