AIMC Topic: Blood Proteins

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Identification of a small set of plasma signalling proteins using neural network for prediction of Alzheimer's disease.

Bioinformatics (Oxford, England)
MOTIVATION: Alzheimer's disease (AD) is a dementia that gets worse with time resulting in loss of memory and cognitive functions. The life expectancy of AD patients following diagnosis is ∼7 years. In 2006, researchers estimated that 0.40% of the wor...

Circulating Proteomic Panels for Noninvasive Diagnosis and Prognostication of Thromboangiitis Obliterans.

Journal of proteome research
Thromboangiitis obliterans (TAO) is often diagnosed late and characterized by high amputation rates. TAO-specific early diagnostic and disease-staging biomarkers are urgently needed. A staged mass spectrometry (MS)-based discovery-verification-valida...

Large-scale plasma proteomic profiling unveils diagnostic biomarkers and pathways for Alzheimer's disease.

Nature aging
Proteomic studies have been instrumental in identifying brain, cerebrospinal fluid and plasma proteins associated with Alzheimer's disease (AD). Here, we comprehensively examined 6,905 aptamers corresponding to 6,106 unique proteins in plasma in more...

[Serum proteomics and machine learning unveil new diagnostic biomarkers for tuberculosis in adolescents and young adults].

Sheng wu gong cheng xue bao = Chinese journal of biotechnology
Adolescents and young adults (AYAs) are one of the major populations susceptible to tuberculosis. However, little is known about the unique characteristics and diagnostic biomarkers of tuberculosis in this population. In this study, 81 AYAs were recr...

Identification of molecular subtypes of dementia by using blood-proteins interaction-aware graph propagational network.

Briefings in bioinformatics
Plasma protein biomarkers have been considered promising tools for diagnosing dementia subtypes due to their low variability, cost-effectiveness, and minimal invasiveness in diagnostic procedures. Machine learning (ML) methods have been applied to en...

In Reply to Performance of Deep Learning in the Interpretation of Serum Protein Electrophoresis.

Clinical chemistry
We thank He et al. for their comments on our article (1), which gives us the opportunity to clarify some methodological points. 1. Detection of abnormal patterns: mechanics.

Plasma protein binding prediction focusing on residue-level features and circularity of cyclic peptides by deep learning.

Bioinformatics (Oxford, England)
MOTIVATION: In recent years, cyclic peptide drugs have been receiving increasing attention because they can target proteins that are difficult to be tackled by conventional small-molecule drugs or antibody drugs. Plasma protein binding rate (%PPB) is...

Achieving Expert-Level Interpretation of Serum Protein Electrophoresis through Deep Learning Driven by Human Reasoning.

Clinical chemistry
BACKGROUND: Serum protein electrophoresis (SPE) is a common clinical laboratory test, mainly indicated for the diagnosis and follow-up of monoclonal gammopathies. A time-consuming and potentially subjective human expertise is required for SPE analysi...

Natural Language Processing of Serum Protein Electrophoresis Reports in the Veterans Affairs Health Care System.

JCO clinical cancer informatics
PURPOSE: Serum protein electrophoresis (SPEP) is a clinical tool used to screen for monoclonal gammopathy, thus it is a critical tool in the evaluation of patients with multiple myeloma. However, SPEP laboratory results are usually returned as short ...