AIMC Topic: Machine Learning

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Identify MRI negative temporal lobe epilepsy with resting fMRI indicators and machine learning techniques.

Scientific reports
About 30% of temporal lobe epilepsy (TLE) cases are negative on MRI, so quantitative diagnosis based on clinical symptoms becomes challenging. There is an urgent need for an accurate and reliable method to differentiate patients with MRI-negative TLE...

Prefrontal-bed nucleus of the stria terminalis physiological and neuropsychological biomarkers predict therapeutic outcomes in depression.

Nature communications
Therapeutic options for refractory depression are urgently needed. We conducted a deep brain stimulation (DBS) randomized controlled trial of the bed nucleus of the stria terminalis (BNST), an extended amygdala structure, and nucleus accumbens (NAc) ...

Development and external validation of a machine learning model to predict high flow nasal cannula failure.

BMJ open respiratory research
INTRODUCTION: High-flow nasal cannula (HFNC) is an important treatment option for acute hypoxic respiratory failure and can improve outcomes. However, patients on a prolonged duration of HFNC have worse clinical outcomes and increased mortality. It i...

Next-generation antifungal peptide discovery: the synergy of artificial intelligence and omics technologies.

World journal of microbiology & biotechnology
There is a growing concern about fungal infections and antifungal resistance among fungal species, underscoring the need for finding alternative treatments. Antifungal peptides (AFPs) are interesting and promising candidates for developing novel anti...

Spatiotemporal evolution of water quality in long-distance water supply projects: an improved PSO-SVR model.

Environmental monitoring and assessment
The spatiotemporal evolution of water quality is fundamental for the management of water resources in long-distance water transfer projects (LWTPs). Due to the multidimensional and nonlinear characteristics of water quality monitoring data, a novel m...

Integrated machine learning and single-cell analysis identify chromatin-remodeling gene signature for diagnosis and prognosis in nasopharyngeal carcinoma.

Clinical and experimental medicine
This study examines the function of chromatin-remodeling genes (CRGs) in nasopharyngeal carcinoma (NPC), with an emphasis on their potential as prognostic and diagnostic biomarkers. We examined gene expression information collected from multiple data...

Predicting distant metastasis in early-onset kidney cancer using machine learning: a SEER database study with external validation.

Clinical and experimental medicine
Patients with early-onset kidney cancer (EOKC) face a marked decline in prognosis after distant metastasis, yet the accuracy of current predictive methods remains limited. This study aims to develop a predictive model using multiple machine learning ...

Demand forecasting and inventory optimization of distribution equipment: A fusion model based on genetic algorithm and machine learning.

PloS one
To improve the intelligent and refined management level of power distribution systems in equipment operation and maintenance as well as emergency support, this work proposes an integrated "prediction-optimization" model that combines genetic algorith...

FormulationLAI: A physiology-based machine learning framework for accelerated development of long-acting injectable formulations.

Journal of controlled release : official journal of the Controlled Release Society
Long-acting injectables (LAIs) represent promising drug delivery platforms for chronic diseases management, but their clinical translation remains constrained by extremely long trial-and-error experiments (8-10 years), limited mechanism insights, and...

Best Practices for Machine Learning-Assisted Protein Engineering.

Journal of chemical information and modeling
Data-driven modeling based on machine learning (ML) is becoming a central component of protein engineering workflows. This perspective presents the elements necessary to develop effective, reliable, and reproducible ML models, and a set of guidelines...