AIMC Topic: Machine Learning

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Reconstructing 3D histological structures using machine learning (artificial intelligence) algorithms.

Pathologie (Heidelberg, Germany)
BACKGROUND: Histomorphometry is currently the gold standard for bone microarchitectural examinations. This relies on two-dimensional (2D) sections to deduce the spatial properties of structures. Micromorphometric parameters are calculated from these ...

Assessment of non-fatal injuries among university students in Hainan: a machine learning approach to exploring key factors.

Frontiers in public health
BACKGROUND: Injuries constitute a significant global public health concern, particularly among individuals aged 0-34. These injuries are affected by various social, psychological, and physiological factors and are no longer viewed merely as accidenta...

Prediction of breast cancer Invasive Disease Events using transfer learning on clinical data as image-form.

PloS one
BACKGROUND AND OBJECTIVE: Detecting patients at high risk of occurrence of an Invasive Disease Event after a first diagnosis of breast cancer, such as recurrence, distant metastasis, contralateral tumor and second tumor, could support clinical decisi...

MERIT: Accurate Prediction of Multi Ligand-binding Residues with Hybrid Deep Transformer Network, Evolutionary Couplings and Transfer Learning.

Journal of molecular biology
Multi-ligand binding residues (MLBRs) are amino acids in protein sequences that interact with multiple different ligands that include proteins, peptides, nucleic acids, and a variety of small molecules. MLBRs are implicated in a number of cellular fu...

Continual learning in the presence of repetition.

Neural networks : the official journal of the International Neural Network Society
Continual learning (CL) provides a framework for training models in ever-evolving environments. Although re-occurrence of previously seen objects or tasks is common in real-world problems, the concept of repetition in the data stream is not often con...

Prioritization of monitoring compounds from SNTS identified organic micropollutants in contaminated groundwater using a machine learning optimized ToxPi model.

Water research
Advanced suspect and non-target screening (SNTS) approach can identify a large number of potential hazardous micropollutants in groundwater, underscoring the need for pinpointing priority pollutants among detected chemicals. This present study theref...

Advances in diagnosis and prognosis of bacteraemia, bloodstream infection, and sepsis using machine learning: A comprehensive living literature review.

Artificial intelligence in medicine
BACKGROUND: Blood-related infections are a significant concern in healthcare. They can lead to serious medical complications and even death if not promptly diagnosed and treated. Throughout time, medical research has sought to identify clinical facto...

Rethinking density ratio estimation based hyper-parameter optimization.

Neural networks : the official journal of the International Neural Network Society
Hyper-parameter optimization (HPO) aims to improve the performance of machine learning algorithms by identifying appropriate hyper-parameters. By converting the computation of expected improvement into density-ratio estimation problems, existing work...

Enhancing Open-Set Domain Adaptation through Optimal Transport and Adversarial Learning.

Neural networks : the official journal of the International Neural Network Society
Open-Set Domain Adaptation (OSDA) is designed to facilitate the transfer of knowledge from a source domain to a target domain, where the class space of the source is a subset of the target. The primary challenge in OSDA is the identification of share...