about
Proteomic profiling of cerebrospinal fluid identifies biomarkers for amyotrophic lateral sclerosisAutomatic annotation of protein motif function with Gene Ontology termsKnowledge transfer via classification rules using functional mapping for integrative modeling of gene expression dataOn Predicting lung cancer subtypes using 'omic' data from tumor and tumor-adjacent histologically-normal tissue.Knowledge-based variable selection for learning rules from proteomic data.Bayesian rule learning for biomedical data miningA metaproteomic approach to study human-microbial ecosystems at the mucosal luminal interface.Discovery and verification of amyotrophic lateral sclerosis biomarkers by proteomicsSemi-automated literature mining to identify putative biomarkers of disease from multiple biofluids.Lung cancer serum biomarker discovery using label-free liquid chromatography-tandem mass spectrometry.sfDM: Open-Source Software for Temporal Analysis and Visualization of Brain Tumor Diffusion MR Using Serial Functional Diffusion Mapping.Novel MRI-derived quantitative biomarker for cardiac function applied to classifying ischemic cardiomyopathy within a Bayesian rule learning framework.cMRI-BED: A novel informatics framework for cardiac MRI biomarker extraction and discovery applied to pediatric cardiomyopathy classification.Evaluation of a 4-protein serum biomarker panel-biglycan, annexin-A6, myeloperoxidase, and protein S100-A9 (B-AMP)-for the detection of esophageal adenocarcinoma.Tunable structure priors for Bayesian rule learning for knowledge integrated biomarker discoveryA computational framework for the detection of subcortical brain dysmaturation in neonatal MRI using 3D Convolutional Neural Networks
P50
Q24678722-B4D2CDDC-BDF1-4A55-AB8E-76A5CF817029Q24791478-4E98C60B-854A-4969-BB17-A61F9C722C94Q28646636-B3E55BE8-9B80-4337-9A8A-2EBFF01F07E3Q31053216-7AA281E1-D3EF-48E1-A78A-C544A5965326Q33504371-AC0AA8B6-4E1E-4BAF-B1B3-97C2617702E0Q33524687-FC3C021A-D007-4FDA-9690-FDEAF995DE8CQ34087598-01861DA3-1C15-4423-9141-51520CB8BDC6Q34285662-C740F0D8-C06D-49E6-93CE-DF43416984D6Q34431532-12FDE7CD-BE4B-4960-893B-EE3FBDBF9F67Q35013631-0CB03FE2-50A4-4406-B101-62F486B11576Q35042226-42FEB113-B251-4F48-ACA5-BF1BF0AA7786Q35634542-473301C0-EC85-45F0-9897-26E5BE966E25Q35989549-EBB5A142-56FA-4A0A-9FAC-3110D095A0B6Q40981383-7C3B7571-9C46-441E-A2F4-E8A17657D4A7Q58739967-3A8CED40-7A6F-449C-9A25-A6314786F62AQ88789625-BA3F32FA-B3F5-42DD-9732-CF3896FC176A
P50
description
investigador
@es
researcher
@en
wetenschapper
@nl
name
Vanathi Gopalakrishnan
@en
Vanathi Gopalakrishnan
@nl
type
label
Vanathi Gopalakrishnan
@en
Vanathi Gopalakrishnan
@nl
prefLabel
Vanathi Gopalakrishnan
@en
Vanathi Gopalakrishnan
@nl
P31
P496
0000-0002-7813-4055