Results for “mass-spectrometry”

Process and analyze mass spectrometry data: import spectra from MGF, mzML, MSP, and JSON formats; apply 40+ filters for metadata harmonization and peak cleaning; compute spectral similarities (cosine, modified cosine) for compound identification; build reproducible processing pipelines.

Analyze proteomics and metabolomics mass spectrometry data with PyOpenMS: read/write MS file formats, process spectra, detect and quantify features, identify peptides and proteins, and run end-to-end LC-MS/MS pipelines using ready-to-run scripts.

Generates spectrograms and multi-panel audio feature visualizations (mel, chroma, MFCC) from audio files via a Go CLI.

Evaluates the trade-off between file size reduction and image fidelity when encoding radio astronomy data using JPEG2000, benchmarking both lossless and lossy compression modes to determine the compression ratio at which visual artifacts first appear.

Screen US stocks for Stockbee-style short-term momentum burst setups using 4% breakout, dollar breakout, range expansion, volume expansion, prior range contraction, close-location, failure filters, and risk-distance scoring.

Generates spectrograms and multi-panel audio feature visualizations from audio files via a command-line tool.

Guides analysis of Neuropixels recordings from raw data to curated units, covering preprocessing, motion correction, spike sorting, quality metrics, and export.

Provides a standardized interface for ingesting raw data across domains such as genomics, network analysis, document review, and spatial mapping, converting it into semantic vectors for agent use.

Convert chemical structures (SMILES or RDKit molecules) into numerical representations for machine learning, covering 100+ featurizers including ECFP, MACCS, descriptors, and pretrained models like ChemBERTa, with support for QSAR modeling and virtual screening.