AIMC Topic:
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Machine learning methods for optimal prediction of motor outcome in Parkinson's disease.
Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB)
Jan 7, 2020
PURPOSE: It is vital to appropriately power clinical trials towards discovery of novel disease-modifying therapies for Parkinson's disease (PD). Thus, it is critical to improve prediction of outcome in PD patients.
Deep Learning to Generate Chemical Property Libraries and Candidate Molecules for Small Molecule Identification in Complex Samples.
Analytical chemistry
Jan 6, 2020
Comprehensive and unambiguous identification of small molecules in complex samples will revolutionize our understanding of the role of metabolites in biological systems. Existing and emerging technologies have enabled measurement of chemical properti...
Risk perception and behavioral change during epidemics: Comparing models of individual and collective learning.
PloS one
Jan 6, 2020
Modern societies are exposed to a myriad of risks ranging from disease to natural hazards and technological disruptions. Exploring how the awareness of risk spreads and how it triggers a diffusion of coping strategies is prominent in the research age...
A personalized computational model predicts cancer risk level of oral potentially malignant disorders and its web application for promotion of non-invasive screening.
Journal of oral pathology & medicine : official publication of the International Association of Oral Pathologists and the American Academy of Oral Pathology
Jan 4, 2020
BACKGROUND: Despite their high accuracy to recognize oral potentially malignant disorders (OPMDs) with cancer risk, non-invasive oral assays are poor in discerning whether the risk is high or low. However, it is critical to identify the risk levels, ...
Learning efficient haptic shape exploration with a rigid tactile sensor array.
PloS one
Jan 2, 2020
Haptic exploration is a key skill for both robots and humans to discriminate and handle unknown objects or to recognize familiar objects. Its active nature is evident in humans who from early on reliably acquire sophisticated sensory-motor capabiliti...
A neurodynamic approach to nonsmooth constrained pseudoconvex optimization problem.
Neural networks : the official journal of the International Neural Network Society
Dec 30, 2019
This paper presents a new neurodynamic approach for solving the constrained pseudoconvex optimization problem based on more general assumptions. The proposed neural network is equipped with a hard comparator function and a piecewise linear function, ...
DeepMF: deciphering the latent patterns in omics profiles with a deep learning method.
BMC bioinformatics
Dec 27, 2019
BACKGROUND: With recent advances in high-throughput technologies, matrix factorization techniques are increasingly being utilized for mapping quantitative omics profiling matrix data into low-dimensional embedding space, in the hope of uncovering ins...
A deep learning approach for converting prompt gamma images to proton dose distributions: A Monte Carlo simulation study.
Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB)
Dec 20, 2019
PURPOSE: In proton therapy, imaging prompt gamma (PG) rays has the potential to verify proton dose (PD) distribution. Despite the fact that there is a strong correlation between the gamma-ray emission and PD, they are still different in terms of the ...
Representation learning of genomic sequence motifs with convolutional neural networks.
PLoS computational biology
Dec 19, 2019
Although convolutional neural networks (CNNs) have been applied to a variety of computational genomics problems, there remains a large gap in our understanding of how they build representations of regulatory genomic sequences. Here we perform systema...
Avoidance of non-localizable obstacles in echolocating bats: A robotic model.
PLoS computational biology
Dec 19, 2019
Most objects and vegetation making up the habitats of echolocating bats return a multitude of overlapping echoes. Recent evidence suggests that the limited temporal and spatial resolution of bio-sonar prevents bats from separately perceiving the obje...
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