Artificial intelligence and pathology: from principles to practice and future applications in histomorphology and molecular profiling
The complexity of diagnostic (surgical) pathology has increased substantially over the last
decades with respect to histomorphological and molecular profiling. Pathology has steadily …
decades with respect to histomorphological and molecular profiling. Pathology has steadily …
Computational drug discovery for castration-resistant prostate cancers through in vitro drug response modeling
Prostate cancer (PC) is the most frequently diagnosed malignancy and a leading cause of
cancer deaths in US men. Many PC cases metastasize and develop resistance to systemic …
cancer deaths in US men. Many PC cases metastasize and develop resistance to systemic …
[HTML][HTML] Predicting COVID-19 disease progression and patient outcomes based on temporal deep learning
Background The coronavirus disease 2019 (COVID-19) pandemic has caused health
concerns worldwide since December 2019. From the beginning of infection, patients will …
concerns worldwide since December 2019. From the beginning of infection, patients will …
[HTML][HTML] 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 …
A cross-study analysis of drug response prediction in cancer cell lines
To enable personalized cancer treatment, machine learning models have been developed
to predict drug response as a function of tumor and drug features. However, most algorithm …
to predict drug response as a function of tumor and drug features. However, most algorithm …
Predicting cancer drug response using parallel heterogeneous graph convolutional networks with neighborhood interactions
Motivation Due to cancer heterogeneity, the therapeutic effect may not be the same when a
cohort of patients of the same cancer type receive the same treatment. The anticancer drug …
cohort of patients of the same cancer type receive the same treatment. The anticancer drug …
[HTML][HTML] Predicting response to enzalutamide and abiraterone in metastatic prostate cancer using whole-omics machine learning
Response to androgen receptor signaling inhibitors (ARSI) varies widely in metastatic
castration resistant prostate cancer (mCRPC). To improve treatment guidance, biomarkers …
castration resistant prostate cancer (mCRPC). To improve treatment guidance, biomarkers …
Trends and potential of machine learning and deep learning in drug study at single-cell level
Cancer treatments always face challenging problems, particularly drug resistance due to
tumor cell heterogeneity. The existing datasets include the relationship between gene …
tumor cell heterogeneity. The existing datasets include the relationship between gene …
A framework for integrating artificial intelligence for clinical care with continuous therapeutic monitoring
The complex relationships between continuously monitored health signals and therapeutic
regimens can be modelled via machine learning. However, the clinical implementation of …
regimens can be modelled via machine learning. However, the clinical implementation of …
Machine learning in the identification, prediction and exploration of environmental toxicology: Challenges and perspectives
X Wu, Q Zhou, L Mu, X Hu - Journal of Hazardous Materials, 2022 - Elsevier
Over the past few decades, data-driven machine learning (ML) has distinguished itself from
hypothesis-driven studies and has recently received much attention in environmental …
hypothesis-driven studies and has recently received much attention in environmental …