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Proteomics

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DeepPhospho accelerates DIA phosphoproteome profiling through in silico library generation.

Nature communications
Phosphoproteomics integrating data-independent acquisition (DIA) enables deep phosphoproteome profiling with improved quantification reproducibility and accuracy compared to data-dependent acquisition (DDA)-based phosphoproteomics. DIA data mining he...

pValid 2: A deep learning based validation method for peptide identification in shotgun proteomics with increased discriminating power.

Journal of proteomics
Tandem mass spectrometry has been the principal method in shotgun proteomics for peptide and protein identification. However, incorrect identifications reported by proteome search engines are still unknown, and further validation methods are needed. ...

Deep-Learning-Derived Evaluation Metrics Enable Effective Benchmarking of Computational Tools for Phosphopeptide Identification.

Molecular & cellular proteomics : MCP
Tandem mass spectrometry (MS/MS)-based phosphoproteomics is a powerful technology for global phosphorylation analysis. However, applying four computational pipelines to a typical mass spectrometry (MS)-based phosphoproteomic dataset from a human canc...

Shotgun Proteomics Sample Processing Automated by an Open-Source Lab Robot.

Journal of visualized experiments : JoVE
Mass spectrometry-based shotgun proteomics experiments require multiple sample preparation steps, including enzymatic protein digestion and clean-up, which can take up significant person-hours of bench labor and present a source of batch-to-batch var...

Machine Learning Modeling from Omics Data as Prospective Tool for Improvement of Inflammatory Bowel Disease Diagnosis and Clinical Classifications.

Genes
Research of inflammatory bowel disease (IBD) has identified numerous molecular players involved in the disease development. Even so, the understanding of IBD is incomplete, while disease treatment is still far from the precision medicine. Reliable di...

Automated assignment of cell identity from single-cell multiplexed imaging and proteomic data.

Cell systems
A major challenge in the analysis of highly multiplexed imaging data is the assignment of cells to a priori known cell types. Existing approaches typically solve this by clustering cells followed by manual annotation. However, these often require sev...

Personalised Medicine for Colorectal Cancer Using Mechanism-Based Machine Learning Models.

International journal of molecular sciences
Gaining insight into the mechanisms of signal transduction networks (STNs) by using critical features from patient-specific mathematical models can improve patient stratification and help to identify potential drug targets. To achieve this, these mod...

Time-resolved in vivo ubiquitinome profiling by DIA-MS reveals USP7 targets on a proteome-wide scale.

Nature communications
Mass spectrometry (MS)-based ubiquitinomics provides system-level understanding of ubiquitin signaling. Here we present a scalable workflow for deep and precise in vivo ubiquitinome profiling, coupling an improved sample preparation protocol with dat...

Neuroinflammation and Alzheimer's Disease: A Machine Learning Approach to CSF Proteomics.

Cells
In Alzheimer's disease (AD), the contribution of pathophysiological mechanisms other than amyloidosis and tauopathy is now widely recognized, although not clearly quantifiable by means of fluid biomarkers. We aimed to identify quantifiable protein bi...

Automated machine learning optimizes and accelerates predictive modeling from COVID-19 high throughput datasets.

Scientific reports
COVID-19 outbreak brings intense pressure on healthcare systems, with an urgent demand for effective diagnostic, prognostic and therapeutic procedures. Here, we employed Automated Machine Learning (AutoML) to analyze three publicly available high thr...