AIMC Topic: Neural Networks, Computer

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Nonlinear effective connectivity measure based on adaptive Neuro Fuzzy Inference System and Granger Causality.

NeuroImage
Exploring brain networks is an essential step towards understanding functional organization of the brain, which needs characterization of linear and nonlinear connections based on measurements like EEG or MEG. Conventional measures of connectivity ar...

iMethyl-STTNC: Identification of N-methyladenosine sites by extending the idea of SAAC into Chou's PseAAC to formulate RNA sequences.

Journal of theoretical biology
N- methyladenosine (mA) is a vital post-transcriptional modification, which adds another layer of epigenetic regulation at RNA level. It chemically modifies mRNA that effects protein expression. RNA sequence contains many genetic code motifs (GAC). A...

Fully automatic cross-modality localization and labeling of vertebral bodies and intervertebral discs in 3D spinal images.

International journal of computer assisted radiology and surgery
PURPOSE: We present a cross-modality and fully automatic pipeline for labeling of intervertebral discs and vertebrae in volumetric data of the lumbar and thoracolumbar spine. The main goal is to provide an algorithm that is applicable to a wide range...

Medical breast ultrasound image segmentation by machine learning.

Ultrasonics
Breast cancer is the most commonly diagnosed cancer, which alone accounts for 30% all new cancer diagnoses for women, posing a threat to women's health. Segmentation of breast ultrasound images into functional tissues can aid tumor localization, brea...

Artificial neural networks: Modeling tree survival and mortality in the Atlantic Forest biome in Brazil.

The Science of the total environment
Models to predict tree survival and mortality can help to understand vegetation dynamics and to predict effects of climate change on native forests. The objective of the present study was to use Artificial Neural Networks, based on the competition in...

Network-Based Drug Discovery: Coupling Network Pharmacology with Phenotypic Screening for Neuronal Excitability.

Journal of molecular biology
Diseases such as chronic pain with complex etiologies are unlikely to respond to single, target-specific therapeutics but rather require intervention at multiple points within a perturbed disease system. Such approaches are being enabled by the rise ...

Referenceless distortion correction of gradient-echo echo-planar imaging under inhomogeneous magnetic fields based on a deep convolutional neural network.

Computers in biology and medicine
Single-shot gradient-echo echo-planar imaging (GE-EPI) plays a significant role in applications where high temporal resolution is necessary. However, GE-EPI is susceptible to inhomogeneous magnetic fields that will cause image distortion. Most existi...

De novo profile generation based on sequence context specificity with the long short-term memory network.

BMC bioinformatics
BACKGROUND: Long short-term memory (LSTM) is one of the most attractive deep learning methods to learn time series or contexts of input data. Increasing studies, including biological sequence analyses in bioinformatics, utilize this architecture. Ami...

Memristive nanowires exhibit small-world connectivity.

Neural networks : the official journal of the International Neural Network Society
Small-world networks provide an excellent balance of efficiency and robustness that is not available with other network topologies. These characteristics are exhibited in the Memristive Nanowire Neural Network (MN), a novel neuromorphic hardware arch...

An effective teeth recognition method using label tree with cascade network structure.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
In this article, we apply the deep learning technique to medical field for the teeth detection and classification of dental periapical radiographs, which is important for the medical curing and postmortem identification. We detect teeth in an input X...