AIMC Topic: Proteomics

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Uncovering injury-specific proteomic signatures and neurodegenerative risks in single and repetitive traumatic brain injury.

Signal transduction and targeted therapy
Traumatic brain injury (TBI) is a major public health concern associated with an increased risk of neurodegenerative diseases including Alzheimer's disease (AD), Parkinson's disease (PD), and chronic traumatic encephalopathy, yet the underlying molec...

Discovery of milk-derived antimicrobial peptides in human milk by DeepMAMP based on peptidomics technology and deep learning method.

Food chemistry
Milk-derived antimicrobial peptides (MAMPs) in human milk (HMAMPs) play an important role in the nutrition and the immune system construction of newborns. Current AMP prediction models cannot accurately predict HMAMPs, thus high-throughput and target...

Pan-omics insights into abiotic stress responses: bridging functional genomics and precision crop breeding.

Functional & integrative genomics
Crop production has been regarded as the major goal of agricultural activities, but the rapidly growing population and climate change have become more complex in the agricultural systems. Abiotic stress greatly affects crop productivity globally; dev...

Plasma proteomic profiles for early detection and risk stratification of non-small cell lung carcinoma: A prospective cohort study with 52,913 participants.

International journal of cancer
Early detection of non-small cell lung cancer (NSCLC) can improve survival rates, and plasma proteomics may provide effective tools for risk prediction. The population for this study included 52,913 participants and 2911 plasma proteomics from UK Bio...

Machine learning and multi-omics analysis reveal key regulators of proneural-mesenchymal transition in glioblastoma.

Scientific reports
Glioblastoma (GBM) is classified into subtypes according to the molecular expression profile; the proneural subtype has a relatively good prognosis, and the mesenchymal type is the most aggressive form with the worst prognosis. GBM undergoes proneura...

Identification of key proteins and pathways in myocardial infarction using machine learning approaches.

Scientific reports
Acute myocardial infarction (AMI) is a leading cause of global morbidity and mortality, requiring deeper insights into its molecular mechanisms for improved diagnosis and treatment. This study combines proteomics, transcriptomics and machine learning...

A robust multiplex-DIA workflow profiles protein turnover regulations associated with cisplatin resistance and aneuploidy.

Nature communications
Quantifying protein turnover is fundamental to understanding cellular processes and advancing drug discovery. Multiplex-DIA mass spectrometry (MS), combined with dynamic SILAC labeling (pulse-SILAC, or pSILAC) reliably measures protein turnover and d...

Integrated plasma and vegetation proteomic characterization of infective endocarditis for early diagnosis and treatment.

Nature communications
Infective endocarditis, a life-threatening condition, poses challenges for early diagnosis and personalized treatment due to insufficient biomarkers and limited understanding of its pathophysiology. Here, we performed proteomic profiling of plasma an...

Explainability of Protein Deep Learning Models.

International journal of molecular sciences
Protein embeddings are the new main source of information about proteins, producing state-of-the-art solutions to many problems, including protein interaction prediction, a fundamental issue in proteomics. Understanding the embeddings and what causes...