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Showing below up to 113 results in range #1 to #113.

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  1. DMR Calling from BSSEQ:‏‎ (13:53, 7 August 2018)
  2. Concepts for Bechmarking Studies:‏‎ (15:06, 7 August 2018)
  3. Funding‏‎ (11:32, 9 August 2018)
  4. Project Imputation in Proteomics‏‎ (14:30, 9 August 2018)
  5. Getting started with MediaWiki‏‎ (05:41, 10 August 2018)
  6. Benchmarking Projects‏‎ (05:46, 10 August 2018)
  7. Project 20 Benchmark Problems for Modelling Intracellular Processes‏‎ (08:52, 10 August 2018)
  8. Benchmarking optimization methods for parameter estimation in large kinetic models‏‎ (08:20, 18 June 2019)
  9. Optimization and uncertainty analysis of ODE models using second order adjoint sensitivity analysis‏‎ (08:47, 25 February 2020)
  10. Exploration, normalization, and genotype calls of high-density oligonucleotide SNP array data‏‎ (09:42, 25 February 2020)
  11. Chemometric methods in data processing of mass spectrometry-based metabolomics: A review‏‎ (10:00, 25 February 2020)
  12. Optimization of miRNA-seq data preprocessing‏‎ (10:05, 25 February 2020)
  13. TEMPLATE‏‎ (10:18, 25 February 2020)
  14. Detecting and overcoming systematic bias in high-throughput screening technologies: a comprehensive review of practical issues and methodological solutions‏‎ (10:22, 25 February 2020)
  15. Identification and Correction of Additive and Multiplicative Spatial Biases in Experimental High-Throughput Screening‏‎ (10:29, 25 February 2020)
  16. Prevention, diagnosis and treatment of high-throughput sequencing data pathologies‏‎ (10:39, 25 February 2020)
  17. Help‏‎ (10:42, 25 February 2020)
  18. Normalization regarding non-random missing values in high-throughput mass spectrometry data‏‎ (10:52, 25 February 2020)
  19. Guidelines for Summarizing a Literature Study‏‎ (11:37, 25 February 2020)
  20. DMRfinder: efficiently identifying differentially methylated regions from MethylC-seq data‏‎ (11:37, 25 February 2020)
  21. Defiant: (DMRs: easy, fast, identification and ANnoTation) identifies differentially Methylated regions from iron-deficient rat hippocampus‏‎ (11:38, 25 February 2020)
  22. 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)
  23. MethCP: Differentially Methylated Region Detection with Change Point Models (bioRxiv)‏‎ (11:38, 25 February 2020)
  24. Which missing value imputation method to use in expression profiles: a comparative study and two selection schemes‏‎ (11:41, 25 February 2020)
  25. Missing values in mass spectrometry based metabolomics: an undervalued step in the data processing pipeline‏‎ (11:45, 25 February 2020)
  26. Recursive partitioning for missing data imputation in the presence of interaction effects.‏‎ (11:48, 25 February 2020)
  27. Review, evaluation, and discussion of the challenges of missing value imputation for mass spectrometry-based label-free global proteomics.‏‎ (11:49, 25 February 2020)
  28. Accounting for the Multiple Natures of Missing Values in Label-Free Quantitative Proteomics Data Sets to Compare Imputation Strategies‏‎ (11:50, 25 February 2020)
  29. Parameter estimation with bio-inspired meta-heuristic optimization: modeling the dynamics of endocytosis‏‎ (11:51, 25 February 2020)
  30. Lessons Learned from Quantitative Dynamical Modeling in Systems Biology‏‎ (11:52, 25 February 2020)
  31. Comprehensive benchmarking of Markov chain Monte Carlo methods for dynamical systems‏‎ (11:53, 25 February 2020)
  32. Data-driven reverse engineering of signaling pathways using ensembles of dynamic models‏‎ (11:53, 25 February 2020)
  33. Parameter estimation in large-scale systems biology models: a parallel and self-adaptive cooperative strategy‏‎ (11:54, 25 February 2020)
  34. Scalable Parameter Estimation for Genome-Scale Biochemical Reaction Networks‏‎ (11:57, 25 February 2020)
  35. MS‐Analyzer: preprocessing and data mining services for proteomics applications on the Grid‏‎ (12:07, 25 February 2020)
  36. Efficient computation of steady states in large-scale ODE models of biochemical reaction networks‏‎ (12:12, 25 February 2020)
  37. Parameter estimation in models of biological oscillators: an automated regularised estimation approach‏‎ (12:13, 25 February 2020)
  38. An Automated Pipeline for High-Throughput Label-Free Quantitative Proteomics‏‎ (12:25, 25 February 2020)
  39. Comparative evaluation of preprocessing freeware on chromatography/mass spectrometry data for signature discovery‏‎ (12:56, 25 February 2020)
  40. Comparison of peak‐picking workflows for untargeted liquid chromatography/high‐resolution mass spectrometry metabolomics data analysis‏‎ (13:02, 25 February 2020)
  41. Comparing gene set analysis methods on single-nucleotide polymorphism data from Genetic Analysis Workshop 16‏‎ (13:03, 25 February 2020)
  42. Comprehensive evaluation of untargeted metabolomics data processing software in feature detection, quantification and discriminating marker selection‏‎ (13:06, 25 February 2020)
  43. Data-driven normalization strategies for high-throughput quantitative RT-PCR‏‎ (13:29, 25 February 2020)
  44. Performance of objective functions and optimization procedures for parameter estimation in system biology models‏‎ (13:38, 25 February 2020)
  45. Machine learning methods for predictive proteomics‏‎ (13:46, 25 February 2020)
  46. Gene set analysis methods: a systematic comparison‏‎ (13:54, 25 February 2020)
  47. Software platform for high-throughput glycomics‏‎ (13:58, 25 February 2020)
  48. MeltDB: a software platform for the analysis and integration of metabolomics experiment data‏‎ (14:04, 25 February 2020)
  49. MetaboAnalyst: a web server for metabolomic data analysis and interpretation‏‎ (14:12, 25 February 2020)
  50. Evaluation of preprocessing, mapping and postprocessing algorithms for analyzing whole genome bisulfite sequencing data‏‎ (14:32, 25 February 2020)
  51. Review, evaluation, and discussion of the challenges of missing value imputation for mass spectrometry-based label-free global proteomics‏‎ (14:44, 25 February 2020)
  52. Recursive partitioning for missing data imputation in the presence of interaction effects‏‎ (14:46, 25 February 2020)
  53. Optimization and profile calculation of ODE models using second order adjoint sensitivity analysis‏‎ (14:48, 25 February 2020)
  54. NOREVA: normalization and evaluation of MS-based metabolomics data‏‎ (14:58, 25 February 2020)
  55. Evaluation of Derivative-Free Optimizers for Parameter Estimation in Systems Biology‏‎ (15:01, 25 February 2020)
  56. OpenMS: a flexible open-source software platform for mass spectrometry data analysis‏‎ (15:04, 25 February 2020)
  57. Performance Evaluation and Online Realization of Data-driven Normalization Methods Used in LC/MS based Untargeted Metabolomics Analysis‏‎ (15:09, 25 February 2020)
  58. Benchmark problems for dynamic modeling of intracellular processes‏‎ (15:13, 25 February 2020)
  59. A comparison of methods for quantifying prediction uncertainty in systems biology‏‎ (15:13, 25 February 2020)
  60. Fast derivatives of likelihood functionals for ODE based models using adjoint-state method‏‎ (15:20, 25 February 2020)
  61. Data processing has major impact on the outcome of quantitative label-free LC-MS analysis‏‎ (15:21, 25 February 2020)
  62. Missing value estimation methods for DNA microarrays‏‎ (15:22, 25 February 2020)
  63. Parameter Estimation and Variable Selection for Big Systems of Linear Ordinary Differential Equations: A Matrix-Based Approach‏‎ (15:34, 25 February 2020)
  64. A systematic study of normalization methods for Infinium 450K methylation data using whole-genome bisulfite sequencing data‏‎ (15:36, 25 February 2020)
  65. Robust calibration of hierarchical population models for heterogeneous cell populations‏‎ (15:38, 25 February 2020)
  66. Efficient parameterization of large-scale dynamic models based on relative measurements‏‎ (15:39, 25 February 2020)
  67. Testing structural identifiability by a simple scaling method‏‎ (15:40, 25 February 2020)
  68. Tracking for parameter and state estimation in possibly misspecified partially observed linear Ordinary Differential Equations‏‎ (15:40, 25 February 2020)
  69. A general modular framework for gene set enrichment analysis‏‎ (15:40, 25 February 2020)
  70. Mini-batch optimization enables training of ODE models on large-scale datasets‏‎ (15:50, 25 February 2020)
  71. Toward a gold standard for benchmarking gene set enrichment analysis‏‎ (15:57, 25 February 2020)
  72. Peakbin selection in mass spectrometry data using a consensus approach with estimation of distribution algorithms‏‎ (16:24, 25 February 2020)
  73. Comparison of metaheuristic strategies for peakbin selection in proteomic mass spectrometry data‏‎ (16:29, 25 February 2020)
  74. An improved algorithm for peak detection in mass spectra based on continuous wavelet transform‏‎ (07:11, 26 February 2020)
  75. Improved peak detection in mass spectrum by incorporating continuous wavelet transform-based pattern matching‏‎ (07:15, 26 February 2020)
  76. Improved Peak Detection and Deconvolution of Native Electrospray Mass Spectra from Large Protein Complexes‏‎ (07:26, 26 February 2020)
  77. Identifying and quantifying metabolites by scoring peaks of GC-MS data‏‎ (07:36, 26 February 2020)
  78. Peak alignment using wavelet pattern matching and differential evolution‏‎ (07:53, 26 February 2020)
  79. Hybrid optimization method with general switching strategy for parameter estimation‏‎ (14:08, 26 February 2020)
  80. Hierarchical optimization for the efficient parametrization of ODE models‏‎ (14:11, 26 February 2020)
  81. Accurate proteome-wide label-free quantification by delayed normalization and maximal peptide ratio extraction, termed MaxLFQ‏‎ (15:32, 27 February 2020)
  82. Preprocessing of tandem mass spectrometric data to support automatic protein identification‏‎ (15:36, 27 February 2020)
  83. Bioinformatics and Statistics: LC‐MS (/MS) Data Preprocessing for Biomarker Discovery‏‎ (15:47, 27 February 2020)
  84. Comparison of statistical methods for classification of ovarian cancer using mass spectrometry data‏‎ (16:41, 28 February 2020)
  85. Predicting Breast Cancer Survivability Using Data Mining Techniques‏‎ (16:49, 28 February 2020)
  86. The impact of sample imbalance on identifying differentially expressed genes‏‎ (13:42, 4 March 2020)
  87. Simultaneous Improvement in the Precision, Accuracy and Robustness of Label-free Proteome Quantification by Optimizing Data Manipulation Chains‏‎ (17:23, 8 November 2020)
  88. A comparative study of evaluating missing value imputation methods in label-free proteomics‏‎ (14:32, 2 February 2021)
  89. The effects of nonignorable missing data on label-free mass spectrometry proteomics experiments‏‎ (14:33, 2 February 2021)
  90. NormalyzerDE: Online Tool for Improved Normalization of Omics Expression Data and High-Sensitivity Differential Expression Analysis‏‎ (14:34, 2 February 2021)
  91. Benchmarking Quantitative Performance in Label-Free Proteomics‏‎ (14:48, 2 February 2021)
  92. Microbiome differential abundance methods produce different results across 38 datasets‏‎ (14:33, 18 February 2022)
  93. Benchmarking microbiome transformations favors experimental quantitative approaches to address compositionality and sampling depth biases‏‎ (14:38, 18 February 2022)
  94. Evaluating supervised and unsupervised background noise correction in human gut microbiome data‏‎ (14:44, 18 February 2022)
  95. A comprehensive benchmarking study of protocols and sequencing platforms for 16S rRNA community profiling‏‎ (14:47, 18 February 2022)
  96. Benchmarking Metagenomics Tools for Taxonomic Classification‏‎ (14:51, 18 February 2022)
  97. Comprehensive benchmarking and ensemble approaches for metagenomic classifiers‏‎ (15:14, 18 February 2022)
  98. Comparative study of classifiers for human microbiome data‏‎ (15:15, 18 February 2022)
  99. Benchmark of data processing methods and machine learning models for gut microbiome-based diagnosis of inflammatory bowel disease‏‎ (15:16, 18 February 2022)
  100. Mockrobiota: a public resource for microbiome bioinformatics benchmarking‏‎ (15:50, 18 February 2022)
  101. Denoising the Denoisers: an independent evaluation of microbiome sequence error-correction approaches‏‎ (15:51, 18 February 2022)
  102. Assessment of statistical methods from single cell, bulk RNA-seq, and metagenomics applied to microbiome data‏‎ (15:53, 18 February 2022)
  103. LEMMI: a continuous benchmarking platform for metagenomics classifiers‏‎ (15:55, 18 February 2022)
  104. A benchmark of genetic variant calling pipelines using metagenomic short-read sequencing‏‎ (15:56, 18 February 2022)
  105. Distribution-based comprehensive evaluation of methods for differential expression analysis in metatranscriptomics‏‎ (15:57, 18 February 2022)
  106. Evaluation of the microba community profiler for taxonomic profiling of metagenomic datasets from the human gut microbiome‏‎ (15:59, 18 February 2022)
  107. Benchmarking of 16S rRNA gene databases using known strain sequences‏‎ (16:00, 18 February 2022)
  108. Analysing microbiome intervention design studies: Comparison of alternative multivariate statistical methods‏‎ (16:01, 18 February 2022)
  109. Catalyst: Fast and flexible modeling of reaction networks‏‎ (16:31, 24 October 2023)
  110. Test title‏‎ (07:29, 25 October 2023)
  111. Benchmarking Studies in Computational Biology‏‎ (07:27, 16 January 2024)
  112. Literature Studies‏‎ (11:04, 3 April 2024)
  113. A Comprehensive Survey of Statistical Approaches for Differential Expression Analysis in Single-Cell RNA Sequencing Studies‏‎ (11:06, 3 April 2024)

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