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)
Mar 26, 2015
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...
Large-scale plasma proteomic profiling unveils diagnostic biomarkers and pathways for Alzheimer's disease.
Nature aging
Jun 1, 2025
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
Apr 25, 2025
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
Jul 25, 2024
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...
Performance of Deep Learning in the Interpretation of Serum Protein Electrophoresis.
Clinical chemistry
Oct 6, 2022
In Reply to Performance of Deep Learning in the Interpretation of Serum Protein Electrophoresis.
Clinical chemistry
Oct 6, 2022
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)
Jan 27, 2022
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
Oct 1, 2021
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
Aug 1, 2020
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 ...
Proteomics Analysis of FLT3-ITD Mutation in Acute Myeloid Leukemia Using Deep Learning Neural Network.
Annals of clinical and laboratory science
Jan 1, 2019
Deep Learning can significantly benefit cancer proteomics and genomics. In this study, we attempted to determine a set of critical proteins that were associated with the FLT3-ITD mutation in newly-diagnosed acute myeloid leukemia patients. A Deep Lea...
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