AIMC Topic: Algorithms

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Improving sepsis classification performance with artificial intelligence algorithms: A comprehensive overview of healthcare applications.

Journal of critical care
PURPOSE: This study investigates the potential of machine learning (ML) algorithms in improving sepsis diagnosis and prediction, focusing on their relevance in healthcare decision-making. The primary objective is to contribute to healthcare decision-...

DeforT: Deformable transformer for visual tracking.

Neural networks : the official journal of the International Neural Network Society
Most trackers formulate visual tracking as common classification and regression (i.e., bounding box regression) tasks. Correlation features that are computed through depth-wise convolution or channel-wise multiplication operations are input into both...

MLm5C: A high-precision human RNA 5-methylcytosine sites predictor based on a combination of hybrid machine learning models.

Methods (San Diego, Calif.)
RNA modification serves as a pivotal component in numerous biological processes. Among the prevalent modifications, 5-methylcytosine (m5C) significantly influences mRNA export, translation efficiency and cell differentiation and are also associated w...

Semi-supervised multi-modal medical image segmentation with unified translation.

Computers in biology and medicine
The two major challenges to deep-learning-based medical image segmentation are multi-modality and a lack of expert annotations. Existing semi-supervised segmentation models can mitigate the problem of insufficient annotations by utilizing a small amo...

Rapid determination of starch and alcohol contents in fermented grains by hyperspectral imaging combined with data fusion techniques.

Journal of food science
Starch and alcohol serve as pivotal indicators in assessing the quality of lees fermentation. In this paper, two hyperspectral imaging (HSI) techniques (visible-near-infrared (Vis-NIR) and NIR) were utilized to acquire separate HSI data, which were t...

Phenome-wide identification of therapeutic genetic targets, leveraging knowledge graphs, graph neural networks, and UK Biobank data.

Science advances
The ongoing expansion of human genomic datasets propels therapeutic target identification; however, extracting gene-disease associations from gene annotations remains challenging. Here, we introduce Mantis-ML 2.0, a framework integrating AstraZeneca'...

Transforming Poultry Farming: A Pyramid Vision Transformer Approach for Accurate Chicken Counting in Smart Farm Environments.

Sensors (Basel, Switzerland)
Smart farm environments, equipped with cutting-edge technology, require proficient techniques for managing poultry. This research investigates automated chicken counting, an essential part of optimizing livestock conditions. By integrating artificial...

Prediction of Drug-Target Affinity Using Attention Neural Network.

International journal of molecular sciences
Studying drug-target interactions (DTIs) is the foundational and crucial phase in drug discovery. Biochemical experiments, while being the most reliable method for determining drug-target affinity (DTA), are time-consuming and costly, making it chall...

Machine learning-based algorithm identifies key mitochondria-related genes in non-alcoholic steatohepatitis.

Lipids in health and disease
BACKGROUND: Evidence suggests that hepatocyte mitochondrial dysfunction leads to abnormal lipid metabolism, redox imbalance, and programmed cell death, driving the onset and progression of non-alcoholic steatohepatitis (NASH). Identifying hub mitocho...

Development and external validation of a multimodal integrated feature neural network (MIFNN) for the diagnosis of malignancy in small pulmonary nodules (≤10 mm).

Biomedical physics & engineering express
. Current lung cancer screening protocols primarily evaluate pulmonary nodules, yet often neglect the malignancy risk associated with small nodules (≤10 mm). This study endeavors to optimize the management of pulmonary nodules in this population by d...