AIMC Topic: Biomarkers

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MicroRNA classification and discovery for major depressive disorder diagnosis: Towards a robust and interpretable machine learning approach.

Journal of affective disorders
BACKGROUND: Major depressive disorder (MDD) is notably underdiagnosed and undertreated due to its complex nature and subjective diagnostic methods. Biomarker identification would help provide a clearer understanding of MDD aetiology. Although machine...

Lipoproteins and metabolites in diagnosing and predicting Alzheimer's disease using machine learning.

Lipids in health and disease
BACKGROUND: Alzheimer's disease (AD) is a chronic neurodegenerative disorder that poses a substantial economic burden. The Random forest algorithm is effective in predicting AD; however, the key factors influencing AD onset remain unclear. This study...

Pilot-Study to Explore Metabolic Signature of Type 2 Diabetes: A Pipeline of Tree-Based Machine Learning and Bioinformatics Techniques for Biomarkers Discovery.

Nutrients
BACKGROUND: This study aims to identify unique metabolomics biomarkers associated with Type 2 Diabetes (T2D) and develop an accurate diagnostics model using tree-based machine learning (ML) algorithms integrated with bioinformatics techniques.

Alzheimer's disease early screening and staged detection with plasma proteome using machine learning and convolutional neural network.

The European journal of neuroscience
Alzheimer's disease (AD) stands as the prevalent progressive neurodegenerative disease, precipitating cognitive impairment and even memory loss. Amyloid biomarkers have been extensively used in the diagnosis of AD. However, amyloid proteins offer lim...

Visualization strategies to aid interpretation of high-dimensional genotoxicity data.

Environmental and molecular mutagenesis
This article describes a range of high-dimensional data visualization strategies that we have explored for their ability to complement machine learning algorithm predictions derived from MultiFlow® assay results. For this exercise, we focused on seve...

ADHD classification with cross-dataset feature selection for biomarker consistency detection.

Journal of neural engineering
Attention deficit hyperactivity disorder (ADHD) is a prevalent neurodevelopmental disorder in children. While numerous intelligent methods are applied for its subjective diagnosis, they seldom consider the consistency problem of ADHD biomarkers. In p...

New Diagnostic Tools for Pulmonary Embolism Detection.

Methodist DeBakey cardiovascular journal
The presentation of pulmonary embolism (PE) varies from asymptomatic to life-threatening, and management involves multiple specialists. Timely diagnosis of PE is based on clinical presentation, D-dimer testing, and computed tomography pulmonary angio...

Optimizing deep learning-based segmentation of densely packed cells using cell surface markers.

BMC medical informatics and decision making
BACKGROUND: Spatial molecular profiling depends on accurate cell segmentation. Identification and quantitation of individual cells in dense tissues, e.g. highly inflamed tissue caused by viral infection or immune reaction, remains a challenge.

Machine Learning Approach to Metabolomic Data Predicts Type 2 Diabetes Mellitus Incidence.

International journal of molecular sciences
Metabolomics, with its wealth of data, offers a valuable avenue for enhancing predictions and decision-making in diabetes. This observational study aimed to leverage machine learning (ML) algorithms to predict the 4-year risk of developing type 2 dia...

Algorithms for predicting COVID outcome using ready-to-use laboratorial and clinical data.

Frontiers in public health
The pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is an emerging crisis affecting the public health system. The clinical features of COVID-19 can range from an asymptomatic state to acute respiratory syndrome and mul...