AIMC Topic: Blood Proteins
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Identifying proteomic prognostic markers for Alzheimer's disease with survival machine learning: The Framingham Heart Study.
The journal of prevention of Alzheimer's disease
Jan 1, 2025
BACKGROUND: Protein abundance levels, sensitive to both physiological changes and external interventions, are useful for assessing the Alzheimer's disease (AD) risk and treatment efficacy. However, identifying proteomic prognostic markers for AD is c...
The predictive value of heparin-binding protein for bacterial infections in patients with severe polytrauma.
PloS one
Dec 26, 2024
INTRODUCTION: Heparin-binding protein is an inflammatory factor with predictive value for sepsis and participates in the inflammatory response through antibacterial effects, chemotaxis, and increased vascular permeability. The role of heparin-binding...
Artificial intelligence aided serum protein electrophoresis analysis of Finnish patient samples: Retrospective validation.
Clinica chimica acta; international journal of clinical chemistry
Dec 9, 2024
BACKGROUND AND AIMS: Serum protein electrophoresis interpretation requires a substantial amount of manual work. In 2020, Chabrun et al. created a machine learning method called SPECTR for the task. We aimed to validate and test the SPECTR method agai...
The state-of-the-art machine learning model for plasma protein binding prediction: Computational modeling with OCHEM and experimental validation.
European journal of pharmaceutical sciences : official journal of the European Federation for Pharmaceutical Sciences
Oct 28, 2024
Plasma protein binding (PPB) is closely related to pharmacokinetics, pharmacodynamics and drug toxicity. Existing models for predicting PPB often suffer from low prediction accuracy and poor interpretability, especially for high PPB compounds, and ar...
Plasma protein-based identification of neuroimage-driven subtypes in mild cognitive impairment via protein-protein interaction aware explainable graph propagational network.
Computers in biology and medicine
Oct 28, 2024
As an early indicator of dementia, mild cognitive impairment (MCI) requires specialized treatment according to its subtypes for the effective prevention and management of dementia progression. Based on the neuropathological characteristics, MCI can b...
The revolution in high-throughput proteomics and AI.
Science (New York, N.Y.)
Sep 26, 2024
The recent capability to measure thousands of plasma proteins from a tiny blood sample has provided a new dimension of expansive data that can advance our understanding of human health. For example, the company SomaLogic has developed the means to me...
GraphBNC: Machine Learning-Aided Prediction of Interactions Between Metal Nanoclusters and Blood Proteins.
Advanced materials (Deerfield Beach, Fla.)
Sep 24, 2024
Hybrid nanostructures between biomolecules and inorganic nanomaterials constitute a largely unexplored field of research, with the potential for novel applications in bioimaging, biosensing, and nanomedicine. Developing such applications relies criti...
Machine learning approach to predict blood-secretory proteins and potential biomarkers for liver cancer using omics data.
Journal of proteomics
Aug 30, 2024
Identifying non-invasive blood-based biomarkers is crucial for early detection and monitoring of liver cancer (LC), thereby improving patient outcomes. This study leveraged computational approaches to predict potential blood-based biomarkers for LC. ...
Using Data-Driven Algorithms with Large-Scale Plasma Proteomic Data to Discover Novel Biomarkers for Diagnosing Depression.
Journal of proteome research
Aug 16, 2024
Given recent technological advances in proteomics, it is now possible to quantify plasma proteomes in large cohorts of patients to screen for biomarkers and to guide the early diagnosis and treatment of depression. Here we used CatBoost machine learn...
Prediction method of pharmacokinetic parameters of small molecule drugs based on GCN network model.
Journal of molecular modeling
Jul 12, 2024
CONTEXT: Accurately predicting plasma protein binding rate (PPBR) and oral bioavailability (OBA) helps to better reveal the absorption and distribution of drugs in the human body and subsequent drug design. Although machine learning models have achie...
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