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Drug Combinations

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Effectiveness and safety of elvitegravir/cobicistat/emtricitabine/tenofovir disoproxil fumarate single-tablet combination among HIV-infected patients in Turkey: results from a real world setting.

African health sciences
BACKGROUND: Efficacy of elvitegravir/cobicistat/emtricitabine/tenofovir disoproxil (E/C/F/TDF) in treatment-naïve and experienced patients with HIV infection was demonstrated in phase 3 trials. The primary objective of this study was to evaluate effe...

Comparative analysis of molecular fingerprints in prediction of drug combination effects.

Briefings in bioinformatics
Application of machine and deep learning methods in drug discovery and cancer research has gained a considerable amount of attention in the past years. As the field grows, it becomes crucial to systematically evaluate the performance of novel computa...

Detecting asthma exacerbations using daily home monitoring and machine learning.

The Journal of asthma : official journal of the Association for the Care of Asthma
OBJECTIVE: Acute exacerbations contribute significantly to the morbidity of asthma. Recent studies have shown that early detection and treatment of asthma exacerbations leads to improved outcomes. We aimed to develop a machine learning algorithm to d...

GraphSynergy: a network-inspired deep learning model for anticancer drug combination prediction.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: To develop an end-to-end deep learning framework based on a protein-protein interaction (PPI) network to make synergistic anticancer drug combination predictions.

Deep learning identifies synergistic drug combinations for treating COVID-19.

Proceedings of the National Academy of Sciences of the United States of America
Effective treatments for COVID-19 are urgently needed. However, discovering single-agent therapies with activity against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been challenging. Combination therapies play an important role i...

[Exploration on rationality evaluation approach of drug combination medication based on sequential analysis and machine learning].

Zhongguo Zhong yao za zhi = Zhongguo zhongyao zazhi = China journal of Chinese materia medica
Drug combination is a common clinical phenomenon. However, the scientific implementation of drug combination is li-mited by the weak rational evaluation that reflects its clinical characteristics. In order to break through the limitations of existing...

Predicting adverse drug reactions of two-drug combinations using structural and transcriptomic drug representations to train an artificial neural network.

Chemical biology & drug design
Adverse drug reactions (ADRs) are pharmacological events triggered by drug interactions with various sources of origin including drug-drug interactions. While there are many computational studies that explore models to predict ADRs originating from s...

Anticancer drug synergy prediction in understudied tissues using transfer learning.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: Drug combination screening has advantages in identifying cancer treatment options with higher efficacy without degradation in terms of safety. A key challenge is that the accumulated number of observations in in-vitro drug responses varies...

Synergistic Drug Combination Prediction by Integrating Multiomics Data in Deep Learning Models.

Methods in molecular biology (Clifton, N.J.)
Intrinsic and acquired drug resistance is a major challenge in cancer therapy. Synergistic drug combinations could help to overcome drug resistance. However, the number of possible drug combinations is enormous, and it is infeasible to experimentally...

Application of machine intelligence technology in the detection of vaccines and medicines for SARS-CoV-2.

European review for medical and pharmacological sciences
Researchers have found many similarities between the 2003 severe acute respiratory syndrome (SARS) virus and SARS-CoV-19 through existing data that reveal the SARS's cause. Artificial intelligence (AI) learning models can be created to predict drug s...