Results 51 to 60 of about 515,000 (271)

A statistical framework for the analysis of microarray probe-level data [PDF]

Annals of Applied Statistics 2007, Vol. 1, No. 2, 333-357, 2007
In microarray technology, a number of critical steps are required to convert the raw measurements into the data relied upon by biologists and clinicians. These data manipulations, referred to as preprocessing, influence the quality of the ultimate measurements and studies that rely upon them.
arxiv   +1 more source

Support vector machine classification and validation of cancer tissue samples using microarray expression data

Bioinform., 2000
MOTIVATION DNA microarray experiments generating thousands of gene expression measurements, are being used to gather information from tissue and cell samples regarding gene expression differences that will be useful in diagnosing disease.
T. Furey, N. Cristianini, Nigel P. Duffy, D. Bednarski, M. Schummer, D. Haussler   +5 more
semanticscholar   +1 more source

Surfaceome: a new era in the discovery of immune evasion mechanisms of circulating tumor cells

Molecular Oncology, EarlyView.
In the era of immunotherapies, many patients either do not respond or eventually develop resistance. We propose to pave the way for proteomic analysis of surface‐expressed proteins called surfaceome, of circulating tumor cells. This approach seeks to identify immune evasion mechanisms and discover potential therapeutic targets. Circulating tumor cells (
Doryan Masmoudi, Jérome Vialaret, Christophe Hirtz, Catherine Alix‐Panabières   +3 more
wiley   +1 more source

Nonparametric estimation of genewise variance for microarray data [PDF]

Annals of Statistics 2010, Vol. 38, No. 5, 2723-2750, 2010
Estimation of genewise variance arises from two important applications in microarray data analysis: selecting significantly differentially expressed genes and validation tests for normalization of microarray data. We approach the problem by introducing a two-way nonparametric model, which is an extension of the famous Neyman--Scott model and is ...
arxiv   +1 more source

Knowledge-based analysis of microarray gene expression data by using support vector machines.

Proceedings of the National Academy of Sciences of the United States of America, 2000
We introduce a method of functionally classifying genes by using gene expression data from DNA microarray hybridization experiments. The method is based on the theory of support vector machines (SVMs).
M. S. Brown, W. Grundy, D. Lin, N. Cristianini, C. Sugnet, T. Furey, M. Ares, D. Haussler   +7 more
semanticscholar   +1 more source

Cell‐free and extracellular vesicle microRNAs with clinical utility for solid tumors

Molecular Oncology, EarlyView.
Cell‐free microRNAs (cfmiRs) are small‐RNA circulating molecules detectable in almost all body biofluids. Innovative technologies have improved the application of cfmiRs to oncology, with a focus on clinical needs for different solid tumors, but with emphasis on diagnosis, prognosis, cancer recurrence, as well as treatment monitoring.
Yoshinori Hayashi, Janelle‐Cheri Millen, Romela Irene Ramos, Jennifer A. Linehan, Timothy G. Wilson, Dave S. B. Hoon, Matias A. Bustos   +6 more
wiley   +1 more source

Combined spatially resolved metabolomics and spatial transcriptomics reveal the mechanism of RACK1‐mediated fatty acid synthesis

Molecular Oncology, EarlyView.
The authors analyzed the spatial distributions of gene and metabolite profiles in cervical cancer through spatial transcriptomic and spatially resolved metabolomic techniques. Pivotal genes and metabolites within these cases were then identified and validated.
Lixiu Xu, Jinqiu Li, Junqi Ma, Ayshamgul Hasim   +3 more
wiley   +1 more source

SOD3 improves the tumor response to chemotherapy by stabilizing endothelial HIF-2α

Nature Communications, 2018
Tumour vasculature influences drug delivery. Here, the authors show that SOD3 re-expression enhances doxorubicin delivery and effects through normalization of tumour vasculature via the HIF-2a/VE-cadherin pathway.
Emilia Mira, Lorena Carmona-Rodríguez, Beatriz Pérez-Villamil, Josefina Casas, María Jesús Fernández-Aceñero, Diego Martínez-Rey, Paula Martín-González, Ignacio Heras-Murillo, Mateo Paz-Cabezas, Manuel Tardáguila, Tim D. Oury, Silvia Martín-Puig, Rosa Ana Lacalle, Gemma Fabriás, Eduardo Díaz-Rubio, Santos Mañes   +15 more
doaj   +1 more source

Bibliometric analysis of research on gene expression in spinal cord injury

Frontiers in Molecular Neuroscience, 2022
BackgroundSpinal cord injury (SCI) is a severe disease with motor and sensory function being destroyed, which leads to a poor prognosis and a serious financial burden. It is urgent to figure out the molecular and pathological mechanisms of SCI to develop
Siqiao Wang, Siqiao Wang, Siqiao Wang, Weijin Qian, Weijin Qian, Shaofeng Chen, Shuyuan Xian, Minghao Jin, Yifan Liu, Hao Zhang, Hengwei Qin, Xinkun Zhang, Jiwen Zhu, Xi Yue, Chaofeng Shi, Penghui Yan, Runzhi Huang, Runzhi Huang, Runzhi Huang, Zongqiang Huang   +19 more
doaj   +1 more source

NanoCMSer: a consensus molecular subtype stratification tool for fresh‐frozen and paraffin‐embedded colorectal cancer samples

Molecular Oncology, EarlyView.
Consensus molecular subtypes (CMS1‐4) have been identified to study colorectal cancer heterogeneity and serve as potential biomarkers. In this study, we developed and evaluated NanoCMSer, a NanoString‐based classifier using 55 genes, optimized for FF and FFPE to facilitate the clinical evaluation of CMS subtyping.
Arezo Torang, Simone van de Weerd, Veerle Lammers, Sander van Hooff, Inge van den Berg, Saskia van den Bergh, Miriam Koopman, Jan N. IJzermans, Jeanine M. L. Roodhart, Jan Koster, Jan Paul Medema   +10 more
wiley   +1 more source