AIMC Topic: Humans

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Noise Correlations for Faster and More Robust Learning.

The Journal of neuroscience : the official journal of the Society for Neuroscience
Distributed population codes are ubiquitous in the brain and pose a challenge to downstream neurons that must learn an appropriate readout. Here we explore the possibility that this learning problem is simplified through inductive biases implemented ...

A stacked ensemble for the detection of COVID-19 with high recall and accuracy.

Computers in biology and medicine
The main challenges for the automatic detection of the coronavirus disease (COVID-19) from computed tomography (CT) scans of an individual are: a lack of large datasets, ambiguity in the characteristics of COVID-19 and the detection techniques having...

Molecular insights on ABL kinase activation using tree-based machine learning models and molecular docking.

Molecular diversity
Abelson kinase (c-Abl) is a non-receptor tyrosine kinase involved in several biological processes essential for cell differentiation, migration, proliferation, and survival. This enzyme's activation might be an alternative strategy for treating disea...

Binding affinity prediction for binary drug-target interactions using semi-supervised transfer learning.

Journal of computer-aided molecular design
In the field of drug-target interactions prediction, the majority of approaches formulated the problem as a simple binary classification task. These methods used binary drug-target interaction datasets to train their models. The prediction of drug-ta...

Deep Collocative Learning for Immunofixation Electrophoresis Image Analysis.

IEEE transactions on medical imaging
Immunofixation Electrophoresis (IFE) analysis is of great importance to the diagnosis of Multiple Myeloma, which is among the top-9 cancer killers in the United States, but has rarely been studied in the context of deep learning. Two possible reasons...

Zero-Shot Super-Resolution With a Physically-Motivated Downsampling Kernel for Endomicroscopy.

IEEE transactions on medical imaging
Super-resolution (SR) methods have seen significant advances thanks to the development of convolutional neural networks (CNNs). CNNs have been successfully employed to improve the quality of endomicroscopy imaging. Yet, the inherent limitation of res...

Safer chemicals using less animals: kick-off of the European ONTOX project.

Toxicology
The 3Rs concept, calling for replacement, reduction and refinement of animal experimentation, is receiving increasing attention around the world, and has found its way to legislation, in particular in the European Union. This is aligned by continuing...

Cross Attention Squeeze Excitation Network (CASE-Net) for Whole Body Fetal MRI Segmentation.

Sensors (Basel, Switzerland)
Segmentation of the fetus from 2-dimensional (2D) magnetic resonance imaging (MRI) can aid radiologists with clinical decision making for disease diagnosis. Machine learning can facilitate this process of automatic segmentation, making diagnosis more...

Representation Learning for Fine-Grained Change Detection.

Sensors (Basel, Switzerland)
Fine-grained change detection in sensor data is very challenging for artificial intelligence though it is critically important in practice. It is the process of identifying differences in the state of an object or phenomenon where the differences are...

Identification of subtypes of anticancer peptides based on sequential features and physicochemical properties.

Scientific reports
Anticancer peptides (ACPs) are a kind of bioactive peptides which could be used as a novel type of anticancer drug that has several advantages over chemistry-based drug, including high specificity, strong tumor penetration capacity, and low toxicity ...