AIMC Topic: Machine Learning

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Large-scale online semantic indexing of biomedical articles via an ensemble of multi-label classification models.

Journal of biomedical semantics
BACKGROUND: In this paper we present the approach that we employed to deal with large scale multi-label semantic indexing of biomedical papers. This work was mainly implemented within the context of the BioASQ challenge (2013-2017), a challenge conce...

Probing the toxicity of nanoparticles: a unified in silico machine learning model based on perturbation theory.

Nanotoxicology
Nanoparticles (NPs) are part of our daily life, having a wide range of applications in engineering, physics, chemistry, and biomedicine. However, there are serious concerns regarding the harmful effects that NPs can cause to the different biological ...

Neural network robust tracking control with adaptive critic framework for uncertain nonlinear systems.

Neural networks : the official journal of the International Neural Network Society
In this paper, we aim to tackle the neural robust tracking control problem for a class of nonlinear systems using the adaptive critic technique. The main contribution is that a neural-network-based robust tracking control scheme is established for no...

Quantum associative memory with linear and non-linear algorithms for the diagnosis of some tropical diseases.

Neural networks : the official journal of the International Neural Network Society
This paper presents the QAMDiagnos, a model of Quantum Associative Memory (QAM) that can be a helpful tool for medical staff without experience or laboratory facilities, for the diagnosis of four tropical diseases (malaria, typhoid fever, yellow feve...

Mimvec: a deep learning approach for analyzing the human phenome.

BMC systems biology
BACKGROUND: The human phenome has been widely used with a variety of genomic data sources in the inference of disease genes. However, most existing methods thus far derive phenotype similarity based on the analysis of biomedical databases by using th...

Deep reconstruction model for dynamic PET images.

PloS one
Accurate and robust tomographic reconstruction from dynamic positron emission tomography (PET) acquired data is a difficult problem. Conventional methods, such as the maximum likelihood expectation maximization (MLEM) algorithm for reconstructing the...

Multiple Cayley-Klein metric learning.

PloS one
As a specific kind of non-Euclidean metric lies in projective space, Cayley-Klein metric has been recently introduced in metric learning to deal with the complex data distributions in computer vision tasks. In this paper, we extend the original Cayle...

Using artificial neural networks to select the parameters for the prognostic of mild cognitive impairment and dementia in elderly individuals.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVES: A huge number of solutions based on computational systems have been recently developed for the classification of cognitive abnormalities in older people, so that individuals at high risk of developing neurodegenerative dise...

Lifelong learning of human actions with deep neural network self-organization.

Neural networks : the official journal of the International Neural Network Society
Lifelong learning is fundamental in autonomous robotics for the acquisition and fine-tuning of knowledge through experience. However, conventional deep neural models for action recognition from videos do not account for lifelong learning but rather l...

A deep learning approach to estimate chemically-treated collagenous tissue nonlinear anisotropic stress-strain responses from microscopy images.

Acta biomaterialia
UNLABELLED: Biological collagenous tissues comprised of networks of collagen fibers are suitable for a broad spectrum of medical applications owing to their attractive mechanical properties. In this study, we developed a noninvasive approach to estim...