AIMC Topic: Neural Networks, Computer

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Relation path feature embedding based convolutional neural network method for drug discovery.

BMC medical informatics and decision making
BACKGROUND: Drug development is an expensive and time-consuming process. Literature-based discovery has played a critical role in drug development and may be a supplementary method to help scientists speed up the discovery of drugs.

Convolutional Neural Networks for the Segmentation of Microcalcification in Mammography Imaging.

Journal of healthcare engineering
Cluster of microcalcifications can be an early sign of breast cancer. In this paper, we propose a novel approach based on convolutional neural networks for the detection and segmentation of microcalcification clusters. In this work, we used 283 mammo...

Phase relations of theta oscillations in a computer model of the hippocampal CA1 field: Key role of Schaffer collaterals.

Neural networks : the official journal of the International Neural Network Society
The hippocampal theta rhythm (4-12 Hz) is one of the most important electrophysiological processes in the hippocampus, it participates in cognitive hippocampal functions, such as navigation in space, novelty detection, and declarative memory. We use ...

DynMat, a network that can learn after learning.

Neural networks : the official journal of the International Neural Network Society
To survive in the dynamically-evolving world, we accumulate knowledge and improve our skills based on experience. In the process, gaining new knowledge does not disrupt our vigilance to external stimuli. In other words, our learning process is 'accum...

Identification of piecewise linear dynamical systems using physically-interpretable neural-fuzzy networks: Methods and applications to origami structures.

Neural networks : the official journal of the International Neural Network Society
Self-locking origami structures are characterized by their piecewise linear constitutive relations between force and deformation, which, in practice, are always completely opaque and unmeasurable: the number of piecewise segments, the positions of no...

Task-based assessment of a convolutional neural network for segmenting breast lesions for radiomic analysis.

Magnetic resonance in medicine
PURPOSE: Radiomics allows for powerful data-mining and feature extraction techniques to guide clinical decision making. Image segmentation is a necessary step in such pipelines and different techniques can significantly affect results. We demonstrate...

Midterm Power Load Forecasting Model Based on Kernel Principal Component Analysis and Back Propagation Neural Network with Particle Swarm Optimization.

Big data
To improve the accuracy of midterm power load forecasting, a forecasting model is proposed by combing kernel principal component analysis (KPCA) with back propagation neural network. First, the dimension of the input space is reduced by KPCA, then in...

Learn to segment single cells with deep distance estimator and deep cell detector.

Computers in biology and medicine
Single cell segmentation is a critical and challenging step in cell imaging analysis. Traditional processing methods require time and labor to manually fine-tune parameters and lack parameter transferability between different situations. Recently, de...

Drug Analogs from Fragment-Based Long Short-Term Memory Generative Neural Networks.

Journal of chemical information and modeling
Several recent reports have shown that long short-term memory generative neural networks (LSTM) of the type used for grammar learning efficiently learn to write Simplified Molecular Input Line Entry System (SMILES) of druglike compounds when trained ...

Developing neural network models for early detection of cardiac arrest in emergency department.

The American journal of emergency medicine
BACKGROUND: Automated surveillance for cardiac arrests would be useful in overcrowded emergency departments. The purpose of this study is to develop and test artificial neural network (ANN) classifiers for early detection of patients at risk of cardi...