Deep learning methods for drug response prediction in cancer: predominant and emerging trends
Cancer claims millions of lives yearly worldwide. While many therapies have been made
available in recent years, by in large cancer remains unsolved. Exploiting computational …
available in recent years, by in large cancer remains unsolved. Exploiting computational …
Omics data and data representations for deep learning-based predictive modeling
S Tsimenidis, E Vrochidou, GA Papakostas - International Journal of …, 2022 - mdpi.com
Medical discoveries mainly depend on the capability to process and analyze biological
datasets, which inundate the scientific community and are still expanding as the cost of next …
datasets, which inundate the scientific community and are still expanding as the cost of next …
Machine learning in onco-pharmacogenomics: A path to precision medicine with many challenges
A Mondello, M Dal Bo, G Toffoli… - Frontiers in Pharmacology, 2024 - frontiersin.org
Over the past two decades, Next-Generation Sequencing (NGS) has revolutionized the
approach to cancer research. Applications of NGS include the identification of tumor specific …
approach to cancer research. Applications of NGS include the identification of tumor specific …
Deepdrug: a general graph‐based deep learning framework for drug‐drug interactions and drug‐target interactions prediction
Computational methods for DDIs and DTIs prediction are essential for accelerating the drug
discovery process. We proposed a novel deep learning method DeepDrug, to tackle these …
discovery process. We proposed a novel deep learning method DeepDrug, to tackle these …
Data augmentation and multimodal learning for predicting drug response in patient-derived xenografts from gene expressions and histology images
Patient-derived xenografts (PDXs) are an appealing platform for preclinical drug studies. A
primary challenge in modeling drug response prediction (DRP) with PDXs and neural …
primary challenge in modeling drug response prediction (DRP) with PDXs and neural …
Multi-omics fusion based on attention mechanism for survival and drug response prediction in Digestive System Tumors
L Zhou, N Wang, Z Zhu, H Gao, N Lu, H Su, X Wang - Neurocomputing, 2024 - Elsevier
In recent decades, digestive system tumors (DST) have become the primary cause of cancer-
related deaths worldwide. Improving tumor prognosis and drug response prediction holds …
related deaths worldwide. Improving tumor prognosis and drug response prediction holds …
NeoHunter: Flexible software for systematically detecting neoantigens from sequencing data
Complicated molecular alterations in tumors generate various mutant peptides. Some of
these mutant peptides can be presented to the cell surface and then elicit immune …
these mutant peptides can be presented to the cell surface and then elicit immune …
Cancer drug sensitivity estimation using modular deep Graph Neural Networks
Computational drug sensitivity models have the potential to improve therapeutic outcomes
by identifying targeted drugs components that are tailored to the transcriptomic profile of a …
by identifying targeted drugs components that are tailored to the transcriptomic profile of a …
High-accuracy protein model quality assessment using attention graph neural networks
P Zhang, C Xia, HB Shen - Briefings in Bioinformatics, 2023 - academic.oup.com
Great improvement has been brought to protein tertiary structure prediction through deep
learning. It is important but very challenging to accurately rank and score decoy structures …
learning. It is important but very challenging to accurately rank and score decoy structures …
DeepAEG: a model for predicting cancer drug response based on data enhancement and edge-collaborative update strategies
Motivation The prediction of cancer drug response is a challenging subject in modern
personalized cancer therapy due to the uncertainty of drug efficacy and the heterogeneity of …
personalized cancer therapy due to the uncertainty of drug efficacy and the heterogeneity of …