Machine learning in drug discovery: a review
This review provides the feasible literature on drug discovery through ML tools and
techniques that are enforced in every phase of drug development to accelerate the research …
techniques that are enforced in every phase of drug development to accelerate the research …
Artificial intelligence-based multi-omics analysis fuels cancer precision medicine
With biotechnological advancements, innovative omics technologies are constantly
emerging that have enabled researchers to access multi-layer information from the genome …
emerging that have enabled researchers to access multi-layer information from the genome …
Deep neural network models for computational histopathology: A survey
CL Srinidhi, O Ciga, AL Martel - Medical image analysis, 2021 - Elsevier
Histopathological images contain rich phenotypic information that can be used to monitor
underlying mechanisms contributing to disease progression and patient survival outcomes …
underlying mechanisms contributing to disease progression and patient survival outcomes …
Applications of machine learning in drug discovery and development
J Vamathevan, D Clark, P Czodrowski… - Nature reviews Drug …, 2019 - nature.com
Drug discovery and development pipelines are long, complex and depend on numerous
factors. Machine learning (ML) approaches provide a set of tools that can improve discovery …
factors. Machine learning (ML) approaches provide a set of tools that can improve discovery …
[HTML][HTML] A review on deep learning approaches in healthcare systems: Taxonomies, challenges, and open issues
S Shamshirband, M Fathi, A Dehzangi… - Journal of Biomedical …, 2021 - Elsevier
In the last few years, the application of Machine Learning approaches like Deep Neural
Network (DNN) models have become more attractive in the healthcare system given the …
Network (DNN) models have become more attractive in the healthcare system given the …
Deep learning models for histopathological classification of gastric and colonic epithelial tumours
O Iizuka, F Kanavati, K Kato, M Rambeau, K Arihiro… - Scientific reports, 2020 - nature.com
Histopathological classification of gastric and colonic epithelial tumours is one of the routine
pathological diagnosis tasks for pathologists. Computational pathology techniques based on …
pathological diagnosis tasks for pathologists. Computational pathology techniques based on …
A comprehensive review of computer-aided whole-slide image analysis: from datasets to feature extraction, segmentation, classification and detection approaches
With the development of Computer-aided Diagnosis (CAD) and image scanning techniques,
Whole-slide Image (WSI) scanners are widely used in the field of pathological diagnosis …
Whole-slide Image (WSI) scanners are widely used in the field of pathological diagnosis …
Emerging role of deep learning‐based artificial intelligence in tumor pathology
The development of digital pathology and progression of state‐of‐the‐art algorithms for
computer vision have led to increasing interest in the use of artificial intelligence (AI) …
computer vision have led to increasing interest in the use of artificial intelligence (AI) …
Artificial intelligence to identify genetic alterations in conventional histopathology
Precision oncology relies on the identification of targetable molecular alterations in tumor
tissues. In many tumor types, a limited set of molecular tests is currently part of standard …
tissues. In many tumor types, a limited set of molecular tests is currently part of standard …
Adversarial attacks and adversarial robustness in computational pathology
Artificial Intelligence (AI) can support diagnostic workflows in oncology by aiding diagnosis
and providing biomarkers directly from routine pathology slides. However, AI applications …
and providing biomarkers directly from routine pathology slides. However, AI applications …