AIMC Topic: Proteomics

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MAGPIE: A Machine Learning Approach to Decipher Protein-Protein Interactions in Human Plasma.

Journal of proteome research
Immunoprecipitation coupled to tandem mass spectrometry (IP-MS/MS) methods are often used to identify protein-protein interactions (PPIs). While these approaches are prone to false positive identifications through contamination and antibody nonspecif...

A Plasma Proteomics-Based Model for Identifying the Risk of Postpartum Depression Using Machine Learning.

Journal of proteome research
Postpartum depression (PPD) poses significant risks to maternal and infant health, yet proteomic analyses of PPD-risk women remain limited. This study analyzed plasma samples from 30 healthy postpartum women and 30 PPD-risk women using mass spectrome...

π-PrimeNovo: an accurate and efficient non-autoregressive deep learning model for de novo peptide sequencing.

Nature communications
Peptide sequencing via tandem mass spectrometry (MS/MS) is essential in proteomics. Unlike traditional database searches, deep learning excels at de novo peptide sequencing, even for peptides missing from existing databases. Current deep learning mod...

Identifying proteomic prognostic markers for Alzheimer's disease with survival machine learning: The Framingham Heart Study.

The journal of prevention of Alzheimer's disease
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...

NovoRank: Refinement for Peptide Sequencing Based on Spectral Clustering and Deep Learning.

Journal of proteome research
peptide sequencing is a valuable technique in mass-spectrometry-based proteomics, as it deduces peptide sequences directly from tandem mass spectra without relying on sequence databases. This database-independent method, however, relies solely on im...

ViroNia: LSTM based proteomics model for precise prediction of HCV.

Computers in biology and medicine
Classification of viruses carries important implications in terms of understanding their evolution and the designing of interventions. This study introduces ViroNia as a novel LSTM-based system specifically meant for high-accuracy classification of v...

Proteome profiling of cerebrospinal fluid using machine learning shows a unique protein signature associated with APOE4 genotype.

Aging cell
Proteome changes associated with APOE4 variant carriage that are independent of Alzheimer's disease (AD) pathology and diagnosis are unknown. This study investigated APOE4 proteome changes in people with AD, mild cognitive impairment, and no impairme...

Faecal metaproteomics analysis reveals a high cardiovascular risk profile across healthy individuals and heart failure patients.

Gut microbes
The gut microbiota is a crucial link between diet and cardiovascular disease (CVD). Using fecal metaproteomics, a method that concurrently captures human gut and microbiome proteins, we determined the crosstalk between gut microbiome, diet, gut healt...

DFASGCNS: A prognostic model for ovarian cancer prediction based on dual fusion channels and stacked graph convolution.

PloS one
Ovarian cancer is a malignant tumor with different clinicopathological and molecular characteristics. Due to its nonspecific early symptoms, the majority of patients are diagnosed with local or extensive metastasis, severely affecting treatment and p...

DeepPD: A Deep Learning Method for Predicting Peptide Detectability Based on Multi-feature Representation and Information Bottleneck.

Interdisciplinary sciences, computational life sciences
Peptide detectability measures the relationship between the protein composition and abundance in the sample and the peptides identified during the analytical procedure. This relationship has significant implications for the fundamental tasks of prote...