BACKGROUND: Over the past decade, machine learning techniques have revolutionized how research and science are done, from designing new materials and predicting their properties to data mining and analysis to assisting drug discovery to advancing cyb...
Drug-induced liver injury (DILI) is the most common cause of acute liver failure and often responsible for drug withdrawals from the market. Clinical manifestations vary, and toxicity may or may not appear dose-dependent. We present several machine-l...
Neural networks : the official journal of the International Neural Network Society
Oct 16, 2018
The big breakthrough on the ImageNet challenge in 2012 was partially due to the 'Dropout' technique used to avoid overfitting. Here, we introduce a new approach called 'Spectral Dropout' to improve the generalization ability of deep neural networks. ...
BACKGROUND: The most tedious and time-consuming task in medical additive manufacturing (AM) is image segmentation. The aim of the present study was to develop and train a convolutional neural network (CNN) for bone segmentation in computed tomography...
Journal of chemical information and modeling
Oct 16, 2018
Machine learning has shown enormous potential for computer-aided drug discovery. Here we show how modern convolutional neural networks (CNNs) can be applied to structure-based virtual screening. We have coupled our densely connected CNN (DenseNet) wi...
G-Networks and their simplified version known as the Random Neural Network have often been used to classify data. In this paper, we present a use of the Random Neural Network to the early detection of potential of toxicity chemical compounds through ...
Recent advances in machine learning have enabled neural networks to solve tasks humans typically perform. These networks offer an exciting new tool for neuroscience that can give us insight in the emergence of neural and behavioral mechanisms. A big ...
BACKGROUND AND OBJECTIVE: Efficiently capturing the severity of positive valence symptoms could aid in risk stratification for adverse outcomes among patients with psychiatric disorders and identify optimal treatment strategies for patient subgroups....
Neural networks : the official journal of the International Neural Network Society
Oct 15, 2018
Learning algorithms for energy based Boltzmann architectures that rely on gradient descent are in general computationally prohibitive, typically due to the exponential number of terms involved in computing the partition function. In this way one has ...
Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
Oct 15, 2018
Spectroscopy is a powerful non-destructive quantization tool. In this paper, the technology is used to predict the nitrogen content of Masson pine seedling leaves. Masson pine is widely planted in China, and its nitrogen content is an important index...
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