scholarly article | Q13442814 |
P8978 | DBLP publication ID | journals/jbcb/GohW16a |
P356 | DOI | 10.1142/S0219720016500293 |
P8608 | Fatcat ID | release_h6qcmjicsza73dakjkxpopcu7e |
P698 | PubMed publication ID | 27640811 |
P50 | author | Limsoon Wong | Q41045443 |
Wilson Goh | Q57001122 | ||
P2860 | cites work | Considerations when using the significance analysis of microarrays (SAM) algorithm | Q24815438 |
Proteogenomic characterization of human colon and rectal cancer | Q28244320 | ||
Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2 | Q29547403 | ||
XDIA: improving on the label-free data-independent analysis | Q33526933 | ||
Contemporary network proteomics and its requirements | Q33565129 | ||
Most Random Gene Expression Signatures Are Significantly Associated with Breast Cancer Outcome | Q34058091 | ||
Finding consistent disease subnetworks across microarray datasets | Q34175074 | ||
Next-generation proteomics: towards an integrative view of proteome dynamics. | Q34315255 | ||
Automated approach for quantitative analysis of complex peptide mixtures from tandem mass spectra | Q34405268 | ||
William Sealy Gosset and William A. Silverman: two "students" of science | Q34448147 | ||
Enhancing the utility of Proteomics Signature Profiling (PSP) with Pathway Derived Subnets (PDSs), performance analysis and specialised ontologies. | Q34549988 | ||
Test set bias affects reproducibility of gene signatures | Q35829697 | ||
Proteomics Signature Profiling (PSP): A Novel Contextualization Approach for Cancer Proteomics | Q36320993 | ||
Quantitative proteomics signature profiling based on network contextualization | Q36370224 | ||
FERAL: network-based classifier with application to breast cancer outcome prediction | Q36614060 | ||
Comparative network-based recovery analysis and proteomic profiling of neurological changes in valproic acid-treated mice. | Q37247740 | ||
Applying mass spectrometry-based proteomics to genetics, genomics and network biology | Q37581410 | ||
How advancement in biological network analysis methods empowers proteomics | Q37975999 | ||
Mass spectrometry-based proteomics and network biology | Q37996108 | ||
Computational proteomics: designing a comprehensive analytical strategy. | Q38122953 | ||
Comparative analysis of statistical methods used for detecting differential expression in label-free mass spectrometry proteomics. | Q38982860 | ||
Rapid mass spectrometric conversion of tissue biopsy samples into permanent quantitative digital proteome maps | Q39404529 | ||
A quantum leap in the reproducibility, precision, and sensitivity of gene expression profile analysis even when sample size is extremely small | Q40746398 | ||
The fickle P value generates irreproducible results | Q41347775 | ||
CORUM: the comprehensive resource of mammalian protein complexes | Q41902591 | ||
Finding consistent disease subnetworks using PFSNet. | Q47825134 | ||
OpenSWATH enables automated, targeted analysis of data-independent acquisition MS data | Q48317444 | ||
Permutation P-values should never be zero: calculating exact P-values when permutations are randomly drawn | Q51610576 | ||
Gene Selection for Cancer Classification using Support Vector Machines | Q56535529 | ||
P433 | issue | 5 | |
P304 | page(s) | 1650029 | |
P577 | publication date | 2016-08-03 | |
P1433 | published in | Journal of Bioinformatics and Computational Biology | Q6294841 |
P1476 | title | Evaluating feature-selection stability in next-generation proteomics | |
P478 | volume | 14 |
Q38846815 | Class-paired Fuzzy SubNETs: A paired variant of the rank-based network analysis family for feature selection based on protein complexes. |
Q36258523 | Fuzzy-FishNET: a highly reproducible protein complex-based approach for feature selection in comparative proteomics |
Q36268613 | GFS: fuzzy preprocessing for effective gene expression analysis. |
Q94672464 | Optimized Mahalanobis-Taguchi System for High-Dimensional Small Sample Data Classification |
Q36329452 | Protein complex-based analysis is resistant to the obfuscating consequences of batch effects --- a case study in clinical proteomics. |
Search more.