AIMC Topic: Biomarkers

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Integrative Analysis of Proteomic, Glycomic, and Metabolomic Data for Biomarker Discovery.

IEEE journal of biomedical and health informatics
Studies associating changes in the levels of multiple biomolecules including proteins, glycans, glycoproteins, and metabolites with the onset of cancer have been widely investigated to identify clinically relevant diagnostic biomarkers. Advances in l...

Neurodegenerative disease: MRI biomarkers - a precision medicine tool in neurology?

Nature reviews. Neurology
Two new studies highlight the potential of neuroimaging to aid the differential diagnosis of neurodegenerative disease, for both clinical practice and emerging trials. Although this approach holds great promise, meaningful implementation of neuroimag...

Applications of Deep Learning in Biomedicine.

Molecular pharmaceutics
Increases in throughput and installed base of biomedical research equipment led to a massive accumulation of -omics data known to be highly variable, high-dimensional, and sourced from multiple often incompatible data platforms. While this data may b...

Translational informatics approach for identifying the functional molecular communicators linking coronary artery disease, infection and inflammation.

Molecular medicine reports
Translational informatics approaches are required for the integration of diverse and accumulating data to enable the administration of effective translational medicine specifically in complex diseases such as coronary artery disease (CAD). In the cur...

Automatic Detection of Cerebral Microbleeds From MR Images via 3D Convolutional Neural Networks.

IEEE transactions on medical imaging
Cerebral microbleeds (CMBs) are small haemorrhages nearby blood vessels. They have been recognized as important diagnostic biomarkers for many cerebrovascular diseases and cognitive dysfunctions. In current clinical routine, CMBs are manually labelle...

Fusing Data Mining, Machine Learning and Traditional Statistics to Detect Biomarkers Associated with Depression.

PloS one
BACKGROUND: Atheoretical large-scale data mining techniques using machine learning algorithms have promise in the analysis of large epidemiological datasets. This study illustrates the use of a hybrid methodology for variable selection that took acco...

Unbiased estimation of biomarker panel performance when combining training and testing data in a group sequential design.

Biometrics
Motivated by an ongoing study to develop a screening test able to identify patients with undiagnosed Sjögren's Syndrome in a symptomatic population, we propose methodology to combine multiple biomarkers and evaluate their performance in a two-stage g...

Serum phosphate as an additional marker for initiating hemodialysis in patients with advanced chronic kidney disease.

Biomedical journal
BACKGROUND: Reconsidering when to initiate renal replacement therapy (RRT) in patients with chronic kidney disease (CKD) has been emphasized recently. With evolving modern aged and diabetes-prone populations, conventional markers of uremia are not su...

A Systematic Strategy for Screening and Application of Specific Biomarkers in Hepatotoxicity Using Metabolomics Combined With ROC Curves and SVMs.

Toxicological sciences : an official journal of the Society of Toxicology
Current studies that evaluate toxicity based on metabolomics have primarily focused on the screening of biomarkers while largely neglecting further verification and biomarker applications. For this reason, we used drug-induced hepatotoxicity as an ex...

Genotoxic mode of action predictions from a multiplexed flow cytometric assay and a machine learning approach.

Environmental and molecular mutagenesis
Several endpoints associated with cellular responses to DNA damage as well as overt cytotoxicity were multiplexed into a miniaturized, "add and read" type flow cytometric assay. Reagents included a detergent to liberate nuclei, RNase and propidium io...