AIMC Topic: Proteomics

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A novel potential biomarker panel to diagnose depression derived from big proteomic data.

Journal of affective disorders
BACKGROUND: There is still no clinical biomarker to diagnose depression. Given the complexity of a multifactorial disease like depression, a single biomarker is unlikely to capture the full heterogeneity of the disease and be applicable in clinical p...

Enhancing peptide identification in metaproteomics through curriculum learning in deep learning.

Nature communications
Metaproteomics offers a powerful window into the active functions of microbial communities, but accurately identifying peptides remains challenging due to the size and incompleteness of protein databases derived from metagenomes. These databases ofte...

Spatial proteomics for investigating solid tumor resistance mechanisms.

Cancer metastasis reviews
Spatial proteomics technologies have been pivotal in profiling tumor immune microenvironments at single-cell resolution, advancing our understanding of cancer biology, identifying key cell populations in solid tumors, and predicting treatment respons...

Discovering Biomarkers for Asymptomatic Tuberculosis via Olink Proteomics and Machine Learning.

Journal of proteome research
The diagnosis of asymptomatic tuberculosis (TB) remains challenging due to an early disease stage. This study aimed to identify and validate plasma biomarkers for asymptomatic TB by integrating the Olink proteomics with multiple machine learning algo...

AI-driven discovery of novel extracellular matrix biomarkers in pelvic organ prolapse.

PLoS computational biology
Deep learning for protein function prediction faces significant challenges in identifying disease-specific proteins. We present Extracellular Matrix Protein Predictor (EPOP), an advanced transfer learning framework leveraging protein language models ...

Unsupervised Machine Learning for Differential Analysis in Proteomics.

Analytical chemistry
Differential analysis in proteomics is pivotal for biomarker discovery and disease mechanism elucidation, yet traditional statistical methods are constrained by distributional assumptions and empirical fold change threshold dependencies. This study s...

The Identification of Biological Stains at Crime Scenes: A Promising Role for Proteomics and Machine Learning.

Analytical chemistry
Forensic body fluid identification is crucial for reconstructing crime scene events. While DNA analysis provides individualization, it lacks information about the fluid's origin. We developed and evaluated three complementary proteomic approaches usi...

Multimodal landscape of atherosclerotic plaques: A spatial omics approach with mass spectrometry imaging.

Analytica chimica acta
Atherosclerotic plaques are complex and heterogeneous structures, originating as fatty streaks in the vasculature and formed by the accumulation of lipids and foam cells. Over time, these lesions progress as inflammation, smooth muscle cell prolifera...

BrainProt v3.0: An Integrative and Simplified Omics-Based Knowledge-Base About the Human Brain and Its Associated Diseases.

Journal of proteome research
The advancements in neuroscience research and omics technologies generate extensive data for brain-related diseases and disorders that are scattered across various manuscript repositories and databases, potentially hindering global initiatives to adv...

Integrative Omics and AI-Driven Systems Biology: Multilayer Networks Decoding Health and Resilience.

Journal of proteome research
Honey bees () are vital pollinators essential for maintaining ecosystem stability and global food production, but they face escalating threats from pathogens, agrochemicals, and climate change. Although proteomics has advanced our understanding of be...