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- DMR Calling from BSSEQ: (13:53, 7 August 2018)
- Concepts for Bechmarking Studies: (15:06, 7 August 2018)
- Funding (11:32, 9 August 2018)
- Project Imputation in Proteomics (14:30, 9 August 2018)
- Getting started with MediaWiki (05:41, 10 August 2018)
- Benchmarking Projects (05:46, 10 August 2018)
- Project 20 Benchmark Problems for Modelling Intracellular Processes (08:52, 10 August 2018)
- Benchmarking optimization methods for parameter estimation in large kinetic models (08:20, 18 June 2019)
- Optimization and uncertainty analysis of ODE models using second order adjoint sensitivity analysis (08:47, 25 February 2020)
- Exploration, normalization, and genotype calls of high-density oligonucleotide SNP array data (09:42, 25 February 2020)
- Chemometric methods in data processing of mass spectrometry-based metabolomics: A review (10:00, 25 February 2020)
- Optimization of miRNA-seq data preprocessing (10:05, 25 February 2020)
- TEMPLATE (10:18, 25 February 2020)
- Detecting and overcoming systematic bias in high-throughput screening technologies: a comprehensive review of practical issues and methodological solutions (10:22, 25 February 2020)
- Identification and Correction of Additive and Multiplicative Spatial Biases in Experimental High-Throughput Screening (10:29, 25 February 2020)
- Prevention, diagnosis and treatment of high-throughput sequencing data pathologies (10:39, 25 February 2020)
- Help (10:42, 25 February 2020)
- Normalization regarding non-random missing values in high-throughput mass spectrometry data (10:52, 25 February 2020)
- Guidelines for Summarizing a Literature Study (11:37, 25 February 2020)
- DMRfinder: efficiently identifying differentially methylated regions from MethylC-seq data (11:37, 25 February 2020)
- Defiant: (DMRs: easy, fast, identification and ANnoTation) identifies differentially Methylated regions from iron-deficient rat hippocampus (11:38, 25 February 2020)
- DMRcaller: a versatile R/Bioconductor package for detection and visualization of differentially methylated regions in CpG and non-CpG contexts (11:38, 25 February 2020)
- MethCP: Differentially Methylated Region Detection with Change Point Models (bioRxiv) (11:38, 25 February 2020)
- Which missing value imputation method to use in expression profiles: a comparative study and two selection schemes (11:41, 25 February 2020)
- Missing values in mass spectrometry based metabolomics: an undervalued step in the data processing pipeline (11:45, 25 February 2020)
- Recursive partitioning for missing data imputation in the presence of interaction effects. (11:48, 25 February 2020)
- Review, evaluation, and discussion of the challenges of missing value imputation for mass spectrometry-based label-free global proteomics. (11:49, 25 February 2020)
- Accounting for the Multiple Natures of Missing Values in Label-Free Quantitative Proteomics Data Sets to Compare Imputation Strategies (11:50, 25 February 2020)
- Parameter estimation with bio-inspired meta-heuristic optimization: modeling the dynamics of endocytosis (11:51, 25 February 2020)
- Lessons Learned from Quantitative Dynamical Modeling in Systems Biology (11:52, 25 February 2020)
- Comprehensive benchmarking of Markov chain Monte Carlo methods for dynamical systems (11:53, 25 February 2020)
- Data-driven reverse engineering of signaling pathways using ensembles of dynamic models (11:53, 25 February 2020)
- Parameter estimation in large-scale systems biology models: a parallel and self-adaptive cooperative strategy (11:54, 25 February 2020)
- Scalable Parameter Estimation for Genome-Scale Biochemical Reaction Networks (11:57, 25 February 2020)
- MS‐Analyzer: preprocessing and data mining services for proteomics applications on the Grid (12:07, 25 February 2020)
- Efficient computation of steady states in large-scale ODE models of biochemical reaction networks (12:12, 25 February 2020)
- Parameter estimation in models of biological oscillators: an automated regularised estimation approach (12:13, 25 February 2020)
- An Automated Pipeline for High-Throughput Label-Free Quantitative Proteomics (12:25, 25 February 2020)
- Comparative evaluation of preprocessing freeware on chromatography/mass spectrometry data for signature discovery (12:56, 25 February 2020)
- Comparison of peak‐picking workflows for untargeted liquid chromatography/high‐resolution mass spectrometry metabolomics data analysis (13:02, 25 February 2020)
- Comparing gene set analysis methods on single-nucleotide polymorphism data from Genetic Analysis Workshop 16 (13:03, 25 February 2020)
- Comprehensive evaluation of untargeted metabolomics data processing software in feature detection, quantification and discriminating marker selection (13:06, 25 February 2020)
- Data-driven normalization strategies for high-throughput quantitative RT-PCR (13:29, 25 February 2020)
- Performance of objective functions and optimization procedures for parameter estimation in system biology models (13:38, 25 February 2020)
- Machine learning methods for predictive proteomics (13:46, 25 February 2020)
- Gene set analysis methods: a systematic comparison (13:54, 25 February 2020)
- Software platform for high-throughput glycomics (13:58, 25 February 2020)
- MeltDB: a software platform for the analysis and integration of metabolomics experiment data (14:04, 25 February 2020)
- MetaboAnalyst: a web server for metabolomic data analysis and interpretation (14:12, 25 February 2020)
- Evaluation of preprocessing, mapping and postprocessing algorithms for analyzing whole genome bisulfite sequencing data (14:32, 25 February 2020)
