Survey of machine learning techniques in drug discovery
N Stephenson, E Shane, J Chase… - Current drug …, 2019 - ingentaconnect.com
Background: Drug discovery, which is the process of discovering new candidate
medications, is very important for pharmaceutical industries. At its current stage, discovering …
medications, is very important for pharmaceutical industries. At its current stage, discovering …
[HTML][HTML] A Survey on Computational Methods in Drug Discovery for Neurodegenerative Diseases
C Vicidomini, F Fontanella, T D'Alessandro… - Biomolecules, 2024 - mdpi.com
Currently, the age structure of the world population is changing due to declining birth rates
and increasing life expectancy. As a result, physicians worldwide have to treat an increasing …
and increasing life expectancy. As a result, physicians worldwide have to treat an increasing …
Multiple instance learning for histopathological breast cancer image classification
PJ Sudharshan, C Petitjean, F Spanhol… - Expert Systems with …, 2019 - Elsevier
Histopathological images are the gold standard for breast cancer diagnosis. During
examination several dozens of them are acquired for a single patient. Conventional, image …
examination several dozens of them are acquired for a single patient. Conventional, image …
Evaluate the malignancy of pulmonary nodules using the 3-d deep leaky noisy-or network
Automatic diagnosing lung cancer from computed tomography scans involves two steps:
detect all suspicious lesions (pulmonary nodules) and evaluate the whole-lung/pulmonary …
detect all suspicious lesions (pulmonary nodules) and evaluate the whole-lung/pulmonary …
Predicting lymph node metastasis using histopathological images based on multiple instance learning with deep graph convolution
Multiple instance learning (MIL) is a typical weakly-supervised learning method where the
label is associated with a bag of instances instead of a single instance. Despite extensive …
label is associated with a bag of instances instead of a single instance. Despite extensive …
BreakHis based breast cancer automatic diagnosis using deep learning: Taxonomy, survey and insights
There are several breast cancer datasets for building Computer Aided Diagnosis systems
(CADs) using either deep learning or traditional models. However, most of these datasets …
(CADs) using either deep learning or traditional models. However, most of these datasets …
A convolutional neural network based crystal plasticity finite element framework to predict localised deformation in metals
Convolutional neural networks (CNNs) find vast applications in the field of image
processing. This study utilises the CNNs in conjunction with the crystal plasticity finite …
processing. This study utilises the CNNs in conjunction with the crystal plasticity finite …
ProLanGO: protein function prediction using neural machine translation based on a recurrent neural network
With the development of next generation sequencing techniques, it is fast and cheap to
determine protein sequences but relatively slow and expensive to extract useful information …
determine protein sequences but relatively slow and expensive to extract useful information …
Synergistic learning of lung lobe segmentation and hierarchical multi-instance classification for automated severity assessment of COVID-19 in CT images
Understanding chest CT imaging of the coronavirus disease 2019 (COVID-19) will help
detect infections early and assess the disease progression. Especially, automated severity …
detect infections early and assess the disease progression. Especially, automated severity …
A new structural health monitoring strategy based on PZT sensors and convolutional neural network
Preliminaries convolutional neural network (CNN) applications have recently emerged in
structural health monitoring (SHM) systems focusing mostly on vibration analysis. However …
structural health monitoring (SHM) systems focusing mostly on vibration analysis. However …