AIMC Topic: Antineoplastic Agents
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ENNAACT is a novel tool which employs neural networks for anticancer activity classification for therapeutic peptides.
Biomedicine & pharmacotherapy = Biomedecine & pharmacotherapie
Nov 27, 2020
The prevalence of cancer as a threat to human life, responsible for 9.6 million deaths worldwide in 2018, motivates the search for new anticancer agents. While many options are currently available for treatment, these are often expensive and impact t...
Machine Learning-Based Risk Assessment for Cancer Therapy-Related Cardiac Dysfunction in 4300 Longitudinal Oncology Patients.
Journal of the American Heart Association
Nov 26, 2020
Background The growing awareness of cardiovascular toxicity from cancer therapies has led to the emerging field of cardio-oncology, which centers on preventing, detecting, and treating patients with cardiac dysfunction before, during, or after cancer...
Harnessing adaptive novelty for automated generation of cancer treatments.
Bio Systems
Nov 17, 2020
Nanoparticles have the potential to modulate both the pharmacokinetic and pharmacodynamic profiles of drugs, thereby enhancing their therapeutic effect. The versatility of nanoparticles allows for a wide range of customization possibilities. However,...
DeepSIBA: chemical structure-based inference of biological alterations using deep learning.
Molecular omics
Nov 14, 2020
Predicting whether a chemical structure leads to a desired or adverse biological effect can have a significant impact for in silico drug discovery. In this study, we developed a deep learning model where compound structures are represented as graphs ...
H-RACS: a handy tool to rank anti-cancer synergistic drugs.
Aging
Nov 10, 2020
Though promising, identifying synergistic combinations from a large pool of candidate drugs remains challenging for cancer treatment. Due to unclear mechanism and limited confirmed cases, only a few computational algorithms are able to predict drug s...
Network-based machine learning in colorectal and bladder organoid models predicts anti-cancer drug efficacy in patients.
Nature communications
Oct 30, 2020
Cancer patient classification using predictive biomarkers for anti-cancer drug responses is essential for improving therapeutic outcomes. However, current machine-learning-based predictions of drug response often fail to identify robust translational...
Development and Validation of a Gene Signature Classifier for Consensus Molecular Subtyping of Colorectal Carcinoma in a CLIA-Certified Setting.
Clinical cancer research : an official journal of the American Association for Cancer Research
Oct 27, 2020
PURPOSE: Consensus molecular subtyping (CMS) of colorectal cancer has potential to reshape the colorectal cancer landscape. We developed and validated an assay that is applicable on formalin-fixed, paraffin-embedded (FFPE) samples of colorectal cance...
Automatic construction of molecular similarity networks for visual graph mining in chemical space of bioactive peptides: an unsupervised learning approach.
Scientific reports
Oct 22, 2020
The increasing interest in bioactive peptides with therapeutic potentials has been reflected in a large variety of biological databases published over the last years. However, the knowledge discovery process from these heterogeneous data sources is a...
Ensemble transfer learning for the prediction of anti-cancer drug response.
Scientific reports
Oct 22, 2020
Transfer learning, which transfers patterns learned on a source dataset to a related target dataset for constructing prediction models, has been shown effective in many applications. In this paper, we investigate whether transfer learning can be used...
Comparative study between deep learning and QSAR classifications for TNBC inhibitors and novel GPCR agonist discovery.
Scientific reports
Oct 8, 2020
Machine learning is a well-known approach for virtual screening. Recently, deep learning, a machine learning algorithm in artificial neural networks, has been applied to the advancement of precision medicine and drug discovery. In this study, we perf...
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