AIMC Topic: Antineoplastic Agents
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Classification and gene selection of triple-negative breast cancer subtype embedding gene connectivity matrix in deep neural network.
Briefings in bioinformatics
Sep 2, 2021
Triple-negative breast cancer (TNBC) has been a challenging breast cancer subtype for oncological therapy. Normally, it can be classified into different molecular subtypes. Accurate and stable classification of the six subtypes is essential for perso...
Large-scale comparative review and assessment of computational methods for anti-cancer peptide identification.
Briefings in bioinformatics
Jul 20, 2021
Anti-cancer peptides (ACPs) are known as potential therapeutics for cancer. Due to their unique ability to target cancer cells without affecting healthy cells directly, they have been extensively studied. Many peptide-based drugs are currently evalua...
AntiCP 2.0: an updated model for predicting anticancer peptides.
Briefings in bioinformatics
May 20, 2021
Increasing use of therapeutic peptides for treating cancer has received considerable attention of the scientific community in the recent years. The present study describes the in silico model developed for predicting and designing anticancer peptides...
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 ...
Artificial Intelligence in Cancer Research and Precision Medicine.
Cancer discovery
Apr 1, 2021
Artificial intelligence (AI) is rapidly reshaping cancer research and personalized clinical care. Availability of high-dimensionality datasets coupled with advances in high-performance computing, as well as innovative deep learning architectures, has...
Machine learning approaches for elucidating the biological effects of natural products.
Natural product reports
Mar 4, 2021
Covering: 2000 to 2020 Machine learning (ML) is an efficient tool for the prediction of bioactivity and the study of structure-activity relationships. Over the past decade, an emerging trend for combining these approaches with the study of natural pr...
Anticancer drug synergy prediction in understudied tissues using transfer learning.
Journal of the American Medical Informatics Association : JAMIA
Jan 15, 2021
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...
canSAR: update to the cancer translational research and drug discovery knowledgebase.
Nucleic acids research
Jan 8, 2021
canSAR (http://cansar.icr.ac.uk) is the largest, public, freely available, integrative translational research and drug discovery knowledgebase for oncology. canSAR integrates vast multidisciplinary data from across genomic, protein, pharmacological, ...
Open Targets Platform: supporting systematic drug-target identification and prioritisation.
Nucleic acids research
Jan 8, 2021
The Open Targets Platform (https://www.targetvalidation.org/) provides users with a queryable knowledgebase and user interface to aid systematic target identification and prioritisation for drug discovery based upon underlying evidence. It is publicl...
Chemotherapy Knowledge Base Management in the Era of Precision Oncology.
JCO clinical cancer informatics
Jan 1, 2021
Cancer medicine has grown increasingly complex in recent years with the advent of precision oncology and wide utilization of multidrug regimens. Representing this increasingly granular knowledge is a significant challenge. As users and managers of a ...
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