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- Peakbin selection in mass spectrometry data using a consensus approach with estimation of distribution algorithms (1 revision)
- Software platform for high-throughput glycomics (1 revision)
- Analysing microbiome intervention design studies: Comparison of alternative multivariate statistical methods (1 revision)
- Detecting and overcoming systematic bias in high-throughput screening technologies: a comprehensive review of practical issues and methodological solutions (1 revision)
- Performance Evaluation and Online Realization of Data-driven Normalization Methods Used in LC/MS based Untargeted Metabolomics Analysis (1 revision)
- Test title (1 revision)
- Assessment of statistical methods from single cell, bulk RNA-seq, and metagenomics applied to microbiome data (1 revision)
- Comparison of metaheuristic strategies for peakbin selection in proteomic mass spectrometry data (1 revision)
- Distribution-based comprehensive evaluation of methods for differential expression analysis in metatranscriptomics (1 revision)
- Identification and Correction of Additive and Multiplicative Spatial Biases in Experimental High-Throughput Screening (1 revision)
- Benchmark of data processing methods and machine learning models for gut microbiome-based diagnosis of inflammatory bowel disease (1 revision)
- Identifying and quantifying metabolites by scoring peaks of GC-MS data (1 revision)
- Mockrobiota: a public resource for microbiome bioinformatics benchmarking (1 revision)
- 20 Benchmark Models: (1 revision - redirect page)
- Improved Peak Detection and Deconvolution of Native Electrospray Mass Spectra from Large Protein Complexes (1 revision)
- Preprocessing of tandem mass spectrometric data to support automatic protein identification (1 revision)
- The impact of sample imbalance on identifying differentially expressed genes (1 revision)
- 20 Benchmark Problem for Modelling Intracellular Processes (1 revision - redirect page)
- Benchmarking Metagenomics Tools for Taxonomic Classification (1 revision)
- Comprehensive benchmarking and ensemble approaches for metagenomic classifiers (1 revision)
