How is mRNA expression predictive for protein expression? A correlation study on human circulating monocytes.
about
The predictive power of brain mRNA mappings for in vivo protein density: a positron emission tomography correlation studyEvaluating genetic markers and neurobiochemical analytes for fluoxetine response using a panel of mouse inbred strainsEvaluating biomarkers to model cancer risk post cosmic ray exposureProximity assays for sensitive quantification of proteinsOpen tubular lab-on-column/mass spectrometry for targeted proteomics of nanogram sample amountsImpacts of different exposure scenarios on transcript abundances in Danio rerio embryos when investigating the toxicological burden of riverine sedimentsSelecting against accidental RNA interactionsValidation of Reference Genes for RT-qPCR Analysis in Noise-Induced Hearing Loss: A Study in Wistar RatA vascular biology network model focused on inflammatory processes to investigate atherogenesis and plaque instability.Chondrocyte channel transcriptomics: do microarray data fit with expression and functional data?Reverse causal reasoning: applying qualitative causal knowledge to the interpretation of high-throughput data.Genome-wide differential gene expression in immortalized DF-1 chicken embryo fibroblast cell lineMENGA: A New Comprehensive Tool for the Integration of Neuroimaging Data and the Allen Human Brain Transcriptome Atlas.Sex differences in mitochondrial biogenesis determine neuronal death and survival in response to oxygen glucose deprivation and reoxygenation.Effects of Arginine concentration on the in vitro expression of Casein and mTOR pathway related genes in mammary epithelial cells from dairy cattleThe predictive nature of transcript expression levels on protein expression in adult human brain.Ex vivo TCR-induced leukocyte gene expression of inflammatory mediators is increased in type 1 diabetic patients but not in overweight childrenCloning and olfactory expression of progestin receptors in the Chinese black sleeper Bostrichthys sinensis.VDR mRNA overexpression is associated with worse prognostic factors in papillary thyroid carcinomaE2F1 and KIAA0191 expression predicts breast cancer patient survivalGlobal signatures of protein and mRNA expression levels.Serotonin receptors in rat jugular vein: presence and involvement in the contraction.Abandon the mouse research ship? Not just yet!Assessment of network perturbation amplitudes by applying high-throughput data to causal biological networks.Three-dimensional culture and transforming growth factor beta3 synergistically promote tenogenic differentiation of equine embryo-derived stem cellsA transcriptional dynamic network during Arabidopsis thaliana pollen development.Cannabinoid receptor type 2, but not type 1, is up-regulated in peripheral blood mononuclear cells of children affected by autistic disorders.Blunted IL17/IL22 and pro-inflammatory cytokine responses in the genital tract and blood of HIV-exposed, seronegative female sex workers in KenyaRegulation of the CCL2 gene in pancreatic β-cells by IL-1β and glucocorticoids: role of MKP-1.Quantification of biological network perturbations for mechanistic insight and diagnostics using two-layer causal models.Preliminary investigation into a potential role for myostatin and its receptor (ActRIIB) in lean and obese horses and ponies.Characterization of the Toll-like receptor expression profile in human multiple myeloma cells.Differential proteome analysis of chikungunya virus infection on host cells.Acclimation to different depths by the marine angiosperm Posidonia oceanica: transcriptomic and proteomic profiles.Low levels of aflatoxin B1, ricin, and milk enhance recombinant protein production in mammalian cellsExtension of life span by impaired glucose metabolism in Caenorhabditis elegans is accompanied by structural rearrangements of the transcriptomic network.The current state of drug discovery and a potential role for NMR metabolomics.Identification of core T cell network based on immunome interactome.Revisiting the identification of canonical splice isoforms through integration of functional genomics and proteomics evidence.Macrophage migration inhibitory factor promoter polymorphisms (-794 CATT 5-8 and -173 G>C): relationship with mRNA expression and soluble MIF levels in young obese subjects.
P2860
Q23992160-72F23D40-9E7E-4A34-B10A-F65F3AF2AB3AQ24632901-92BAF5FF-346A-4036-A136-552AAB049ABDQ26747120-AB2D65AF-AF4F-42E7-804D-75C324FDBED3Q26748501-D168642F-E8FE-4B4F-833B-8897151FBDADQ27312175-2AE3D105-43A1-43F9-95A4-D0B5943DCFA2Q28542773-E31FB62C-E147-40AA-A23F-4F4990344297Q28595575-773B9645-1118-4201-981C-550F68FB735CQ30402745-38F7DEFB-195C-4D91-9BE0-C966D98A4D2BQ30597305-1E775CEE-FA2D-4227-A463-EFE1950B61A0Q30664498-3816401A-937F-4457-B70B-F0DE1CF10267Q30699718-2A52CEF9-5085-4868-A242-EED717BF44C5Q31042174-034BA9B0-E8B0-4EDF-BBEE-05663B21CFB3Q31046346-A2B42930-36BA-4EDD-8ED7-FFBBDB6891CEQ31148061-6F9E78F4-1A46-4663-A524-ED98B507075AQ33553334-143787F1-4EC5-4914-ADAD-78748C701664Q33595632-2EC58E10-45CB-4AE5-8088-B1AAC7DD2199Q33624786-519060BB-85F4-458A-8790-0D793822880AQ33647247-F22CCE5C-96E5-4E1A-A5CC-CB17DB9E337AQ33656421-524ABCD1-52EF-46FE-BA44-2626F171A9B4Q33858905-C1A0B739-3CB0-48F9-A307-38A87B785F1FQ33874396-03AB959B-A417-40FF-B28C-9F23BFEB474EQ34028130-7C75417F-2361-4357-92E6-DBE8EFC2C290Q34069683-FD4C75A6-95A0-4CA5-AFEC-01DFD14D8210Q34288079-BC3D04BD-9EBF-4B27-9832-F9C174A5B203Q34331010-505E34D5-04E2-4755-8395-09E599E57F45Q34333587-1E184FED-E095-4F61-A48E-EC563F5C0C76Q34339177-B241C944-6E6D-4636-BA06-33465143BFA6Q34395906-7FD9642D-5D33-480B-A06A-BAF886A0722AQ34442623-0CDEAFB9-4C26-4445-9D49-E7990F840587Q34486361-CA70CF9C-5032-4505-9048-BD016E25E1B7Q34497809-BCF4213E-CFF0-41C7-878A-36CD42EC96DAQ34673706-43C793BD-3C61-40E3-B41E-BBC2E09E21E3Q34674979-0842E7AC-B688-418E-99CB-7170B0F291F8Q34778563-1AE39857-FB7B-4DB9-A2A4-464C8B114FCFQ34938015-F6C20199-9B90-4716-8B7F-D031A054624AQ35035607-9D5EF5CD-AAB9-4106-9E16-ACD275206B24Q35072850-5157BF71-E3DF-44F9-977E-3C285E0EE829Q35094597-FAB7E678-32E1-49E5-9B75-C9578495FE4EQ35213338-AA272AD5-C91D-4133-B9E4-C326553249B7Q35564125-204D7974-BD51-4F80-BB07-CCDB278419B0
P2860
How is mRNA expression predictive for protein expression? A correlation study on human circulating monocytes.
description
2008 nî lūn-bûn
@nan
2008年の論文
@ja
2008年論文
@yue
2008年論文
@zh-hant
2008年論文
@zh-hk
2008年論文
@zh-mo
2008年論文
@zh-tw
2008年论文
@wuu
2008年论文
@zh
2008年论文
@zh-cn
name
How is mRNA expression predict ...... n human circulating monocytes.
@en
type
label
How is mRNA expression predict ...... n human circulating monocytes.
@en
prefLabel
How is mRNA expression predict ...... n human circulating monocytes.
@en
P2093
P1476
How is mRNA expression predict ...... n human circulating monocytes.
@en
P2093
Feiyan Deng
Gary Guishan Xiao
Hongwen Deng
Jingyun Xie
Shufeng Lei
P304
P356
10.1111/J.1745-7270.2008.00418.X
P577
2008-05-01T00:00:00Z