AIMC Topic: Algorithms

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Ensemble Machine Learning and Predicted Properties Promote Antimicrobial Peptide Identification.

Interdisciplinary sciences, computational life sciences
The emergence of antibiotic-resistant microbes raises a pressing demand for novel alternative treatments. One promising alternative is the antimicrobial peptides (AMPs), a class of innate immunity mediators within the therapeutic peptide realm. AMPs ...

Masked cross-domain self-supervised deep learning framework for photoacoustic computed tomography reconstruction.

Neural networks : the official journal of the International Neural Network Society
Accurate image reconstruction is crucial for photoacoustic (PA) computed tomography (PACT). Recently, deep learning has been used to reconstruct PA images with a supervised scheme, which requires high-quality images as ground truth labels. However, p...

Improving prediction of blood cancer using leukemia microarray gene data and Chi2 features with weighted convolutional neural network.

Scientific reports
Blood cancer has emerged as a growing concern over the past decade, necessitating early diagnosis for timely and effective treatment. The present diagnostic method, which involves a battery of tests and medical experts, is costly and time-consuming. ...

Comprehensive assessment of machine learning methods for diagnosing gastrointestinal diseases through whole metagenome sequencing data.

Gut microbes
The gut microbiome, linked significantly to host diseases, offers potential for disease diagnosis through machine learning (ML) pipelines. These pipelines, crucial in modeling diseases using high-dimensional microbiome data, involve selecting profile...

ConKeD: multiview contrastive descriptor learning for keypoint-based retinal image registration.

Medical & biological engineering & computing
Retinal image registration is of utmost importance due to its wide applications in medical practice. In this context, we propose ConKeD, a novel deep learning approach to learn descriptors for retinal image registration. In contrast to current regist...

Shuffling-type gradient method with bandwidth-based step sizes for finite-sum optimization.

Neural networks : the official journal of the International Neural Network Society
Shuffling-type gradient method is a popular machine learning algorithm that solves finite-sum optimization problems by randomly shuffling samples during iterations. In this paper, we explore the convergence properties of shuffling-type gradient metho...

PLEASING: Exploring the historical and potential events for temporal knowledge graph reasoning.

Neural networks : the official journal of the International Neural Network Society
Temporal Knowledge Graphs (TKGs) enable effective modeling of knowledge dynamics and event evolution, facilitating deeper insights and analysis into temporal information. Recently, extrapolation of TKG reasoning has attracted great significance due t...

A Delayed Spiking Neural Membrane System for Adaptive Nearest Neighbor-Based Density Peak Clustering.

International journal of neural systems
Although the density peak clustering (DPC) algorithm can effectively distribute samples and quickly identify noise points, it lacks adaptability and cannot consider the local data structure. In addition, clustering algorithms generally suffer from hi...

Robust optimization of a novel ultraviolet (UV) photoreactor for water disinfection: A neural network approach.

Chemosphere
To optimize the ultraviolet (UV) water disinfection process, it is crucial to determine the ideal geometric dimensions of a corresponding model that enhance performance while minimizing the impact of uncertain photoreactor inputs. As water treatment ...

A novel way to prospectively evaluate of AI-enhanced ECG algorithms.

Journal of electrocardiology
Significant strides will be made in the field of computerized electrocardiology through the development of artificial intelligence (AI)-enhanced ECG (AI-ECG) algorithms. Yet, the scientific discourse has primarily relied upon on retrospective analyse...