AIMC Topic: Drug Therapy, Combination
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Augmented drug combination dataset to improve the performance of machine learning models predicting synergistic anticancer effects.
Scientific reports
Jan 18, 2024
Combination therapy has gained popularity in cancer treatment as it enhances the treatment efficacy and overcomes drug resistance. Although machine learning (ML) techniques have become an indispensable tool for discovering new drug combinations, the ...
Antimalarial Drug Combination Predictions Using the Machine Learning Synergy Predictor (MLSyPred©) tool.
Acta parasitologica
Jan 2, 2024
PURPOSE: Antimalarial drug resistance is a global public health problem that leads to treatment failure. Synergistic drug combinations can improve treatment outcomes and delay the development of drug resistance. Here, we describe the implementation o...
Treatment selection using prototyping in latent-space with application to depression treatment.
PloS one
Nov 12, 2021
Machine-assisted treatment selection commonly follows one of two paradigms: a fully personalized paradigm which ignores any possible clustering of patients; or a sub-grouping paradigm which ignores personal differences within the identified groups. W...
Computational medication regimen for Parkinson's disease using reinforcement learning.
Scientific reports
Apr 29, 2021
Our objective is to derive a sequential decision-making rule on the combination of medications to minimize motor symptoms using reinforcement learning (RL). Using an observational longitudinal cohort of Parkinson's disease patients, the Parkinson's P...
CellBox: Interpretable Machine Learning for Perturbation Biology with Application to the Design of Cancer Combination Therapy.
Cell systems
Dec 28, 2020
Systematic perturbation of cells followed by comprehensive measurements of molecular and phenotypic responses provides informative data resources for constructing computational models of cell biology. Models that generalize well beyond training data ...
Artificial intelligence in COVID-19 drug repurposing.
The Lancet. Digital health
Sep 18, 2020
Drug repurposing or repositioning is a technique whereby existing drugs are used to treat emerging and challenging diseases, including COVID-19. Drug repurposing has become a promising approach because of the opportunity for reduced development timel...
Predicting drug-drug interactions using multi-modal deep auto-encoders based network embedding and positive-unlabeled learning.
Methods (San Diego, Calif.)
Jun 1, 2020
Drug-drug interactions (DDIs) are crucial for public health and patient safety, which has aroused widespread concern in academia and industry. The existing computational DDI prediction methods are mainly divided into four categories: literature extra...
Vitamin D insufficiency is associated with subclinical atherosclerosis in HIV-1-infected patients on combination antiretroviral therapy.
HIV research & clinical practice
Feb 17, 2020
Vitamin D insufficiency has been associated with faster progression of atherosclerosis and increased cardiovascular disease risk, but limited data are available in HIV-infected people. So, we examined potential correlation between vitamin D status a...
Using machine learning to optimize antibiotic combinations: dosing strategies for meropenem and polymyxin B against carbapenem-resistant Acinetobacter baumannii.
Clinical microbiology and infection : the official publication of the European Society of Clinical Microbiology and Infectious Diseases
Feb 12, 2020
OBJECTIVES: Increased rates of carbapenem-resistant strains of Acinetobacter baumannii have forced clinicians to rely upon last-line agents, such as the polymyxins, or empirical, unoptimized combination therapy. Therefore, the objectives of this stud...
Macrolide combination therapy for patients hospitalised with community-acquired pneumonia? An individualised approach supported by machine learning.
The European respiratory journal
Dec 12, 2019
BACKGROUND: The role of macrolide/β-lactam combination therapy in community-acquired pneumonia (CAP) of moderate severity is a matter of debate. Macrolides expand the coverage to atypical pathogens and attenuate pulmonary inflammation, but have been ...
Pneumonia, Bacterial
Community-Acquired Infections
beta-Lactams
Drug Therapy, Combination
Machine Learning
Propensity Score
Macrolides
Anti-Bacterial Agents
Severity of Illness Index
Aged, 80 and over
Prospective Studies
Female
Treatment Outcome
Humans
Adult
Hospitalization
Male
Europe
Aged
Middle Aged
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