AIMC Topic: Machine Learning

Clear Filters Showing 7671 to 7680 of 34417 articles

Integrated machine learning to predict the prognosis of lung adenocarcinoma patients based on SARS-COV-2 and lung adenocarcinoma crosstalk genes.

Cancer science
Viruses are widely recognized to be intricately associated with both solid and hematological malignancies in humans. The primary goal of this research is to elucidate the interplay of genes between SARS-CoV-2 infection and lung adenocarcinoma (LUAD),...

Predicting the Reparability of Rotator Cuff Tears: Machine Learning and Comparison With Previous Scoring Systems.

The American journal of sports medicine
BACKGROUND: Repair of rotator cuff tear is not always feasible, depending on the severity. Although several studies have investigated factors related to reparability and various methods to predict it, inconsistent scoring methods and a lack of valida...

Machine-Learning-Assisted Materials Discovery from Electronic Band Structure.

Journal of chemical information and modeling
Traditional methods of materials discovery, often relying on intuition and trial-and-error experimentation, are time-consuming and limited in their ability to explore the vast design space effectively. The emergence of machine learning (ML) as a powe...

Machine Learning Framework for Conotoxin Class and Molecular Target Prediction.

Toxins
Conotoxins are small and highly potent neurotoxic peptides derived from the venom of marine cone snails which have captured the interest of the scientific community due to their pharmacological potential. These toxins display significant sequence and...

Quantification of Size-Binned Particulate Matter in Electronic Cigarette Aerosols Using Multi-Spectral Optical Sensing and Machine Learning.

Sensors (Basel, Switzerland)
To monitor health risks associated with vaping, we introduce a multi-spectral optical sensor powered by machine learning for real-time characterization of electronic cigarette aerosols. The sensor can accurately measure the mass of particulate matter...

Multimodal AI/ML for discovering novel biomarkers and predicting disease using multi-omics profiles of patients with cardiovascular diseases.

Scientific reports
Cardiovascular diseases (CVDs) are complex, multifactorial conditions that require personalized assessment and treatment. Advancements in multi-omics technologies, namely RNA sequencing and whole-genome sequencing, have provided translational researc...

Predicting the risk of pulmonary infection after kidney transplantation using machine learning methods: a retrospective cohort study.

International urology and nephrology
PURPOSE: Pulmonary infection is the most common and serious complication after kidney transplantation that affects the survival of the transplanted kidney and the quality of life of patients. This study aims to construct a machine learning model for ...

Detection of carcinoembryonic antigen specificity using microwave biosensor with machine learning.

Biosensors & bioelectronics
Early diagnosis and screening of tumor markers are essential for effective cancer treatment and improve the treatment efficiency and prognosis of tumor recurrence and metastasis. In this study, a split-ring resonator (SRR) circuit based on an interdi...

Combining array-assisted SERS microfluidic chips and machine learning algorithms for clinical leukemia phenotyping.

Talanta
The disease progression and treatment options of leukemia between different subtypes vary considerably, emphasizing the importance of phenotyping. However, early typing of leukemia remains challenging due to the lack of highly sensitive and specific ...

Combination of plasma-based lipidomics and machine learning provides a useful diagnostic tool for ovarian cancer.

Journal of pharmaceutical and biomedical analysis
Ovarian cancer (OC), the second leading cause of death among gynecological cancers, is often diagnosed at an advanced stage due to its asymptomatic nature at early stages. This study aimed to explore the diagnostic potential of plasma-based lipidomic...