AIMC Topic: Proteomics

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Using Data-Driven Algorithms with Large-Scale Plasma Proteomic Data to Discover Novel Biomarkers for Diagnosing Depression.

Journal of proteome research
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...

Identification of COL3A1 as a candidate protein involved in the crosstalk between obesity and diarrhea using quantitative proteomics and machine learning.

European journal of pharmacology
BACKGROUND: Increasing epidemiologic studies have shown a positive correlation between obesity and chronic diarrhea. Nevertheless, the precise etiology remains uncertain.

High-resolution proteomics and machine-learning identify protein classifiers of honey made by Sicilian black honeybees (Apis mellifera ssp. sicula).

Food research international (Ottawa, Ont.)
Apis mellifera ssp. sicula, also known as the Sicilian black honeybee, is a Slow Food Presidium that produces honey with outstanding nutraceutical properties, including high antioxidant capacity. In this study, we used high-resolution proteomics to p...

Derivation and external validation of mass spectrometry-based proteomic model using machine learning algorithms to predict plaque rupture in patients with acute coronary syndrome.

Clinica chimica acta; international journal of clinical chemistry
BACKGROUND: A poor prognosis is associated with atherosclerotic plaque rupture (PR) despite after conventional therapy for patients with acute coronary syndrome (ACS). Timely identification of PR improves the risk stratification and prognosis of ACS ...

Combined High-Throughput Proteomics and Random Forest Machine-Learning Approach Differentiates and Classifies Metabolic, Immune, Signaling and ECM Intra-Tumor Heterogeneity of Colorectal Cancer.

Cells
Colorectal cancer (CRC) is a frequent, worldwide tumor described for its huge complexity, including inter-/intra-heterogeneity and tumor microenvironment (TME) variability. Intra-tumor heterogeneity and its connections with metabolic reprogramming an...

Proteomics and machine learning in the prediction and explanation of low pectoralis muscle area.

Scientific reports
Low muscle mass is associated with numerous adverse outcomes independent of other associated comorbid diseases. We aimed to predict and understand an individual's risk for developing low muscle mass using proteomics and machine learning. We identifie...

Mass Spectrometric and Artificial Intelligence-Based Identification of the Secretome of Plasmodium falciparum Merozoites to Provide Novel Candidates for Vaccine Development Pipeline.

Proteomics. Clinical applications
PURPOSE: Merozoites are the only extracellular form of blood stage parasites, making it a worthwhile target. Multiple invasins that are stored in the merozoite apical organelles, are secreted just prior to invasion, and mediates its interaction with ...

pyRBDome: a comprehensive computational platform for enhancing RNA-binding proteome data.

Life science alliance
High-throughput proteomics approaches have revolutionised the identification of RNA-binding proteins (RBPome) and RNA-binding sequences (RBDome) across organisms. Yet, the extent of noise, including false positives, associated with these methodologie...

Machine Learning Strategies to Tackle Data Challenges in Mass Spectrometry-Based Proteomics.

Journal of the American Society for Mass Spectrometry
In computational proteomics, machine learning (ML) has emerged as a vital tool for enhancing data analysis. Despite significant advancements, the diversity of ML model architectures and the complexity of proteomics data present substantial challenges...

Computational approaches for clinical, genomic and proteomic markers of response to glucagon-like peptide-1 therapy in type-2 diabetes mellitus: An exploratory analysis with machine learning algorithms.

Diabetes & metabolic syndrome
INTRODUCTION: In 2021, the International Diabetes Federation reported that 537 million people worldwide are living with diabetes. While glucagon-like peptide-1 agonists provide significant benefits in diabetes management, approximately 40% of patient...