AIMC Topic: Cell Line, Tumor
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MARSY: a multitask deep-learning framework for prediction of drug combination synergy scores.
Bioinformatics (Oxford, England)
Apr 3, 2023
MOTIVATION: Combination therapies have emerged as a treatment strategy for cancers to reduce the probability of drug resistance and to improve outcomes. Large databases curating the results of many drug screening studies on preclinical cancer cell li...
CCSynergy: an integrative deep-learning framework enabling context-aware prediction of anti-cancer drug synergy.
Briefings in bioinformatics
Jan 19, 2023
Combination therapy is a promising strategy for confronting the complexity of cancer. However, experimental exploration of the vast space of potential drug combinations is costly and unfeasible. Therefore, computational methods for predicting drug sy...
Deep learning-based classification of breast cancer cells using transmembrane receptor dynamics.
Bioinformatics (Oxford, England)
Dec 22, 2021
MOTIVATION: Motions of transmembrane receptors on cancer cell surfaces can reveal biophysical features of the cancer cells, thus providing a method for characterizing cancer cell phenotypes. While conventional analysis of receptor motions in the cell...
Cytotoxicity and molecular docking analysis of racemolactone I, a new sesquiterpene lactone isolated from .
Pharmaceutical biology
Dec 1, 2021
CONTEXT: Traditionally, Hook. f. (Asteraceae) has been reported to be effective in cancer treatment which motivated the authors to explore the plant for novel anticancer compounds.
Comparative analysis of molecular fingerprints in prediction of drug combination effects.
Briefings in bioinformatics
Nov 5, 2021
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...
Drug sensitivity prediction from cell line-based pharmacogenomics data: guidelines for developing machine learning models.
Briefings in bioinformatics
Nov 5, 2021
The goal of precision oncology is to tailor treatment for patients individually using the genomic profile of their tumors. Pharmacogenomics datasets such as cancer cell lines are among the most valuable resources for drug sensitivity prediction, a cr...
DeepDRK: a deep learning framework for drug repurposing through kernel-based multi-omics integration.
Briefings in bioinformatics
Sep 2, 2021
Recent pharmacogenomic studies that generate sequencing data coupled with pharmacological characteristics for patient-derived cancer cell lines led to large amounts of multi-omics data for precision cancer medicine. Among various obstacles hindering ...
Dual-responsive biohybrid neutrobots for active target delivery.
Science robotics
Mar 24, 2021
Swimming biohybrid microsized robots (e.g., bacteria- or sperm-driven microrobots) with self-propelling and navigating capabilities have become an exciting field of research, thanks to their controllable locomotion in hard-to-reach areas of the body ...
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...
Deep learning for in vivo near-infrared imaging.
Proceedings of the National Academy of Sciences of the United States of America
Jan 5, 2021
Detecting fluorescence in the second near-infrared window (NIR-II) up to ∼1,700 nm has emerged as a novel in vivo imaging modality with high spatial and temporal resolution through millimeter tissue depths. Imaging in the NIR-IIb window (1,500-1,700 ...
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