Posters on the topic "New Technology: AI and Bioinformatics in Mass Spectrometry"

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  • P-II-0445
Extending PSM rescoring for timsTOF data to MSFragger with Oktoberfest
  • Charlotte Adams (Antwerp / BE)
  • P-II-0446
Tree-based quantification retrieves proteoforms from bottom-up proteomics data
  • P-II-0447
Zero-shot retention time prediction for unseen post-translational modifications with molecular structure encodings
  • P-II-0448
Mixed-species based computational simulation of proteoform groups
  • P-II-0449
Towards open modification search with MS Andrea
  • P-II-0450
DIAgui a shiny app to process the output from DIA-NN
  • P-II-0451
Evaluation of different rescoring platforms to increase peptide identifications
  • P-II-0452
pi-SPECFormer: pretraining improves spectrum embedding through contrastive learning with transformer
  • P-II-0453
MassDash: a web-based dashboard for data-independent acquisition mass spectrometry visualization
  • P-II-0454
imputomics 2.0: bridging data gaps in proteomics with missing value imputation algorithms and their benchmark
  • P-II-0455
Phosphorylation site prediction model using structural context
  • P-II-0456
Tesorai Search: Large pretrained model boosts peptide identifications without the need for Percolator
  • P-II-0457
DIA-NN: enabling peptidoform confidence in DIA proteomics
  • P-II-0458
IM2Deep predicts collisional cross-sections of modified and multiconformational peptide ions
  • P-II-0459
Faster and more accurate intensity-based PTM localization in Chimerys
  • P-II-0460
Altimeter and pioneer: fast and open-source software for analyzing narrow-window data-independent acquisition proteomics experiments
  • P-II-0461
Effect of sample size on statistical accessibility of proteins and enrichment analysis in proteomics experiments
  • P-II-0462
Feature screening and model regularization for k-mer representations of biological sequences
  • P-II-0463
NA deconvolution analysis(NADA) promotes excavation of unique proteins and protein-protein interactions from proteomics datasets
  • P-II-0464
Deep learning for MALDI-MSI: towards a common framework for transferable tissue diagnostics
  • P-II-0465
FLASHTagger: an open-source web application for ion type- and precursor mass-free protein identification in top-down mass spectrometry
  • P-II-0466
Inferring dynamic interaction network landscape with deep graph contrastive representation learning
  • P-II-0467
XL-Prosit: accurate fragment intensity prediction and enhanced cross-linked peptide identification for protein structural and interaction studies
  • P-II-0468
TIMSrescore: timsTOF-optimized PSM rescoring boosts identification rates for immunopeptidomics
  • P-II-0469
MHC validator boosts unbiased discovery of conserved T-Cell epitope vaccine candidates through improved immunopeptidomics sensitivity
  • P-II-0470
midiaID: a flexible pipeline for the analysis of midiaPASEF data
  • P-II-0471
A novel supervised learning algorithm for real-time collision energy selection to optimize peptide fragmentation in mass spectrometry
  • P-II-0472
GPMelt: a versatile and interpretable Gaussian process-based statistical framework for analyzing thermal proteome profiling datasets
  • P-II-0473
A Shrinkage-based statistical method for testing group mean differences in quantitative bottom-up proteomics
  • P-II-0474
Transforming de novo peptide sequencing by explainable AI
  • P-II-0475
MS-BERT: a pre-trained model for data-independent acquisition mass spectrometry-based proteomics data analysis
  • P-II-0476
MsBayesImpute: a versatile framework for handling missing values in mass-spectrometry data through a combined Bayesian factorization and probabilistic dropout model
  • P-II-0477
Identifying and quantifying the unknown. Two workflows combined to search for variant/mutation information and to quantify un-/identified spectra in MS-based proteomics
  • P-II-0478
Statistical benchmark of Label-free DIA quantification using the generic integration algorithm with iSanXoT
  • P-II-0479
SpectroPipeR – an R package for the downstream analysis of Spectronaut DIA-MS data
  • P-II-0480
Systems-wide Functional Characterization of Time-resolved Protein Dynamics by Mass Spectrometry
  • P-II-0481
Enhancing peptide-spectrum match identification: leveraging mokapot with nonlinear machine learning algorithms to increase PSMs, with consideration for false positives
  • P-II-0482
Direct spectrum-centric analysis of diaPASEF data using diaTracer and FragPipe computational platform
  • P-II-0483
Artificial Intelligence in mass spectrometry: fascinating past, exciting present and promising future
  • P-II-0484
Optimization of peptide selection for absolute protein quantification using QconCATs
  • P-II-0485
Aiproteomics: a Python package to facilitate the use of deep learning for DIA-MS-based phosphoproteomics data processing
  • P-II-0486
CURTAIN – a unique web-based tool for exploration and sharing of MS-based proteomics data
  • P-II-0487
DIGging into the DIGestion
  • P-II-0488
An infinite well: harmonizing all public proteomics data for machine learning
  • P-II-0489
AI-powered Onco-Knowledge Graph (OncoKG) for Precision Medicine
  • P-II-0490
Optimizing search engines to detect marginal peptide matches using large-scale matched-matrix "truth" datasets
  • P-II-0491
ProteomicsDB: connecting proteomes across species
  • P-II-0493
A CCS-centric HLA-specific trained de novo module for precise and accurate real-time immunopeptide identification on the Bruker ProteoScape platform
  • P-II-0494
The benefit of in silico predicted spectral libraries in data-independent acquisition data analysis workflows
  • P-II-0495
TimSim – a simulation framework for PASEF-like timsTOF proteomics mass spectrometry raw data
  • P-II-0497
SWAPS: a joint peptide identification and quantification approach to increase the depth and coverage of LC-MS experiments
  • P-II-0498
Benchmarking deep learning models and classical de novo sequencing tools for immunopeptidomics
  • P-II-0499
Benchmarking predictions models for improved peptide identification with MSBooster and Koina
  • P-II-0500
Enhancing metaproteomics analysis with deep learning prediction to uncover new microbial communities in deep-sea hydrothermal vents
  • P-II-0501
Unleash the power of Hybrid-DIA data analysis with AI-driven software for biomarker discovery and validation in translational research
  • P-II-0502
SPverse: an R package for data analysis of spatial proteomics derived from micro-FFPE tissues
  • P-II-0503
Enhancing protein analysis with the confident PTM/Mutation algorithm
  • P-II-0504
Deep learning prediction of Glycopeptide tandem mass spectra powers Glycoproteomics
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