AIMC Topic: Algorithms

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Machine learning-based prediction of antimicrobial resistance and identification of AMR-related SNPs in Mycobacterium tuberculosis.

BMC genomic data
BACKGROUND: Mycobacterium tuberculosis (MTB) is a human-specific pathogen that primarily infects humans, causing tuberculosis (TB). Antimicrobial resistance (AMR) in MTB presents a formidable challenge to global health. The employment of machine lear...

Neural network based AI model for lung health assessment.

Scientific reports
Treating pulmonary diseases is pivotal in healthcare since they are the third leading cause of mortality globally. To aid medical experts in diagnosis, various studies have been conducted using artificial intelligence (AI) compatible devices to analy...

TECM-ChI: A TECM network-based method for chromatin interaction prediction.

Gene
Chromatin interactions refer to regulatory relationships formed between chromatin regions through physical contact or spatial proximity, playing a crucial role in genome function, structure, and the development of diseases. In cancer research, for ex...

Tiny-objective segmentation for spot signs on multi-phase CT angiography via contrastive learning with dynamic-updated positive-negative memory banks.

Computers in biology and medicine
BACKGROUND AND OBJECTIVE: Presence of spot sign on CT Angiography (CTA) is associated with hematoma growth in patients with intracerebral hemorrhage. Measuring spot sign volume over time may aid to predict hematoma expansion. Due to the difficulties ...

Deep homo-heterogeneous association mining with hybrid scholars and multidimensional mixed moment networks: Embedding-Driven prediction of microbe-drug interactions.

Computers in biology and medicine
Drug repurposing accelerates microbial therapy development by bypassing the costly and time-consuming traditional drug discovery process. However, existing computational methods for predicting drug-microbe associations (MDAs) struggle to capture comp...

Enhancing Parkinson's disease prediction using meta-heuristic optimized machine learning models.

Personalized medicine
Parkinson's disease is a progressive neurological disorder affecting movement and cognition. Early detection is crucial but challenging with traditional methods. This study applies meta-heuristic optimization to enhance machine learning prediction mo...

Implementing Large Language Models in Health Care: Clinician-Focused Review With Interactive Guideline.

Journal of medical Internet research
BACKGROUND: Large language models (LLMs) can generate outputs understandable by humans, such as answers to medical questions and radiology reports. With the rapid development of LLMs, clinicians face a growing challenge in determining the most suitab...

A new method for ranking q-rung ortho-pair fuzzy numbers and application.

PloS one
The effectiveness of the q-rung ortho-pair fuzzy multi-attribute decision-making method is primarily influenced by the q-rung ortho-pair fuzzy number ranking method. This paper conducts an in-depth analysis of the shortcomings of eight existing q-run...

Optimizing EV charging stations and power trading with deep learning and path optimization.

PloS one
The rapid growth of electric vehicles (EVs) presents significant challenges for power grids, particularly in managing fluctuating demand and optimizing the placement of charging infrastructure. This study proposes an integrated framework combining de...

Modelling key ecological factors influencing the distribution and content of silymarin antioxidant in Silybum marianum L.

PloS one
The increasing demand for natural medicine has increased the significance of Silybum marianum as a valuable medicinal plant. It is used to restore liver cells; reduce blood cholesterol; prevent prostate, skin, and breast cancer; and protect cervical ...