Hunter Moseley

The paper identifies PLK1-mediated phosphorylation of PHGDH as a mechanism driving serine metabolism reprogramming in advanced prostate cancer, increasing reliance on exogenous serine uptake.

The recount3 Python package provides programmatic access to uniformly processed RNA-seq data, enabling efficient use in Python-based computational pipelines.

The paper introduces ICI-Kt, a method to interpret left-censored missing values in metabolomics as informative, improving outlier detection and network construction.

The paper updates the mwtab Python library and mwFileStatusWebsite to improve validation and support growing metabolomics datasets in the Metabolomics Workbench.

The paper trains and evaluates machine learning models to predict pathway associations of metabolites using MetaCyc data, achieving performance comparable to KEGG-based models.

The paper improves pathway enrichment analysis of metabolomics datasets by generating high-quality predicted metabolite-pathway annotations, increasing the number of detected enriched pathways by 8-fold.

The paper trains a model to predict compound pathway involvement across KEGG, Reactome, and MetaCyc using standardized chemical representations, achieving high accuracy.

The paper predicts pathway involvement of unannotated compounds using Reactome data, achieving higher accuracy than KEGG-based models.

The paper predicts pathway involvement for all KEGG pathway and compound entries using machine learning, achieving higher performance than metabolic pathways alone.

The paper introduces a machine learning model that predicts metabolite associations with KEGG Level 3 pathways, achieving high accuracy and transfer learning to Level 2 categories.

The paper predicts metabolite pathway involvement using a single binary classifier with combined metabolite and pathway features, outperforming previous methods.

The paper evaluates an erroneous KEGG-derived dataset used in machine learning for predicting metabolite-to-pathway mappings and shows how duplicates inflated model performance.

md_harmonize harmonizes compounds and metabolic reactions across databases by generating unique identifiers based on atom structures for improved metabolic network analysis.

The paper develops a new benchmark dataset for predicting metabolite pathway involvement using machine learning models and evaluates their performance.

The paper describes a website that validates metabolomics data files in the Metabolomics Workbench and provides a tool for format validation and error detection.

MESSES transforms messy research datasets into clean submissions for the Metabolomics Workbench, improving metadata capture and ease of deposition.

kegg_pull provides a Python package with API and CLI for efficient and flexible retrieval of KEGG database entries.

Academic Tracker is a software tool that tracks and reports peer-reviewed publications for authors and grants, helping to meet federal funding agency requirements.

The paper introduces a scan-centric method for characterizing peaks in mass spectrometry data, improving the accuracy of isotope abundance analysis.

The paper harmonizes metabolic reactions and compounds across databases by improving atom-level consistency and establishing hierarchical relationships.

The paper introduces a graph coloring method to generate atom identifiers for compounds, enabling compound harmonization across metabolic databases and improving metabolic network integration.

The paper develops a method to convert electron density maps into units of electrons, enabling biochemical interpretation of structural quality in PDB entries.

GOcats organizes Gene Ontology into user-defined subgraphs, improving annotation accuracy compared to existing methods.

The paper analyzes vertebrate sperm protamine sequences to identify conserved residues and proposes dityrosine or cysteine-tyrosine cross-linking as mechanisms for protamine structure in metatherians.

The paper develops a software package for selecting metabolic models using isotopologue data from SIRM experiments, demonstrating its effectiveness with UDP-GlcNAc datasets.

The paper introduces a framework for deconvolving isotopologue profiles using moiety models and provides Python packages for optimization and model selection.

The paper identifies high-quality metal ion-centric regions in the wwPDB by evaluating structural quality using electron density maps and four quality criteria.

This paper introduces GOcats, a tool that improves gene ontology enrichment by handling relations more accurately, showing significant improvements in two gene-expression datasets.

The paper develops a machine learning method to classify molecular formula assignments into lipid categories using SMIRFE data, achieving high accuracy and precision.

The paper identifies and addresses high peak density artifacts in Fourier transform mass spectra, which can mislead data analysis by creating false peaks.