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- Review, evaluation, and discussion of the challenges of missing value imputation for mass spectrometry-based label-free global proteomics (14:44, 25 February 2020)
- Recursive partitioning for missing data imputation in the presence of interaction effects (14:46, 25 February 2020)
- Optimization and profile calculation of ODE models using second order adjoint sensitivity analysis (14:48, 25 February 2020)
- NOREVA: normalization and evaluation of MS-based metabolomics data (14:58, 25 February 2020)
- Evaluation of Derivative-Free Optimizers for Parameter Estimation in Systems Biology (15:01, 25 February 2020)
- OpenMS: a flexible open-source software platform for mass spectrometry data analysis (15:04, 25 February 2020)
- Performance Evaluation and Online Realization of Data-driven Normalization Methods Used in LC/MS based Untargeted Metabolomics Analysis (15:09, 25 February 2020)
- A comparison of methods for quantifying prediction uncertainty in systems biology (15:13, 25 February 2020)
- Benchmark problems for dynamic modeling of intracellular processes (15:13, 25 February 2020)
- Fast derivatives of likelihood functionals for ODE based models using adjoint-state method (15:20, 25 February 2020)
- Data processing has major impact on the outcome of quantitative label-free LC-MS analysis (15:21, 25 February 2020)
- Missing value estimation methods for DNA microarrays (15:22, 25 February 2020)
- Parameter Estimation and Variable Selection for Big Systems of Linear Ordinary Differential Equations: A Matrix-Based Approach (15:34, 25 February 2020)
- A systematic study of normalization methods for Infinium 450K methylation data using whole-genome bisulfite sequencing data (15:36, 25 February 2020)
- Robust calibration of hierarchical population models for heterogeneous cell populations (15:38, 25 February 2020)
- Efficient parameterization of large-scale dynamic models based on relative measurements (15:39, 25 February 2020)
- Testing structural identifiability by a simple scaling method (15:40, 25 February 2020)
- Tracking for parameter and state estimation in possibly misspecified partially observed linear Ordinary Differential Equations (15:40, 25 February 2020)
- A general modular framework for gene set enrichment analysis (15:40, 25 February 2020)
- Mini-batch optimization enables training of ODE models on large-scale datasets (15:50, 25 February 2020)
- Toward a gold standard for benchmarking gene set enrichment analysis (15:57, 25 February 2020)
- Peakbin selection in mass spectrometry data using a consensus approach with estimation of distribution algorithms (16:24, 25 February 2020)
- Comparison of metaheuristic strategies for peakbin selection in proteomic mass spectrometry data (16:29, 25 February 2020)
- An improved algorithm for peak detection in mass spectra based on continuous wavelet transform (07:11, 26 February 2020)
- Improved peak detection in mass spectrum by incorporating continuous wavelet transform-based pattern matching (07:15, 26 February 2020)
- Improved Peak Detection and Deconvolution of Native Electrospray Mass Spectra from Large Protein Complexes (07:26, 26 February 2020)
- Identifying and quantifying metabolites by scoring peaks of GC-MS data (07:36, 26 February 2020)
- Peak alignment using wavelet pattern matching and differential evolution (07:53, 26 February 2020)
- Hybrid optimization method with general switching strategy for parameter estimation (14:08, 26 February 2020)
- Hierarchical optimization for the efficient parametrization of ODE models (14:11, 26 February 2020)
- Accurate proteome-wide label-free quantification by delayed normalization and maximal peptide ratio extraction, termed MaxLFQ (15:32, 27 February 2020)
- Preprocessing of tandem mass spectrometric data to support automatic protein identification (15:36, 27 February 2020)
- Bioinformatics and Statistics: LC‐MS (/MS) Data Preprocessing for Biomarker Discovery (15:47, 27 February 2020)
- Comparison of statistical methods for classification of ovarian cancer using mass spectrometry data (16:41, 28 February 2020)
- Predicting Breast Cancer Survivability Using Data Mining Techniques (16:49, 28 February 2020)
- The impact of sample imbalance on identifying differentially expressed genes (13:42, 4 March 2020)
- Simultaneous Improvement in the Precision, Accuracy and Robustness of Label-free Proteome Quantification by Optimizing Data Manipulation Chains (17:23, 8 November 2020)
- A comparative study of evaluating missing value imputation methods in label-free proteomics (14:32, 2 February 2021)
- The effects of nonignorable missing data on label-free mass spectrometry proteomics experiments (14:33, 2 February 2021)
- NormalyzerDE: Online Tool for Improved Normalization of Omics Expression Data and High-Sensitivity Differential Expression Analysis (14:34, 2 February 2021)
- Benchmarking Quantitative Performance in Label-Free Proteomics (14:48, 2 February 2021)
- Microbiome differential abundance methods produce different results across 38 datasets (14:33, 18 February 2022)
- Benchmarking microbiome transformations favors experimental quantitative approaches to address compositionality and sampling depth biases (14:38, 18 February 2022)
- Evaluating supervised and unsupervised background noise correction in human gut microbiome data (14:44, 18 February 2022)
- A comprehensive benchmarking study of protocols and sequencing platforms for 16S rRNA community profiling (14:47, 18 February 2022)
- Benchmarking Metagenomics Tools for Taxonomic Classification (14:51, 18 February 2022)
- Comprehensive benchmarking and ensemble approaches for metagenomic classifiers (15:14, 18 February 2022)
- Comparative study of classifiers for human microbiome data (15:15, 18 February 2022)
- Benchmark of data processing methods and machine learning models for gut microbiome-based diagnosis of inflammatory bowel disease (15:16, 18 February 2022)
- Mockrobiota: a public resource for microbiome bioinformatics benchmarking (15:50, 18 February 2022)
