AIMC Topic: Algorithms

Clear Filters Showing 10511 to 10520 of 28713 articles

Automatic Segmentation and Classification for Antinuclear Antibody Images Based on Deep Learning.

Computational intelligence and neuroscience
Antinuclear antibodies (ANAs) testing is the main serological diagnosis screening test for autoimmune diseases. ANAs testing is conducted principally by the indirect immunofluorescence (IIF) on human epithelial cell-substrate (HEp-2) protocol. Howeve...

NeuroPpred-SVM: A New Model for Predicting Neuropeptides Based on Embeddings of BERT.

Journal of proteome research
Neuropeptides play pivotal roles in different physiological processes and are related to different kinds of diseases. Identification of neuropeptides is of great benefit for studying the mechanism of these physiological processes and the treatment of...

Systematic Evaluation of Local and Global Machine Learning Models for the Prediction of ADME Properties.

Molecular pharmaceutics
Machine learning (ML) has become an indispensable tool to predict absorption, distribution, metabolism, and excretion (ADME) properties in pharmaceutical research. ML algorithms are trained on molecular structures and corresponding ADME assay data to...

Toward an Integrated Machine Learning Model of a Proteomics Experiment.

Journal of proteome research
In recent years machine learning has made extensive progress in modeling many aspects of mass spectrometry data. We brought together proteomics data generators, repository managers, and machine learning experts in a workshop with the goals to evaluat...

Artificial intelligence-based tools applied to pathological diagnosis of microbiological diseases.

Pathology, research and practice
Infectious diseases still threaten the global community, especially in resource-limited countries. An accurate diagnosis is paramount to proper patient and public health management. Identification of many microbes still relies on manual microscopic e...

Deep compressed sensing MRI via a gradient-enhanced fusion model.

Medical physics
BACKGROUND: Compressed sensing has been employed to accelerate magnetic resonance imaging by sampling fewer measurements. However, conventional iterative optimization-based CS-MRI are time-consuming for iterative calculations and often share poor gen...

Cross Dataset Analysis for Generalizability of HRV-Based Stress Detection Models.

Sensors (Basel, Switzerland)
Stress is an increasingly prevalent mental health condition across the world. In Europe, for example, stress is considered one of the most common health problems, and over USD 300 billion are spent on stress treatments annually. Therefore, monitoring...

An Efficient Feature Selection Algorithm for Gene Families Using NMF and ReliefF.

Genes
Gene families, which are parts of a genome's information storage hierarchy, play a significant role in the development and diversity of multicellular organisms. Several studies have focused on the characteristics of gene families, such as function, h...

Ultra-low-dose CT lung screening with artificial intelligence iterative reconstruction: evaluation via automatic nodule-detection software.

Clinical radiology
AIM: To test the feasibility of ultra-low-dose (ULD) computed tomography (CT) combined with an artificial intelligence iterative reconstruction (AIIR) algorithm for screening pulmonary nodules using computer-assisted diagnosis (CAD).

Use of semi-synthetic data for catheter segmentation improvement.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
In the era of data-driven machine learning algorithms, data is the new oil. For the most optimal results, datasets should be large, heterogeneous and, crucially, correctly labeled. However, data collection and labeling are time-consuming and labor-in...