AIMC Topic: Machine Learning

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Automated assessment of cardiac dynamics in aging and dilated cardiomyopathy Drosophila models using machine learning.

Communications biology
The Drosophila model is pivotal in deciphering the pathophysiological underpinnings of various human ailments, notably aging and cardiovascular diseases. Cutting-edge imaging techniques and physiology yield vast high-resolution videos, demanding adva...

Developing a prognostic model using machine learning for disulfidptosis related lncRNA in lung adenocarcinoma.

Scientific reports
Disulfidptosis represents a novel cell death mechanism triggered by disulfide stress, with potential implications for advancements in cancer treatments. Although emerging evidence highlights the critical regulatory roles of long non-coding RNAs (lncR...

Breaking new ground: can artificial intelligence and machine learning transform papillary glioneuronal tumor diagnosis?

Neurosurgical review
Papillary glioneuronal tumors (PGNTs), classified as Grade I by the WHO in 2016, present diagnostic challenges due to their rarity and potential for malignancy. Xiaodan Du et al.'s recent study of 36 confirmed PGNT cases provides critical insights in...

Identification of Multiclass Pediatric Low-Grade Neuroepithelial Tumor Molecular Subtype with ADC MR Imaging and Machine Learning.

AJNR. American journal of neuroradiology
BACKGROUND AND PURPOSE: Molecular biomarker identification increasingly influences the treatment planning of pediatric low-grade neuroepithelial tumors (PLGNTs). We aimed to develop and validate a radiomics-based ADC signature predictive of the molec...

Machine learning prediction of malaria vaccine efficacy based on antibody profiles.

PLoS computational biology
Immunization through repeated direct venous inoculation of Plasmodium falciparum (Pf) sporozoites (PfSPZ) under chloroquine chemoprophylaxis, using the PfSPZ Chemoprophylaxis Vaccine (PfSPZ-CVac), induces high-level protection against controlled huma...

Economic benefit analysis of lithium battery recycling based on machine learning algorithm.

PloS one
Lithium batteries, as an important energy storage device, are widely used in the fields of renewable vehicles and renewable energy. The related lithium battery recycling industry has also ushered in a golden period of development. However, the high c...

A Dynamic Window Method Based on Reinforcement Learning for SSVEP Recognition.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Steady-state visual evoked potential (SSVEP) is one of the most used brain-computer interface (BCI) paradigms. Conventional methods analyze SSVEPs at a fixed window length. Compared with these methods, dynamic window methods can achieve a higher info...

Mining for Potent Inhibitors through Artificial Intelligence and Physics: A Unified Methodology for Ligand Based and Structure Based Drug Design.

Journal of chemical information and modeling
Determining the viability of a new drug molecule is a time- and resource-intensive task that makes computer-aided assessments a vital approach to rapid drug discovery. Here we develop a machine learning algorithm, iMiner, that generates novel inhibit...

Multi-machine Learning Model Based on Habitat Subregions for Outcome Prediction in Adenomyosis Treated by Uterine Artery Embolization.

Academic radiology
RATIONALE AND OBJECTIVES: To establish and validate a predictive multi-machine learning model for the long-term efficacy of uterine artery embolization (UAE) in the treatment of adenomyosis based on habitat subregions.

Identification of Prolactinoma in Pituitary Neuroendocrine Tumors Using Radiomics Analysis Based on Multiparameter MRI.

Journal of imaging informatics in medicine
This study aims to investigate the feasibility of preoperatively predicting histological subtypes of pituitary neuroendocrine tumors (PitNETs) using machine learning and radiomics based on multiparameter MRI. Patients with PitNETs from January 2016 t...