AIMC Topic: Algorithms

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Identifying Protein Phosphorylation Site-Disease Associations Based on Multi-Similarity Fusion and Negative Sample Selection by Convolutional Neural Network.

Interdisciplinary sciences, computational life sciences
As one of the most important post-translational modifications (PTMs), protein phosphorylation plays a key role in a variety of biological processes. Many studies have shown that protein phosphorylation is associated with various human diseases. There...

Learning vs. understanding: When does artificial intelligence outperform process-based modeling in soil organic carbon prediction?

New biotechnology
In recent years, machine learning (ML) algorithms have gained substantial recognition for ecological modeling across various temporal and spatial scales. However, little evaluation has been conducted for the prediction of soil organic carbon (SOC) on...

Prognosis of COVID-19 severity using DERGA, a novel machine learning algorithm.

European journal of internal medicine
It is important to determine the risk for admission to the intensive care unit (ICU) in patients with COVID-19 presenting at the emergency department. Using artificial neural networks, we propose a new Data Ensemble Refinement Greedy Algorithm (DERGA...

The scope of artificial intelligence in retinopathy of prematurity (ROP) management.

Indian journal of ophthalmology
Artificial Intelligence (AI) is a revolutionary technology that has the potential to develop into a widely implemented system that could reduce the dependence on qualified professionals/experts for screening the large at-risk population, especially i...

Algorithmic Identification of Treatment-Emergent Adverse Events From Clinical Notes Using Large Language Models: A Pilot Study in Inflammatory Bowel Disease.

Clinical pharmacology and therapeutics
Outpatient clinical notes are a rich source of information regarding drug safety. However, data in these notes are currently underutilized for pharmacovigilance due to methodological limitations in text mining. Large language models (LLMs) like Bidir...

GraphsformerCPI: Graph Transformer for Compound-Protein Interaction Prediction.

Interdisciplinary sciences, computational life sciences
Accurately predicting compound-protein interactions (CPI) is a critical task in computer-aided drug design. In recent years, the exponential growth of compound activity and biomedical data has highlighted the need for efficient and interpretable pred...

Modular Spiking Neural Membrane Systems for Image Classification.

International journal of neural systems
A variant of membrane computing models called Spiking Neural P systems (SNP systems) closely mimics the structure and behavior of biological neurons. As third-generation neural networks, SNP systems have flexible architectures allowing the design of ...

Coarse-Grained Crystal Graph Neural Networks for Reticular Materials Design.

Journal of chemical information and modeling
Reticular materials, including metal-organic frameworks and covalent organic frameworks, combine the relative ease of synthesis and an impressive range of applications in various fields from gas storage to biomedicine. Diverse properties arise from t...

Algorithms for Liver Segmentation in Computed Tomography Scans: A Historical Perspective.

Sensors (Basel, Switzerland)
Oncology has emerged as a crucial field of study in the domain of medicine. Computed tomography has gained widespread adoption as a radiological modality for the identification and characterisation of pathologies, particularly in oncology, enabling p...

Deep self-supervised machine learning algorithms with a novel feature elimination and selection approaches for blood test-based multi-dimensional health risks classification.

BMC bioinformatics
BACKGROUND: Blood test is extensively performed for screening, diagnoses and surveillance purposes. Although it is possible to automatically evaluate the raw blood test data with the advanced deep self-supervised machine learning approaches, it has n...