AIMC Topic: Machine Learning

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PeptideForest: Semisupervised Machine Learning Integrating Multiple Search Engines for Peptide Identification.

Journal of proteome research
The first step in bottom-up proteomics is the assignment of measured fragmentation mass spectra to peptide sequences, also known as peptide spectrum matches. In recent years novel algorithms have pushed the assignment to new heights; unfortunately, d...

Machine learning-driven Raman spectroscopy: A novel approach to lipid profiling in diabetic kidney disease.

Nanomedicine : nanotechnology, biology, and medicine
Diabetes mellitus is a chronic metabolic disease that increasingly affects people every year. It is known that with its progression and poor management, metabolic changes can lead to organ dysfunctions, including kidneys. The study aimed to combine R...

Subway opening enables urban green development: Evidence from difference-in-differences and double dual machine learning methods.

Journal of environmental management
Urban rail transport is an important part of transport infrastructure, which is significant in empowering green-oriented urban development. However, few studies in the existing literature explore the effect of subway opening on urban green developmen...

Functional Disability and Psychological Impact in Headache Patients: A Comparative Study Using Conventional Statistics and Machine Learning Analysis.

Medicina (Kaunas, Lithuania)
: Recent research has focused on exploring the relationships between various factors associated with headaches and understanding their impact on individuals' psychological states. Utilizing statistical methods and machine learning models, these studi...

Stacking Ensemble Deep Learning for Real-Time Intrusion Detection in IoMT Environments.

Sensors (Basel, Switzerland)
The Internet of Medical Things (IoMT) is revolutionizing healthcare by enabling advanced patient care through interconnected medical devices and systems. However, its critical role and sensitive data make it a prime target for cyber threats, requirin...

Robust Automated Harmonization of Heterogeneous Data Through Ensemble Machine Learning: Algorithm Development and Validation Study.

JMIR medical informatics
BACKGROUND: Cohort studies contain rich clinical data across large and diverse patient populations and are a common source of observational data for clinical research. Because large scale cohort studies are both time and resource intensive, one alter...

Death risk prediction model for patients with non-traumatic intracerebral hemorrhage.

BMC medical informatics and decision making
BACKGROUND: This study aimed to assess the risk of death from non-traumatic intracerebral hemorrhage (ICH) using a machine learning model.

Identification of depressive symptoms in adolescents using machine learning combining childhood and adolescence features.

BMC public health
BACKGROUND: Depressive symptoms in adolescents can significantly affect their daily lives and pose risks to their future development. These symptoms may be linked to various factors experienced during both childhood and adolescence. Machine learning ...

Ensemble machine learning models for lung cancer incidence risk prediction in the elderly: a retrospective longitudinal study.

BMC cancer
BACKGROUND: Identifying high risk factors and predicting lung cancer incidence risk are essential to prevention and intervention of lung cancer for the elderly. We aim to develop lung cancer incidence risk prediction model in the elderly to facilitat...