AIMC Topic: Neural Networks, Computer

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Predicting Metabolic Reaction Networks with Perturbation-Theory Machine Learning (PTML) Models.

Current topics in medicinal chemistry
BACKGROUND: Checking the connectivity (structure) of complex Metabolic Reaction Networks (MRNs) models proposed for new microorganisms with promising properties is an important goal for chemical biology.

[Patient-tailored approach in tertiary care expert centres using individual dynamic network analysis].

Tijdschrift voor psychiatrie
BACKGROUND: Patients with mental health disorders often have difficulty perceiving associations between multiple symptoms, such as inter-relations between somatic and psychological symptoms. This difficulty may be particularly challenging in patients...

Bionoi: A Voronoi Diagram-Based Representation of Ligand-Binding Sites in Proteins for Machine Learning Applications.

Methods in molecular biology (Clifton, N.J.)
Bionoi is a new software to generate Voronoi representations of ligand-binding sites in proteins for machine learning applications. Unlike many other deep learning models in biomedicine, Bionoi utilizes off-the-shelf convolutional neural network arch...

Multifunctionality in a reservoir computer.

Chaos (Woodbury, N.Y.)
Multifunctionality is a well observed phenomenological feature of biological neural networks and considered to be of fundamental importance to the survival of certain species over time. These multifunctional neural networks are capable of performing ...

Functional differentiations in evolutionary reservoir computing networks.

Chaos (Woodbury, N.Y.)
We propose an extended reservoir computer that shows the functional differentiation of neurons. The reservoir computer is developed to enable changing of the internal reservoir using evolutionary dynamics, and we call it an evolutionary reservoir com...

A state observer for the computational network model of neural populations.

Chaos (Woodbury, N.Y.)
A state observer plays a vital role in the design of state feedback neuromodulation schemes used to prevent and treat neurological or psychiatric disorders. This paper aims to design a state observer to reconstruct all unmeasured states of the comput...

A few-shot segmentation method for prohibited item inspection.

Journal of X-ray science and technology
BACKGROUND: With the rapid development of deep learning, several neural network models have been proposed for automatic segmentation of prohibited items. These methods usually based on a substantial amount of labelled training data. However, for some...

Method for determining load magnitude and location from the plastic deformation of fixed beams using a neural network.

Science progress
Fixed beam structures are widely used in engineering, and a common problem is determining the load conditions of these structures resulting from impact loads. In this study, a method for accurately identifying the location and magnitude of the load c...

Topological Feature Extraction and Visualization of Whole Slide Images using Graph Neural Networks.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
Whole-slide images (WSI) are digitized representations of thin sections of stained tissue from various patient sources (biopsy, resection, exfoliation, fluid) and often exceed 100,000 pixels in any given spatial dimension. Deep learning approaches to...

CheXclusion: Fairness gaps in deep chest X-ray classifiers.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
Machine learning systems have received much attention recently for their ability to achieve expert-level performance on clinical tasks, particularly in medical imaging. Here, we examine the extent to which state-of-the-art deep learning classifiers t...