Institution
Stanford University
Education•Stanford, California, United States•
About: Stanford University is a education organization based out in Stanford, California, United States. It is known for research contribution in the topics: Population & Transplantation. The organization has 125751 authors who have published 320347 publications receiving 21892059 citations. The organization is also known as: Leland Stanford Junior University & University of Stanford.
Topics: Population, Transplantation, Medicine, Cancer, Gene
Papers published on a yearly basis
Papers
More filters
••
Mayo Clinic1, American College of Rheumatology2, Stanford University3, Johns Hopkins University4, University of Colorado Hospital5, Cleveland Clinic6, University of Calgary7, National Institutes of Health8, University of Kentucky9, University of Illinois at Chicago10, Harvard University11, SUNY Downstate Medical Center12, University of California, San Diego13
TL;DR: Criteria for the classification of giant cell (temporal) arteritis were developed by comparing 214 patients who had this disease with 593 patients with other forms of vasculitis, and 2 other variables were included: scalp tenderness and claudication of the jaw or tongue or on deglutition.
Abstract: Criteria for the classification of giant cell (temporal) arteritis were developed by comparing 214 patients who had this disease with 593 patients with other forms of vasculitis. For the traditional format classification, 5 criteria were selected: age greater than or equal to 50 years at disease onset, new onset of localized headache, temporal artery tenderness or decreased temporal artery pulse, elevated erythrocyte sedimentation rate (Westergren) greater than or equal to 50 mm/hour, and biopsy sample including an artery, showing necrotizing arteritis, characterized by a predominance of mononuclear cell infiltrates or a granulomatous process with multinucleated giant cells. The presence of 3 or more of these 5 criteria was associated with a sensitivity of 93.5% and a specificity of 91.2%. A classification tree was also constructed using 6 criteria. These criteria were the same as for the traditional format, except that elevated erythrocyte sedimentation rate was excluded, and 2 other variables were included: scalp tenderness and claudication of the jaw or tongue or on deglutition. The classification tree was associated with a sensitivity of 95.3% and specificity of 90.7%.
2,204 citations
••
[...]
TL;DR: This paper will discuss how geometry and topology can be applied to make useful contributions to the analysis of various kinds of data, particularly high throughput data from microarray or other sources.
Abstract: An important feature of modern science and engineering is that data of various kinds is being produced at an unprecedented rate This is so in part because of new experimental methods, and in part because of the increase in the availability of high powered computing technology It is also clear that the nature of the data we are obtaining is significantly different For example, it is now often the case that we are given data in the form of very long vectors, where all but a few of the coordinates turn out to be irrelevant to the questions of interest, and further that we don’t necessarily know which coordinates are the interesting ones A related fact is that the data is often very high-dimensional, which severely restricts our ability to visualize it The data obtained is also often much noisier than in the past and has more missing information (missing data) This is particularly so in the case of biological data, particularly high throughput data from microarray or other sources Our ability to analyze this data, both in terms of quantity and the nature of the data, is clearly not keeping pace with the data being produced In this paper, we will discuss how geometry and topology can be applied to make useful contributions to the analysis of various kinds of data Geometry and topology are very natural tools to apply in this direction, since geometry can be regarded as the study of distance functions, and what one often works with are distance functions on large finite sets of data The mathematical formalism which has been developed for incorporating geometric and topological techniques deals with point clouds, ie finite sets of points equipped with a distance function It then adapts tools from the various branches of geometry to the study of point clouds The point clouds are intended to be thought of as finite samples taken from a geometric object, perhaps with noise Here are some of the key points which come up when applying these geometric methods to data analysis • Qualitative information is needed: One important goal of data analysis is to allow the user to obtain knowledge about the data, ie to understand how it is organized on a large scale For example, if we imagine that we are looking at a data set constructed somehow from diabetes patients, it would be important to develop the understanding that there are two types of the disease, namely the juvenile and adult onset forms Once that is established, one of course wants to develop quantitative methods for distinguishing them, but the first insight about the distinct forms of the disease is key
2,203 citations
••
The Heart Research Institute1, University of Erlangen-Nuremberg2, Saarland University3, Barts Health NHS Trust4, John Hunter Hospital5, Université catholique de Louvain6, University of Kiel7, University of Cologne8, Leipzig University9, Medical University of Vienna10, Complutense University of Madrid11, St. Vincent's Health System12, University of Duisburg-Essen13, Canterbury Hospital14, University of Zurich15, University of Glasgow16, Auckland City Hospital17, University of Freiburg18, Jagiellonian University19, Stanford University20, Harvard University21
TL;DR: Catheter-based renal denervation can safely be used to substantially reduce blood pressure in treatment-resistant hypertensive patients and should be continued, according to the authors.
2,200 citations
••
TL;DR: A modified and improved SCARE checklist is presented, after a Delphi consensus exercise was completed to update the SCARE guidelines.
2,195 citations
••
TL;DR: Using cDNA microarrays to explore the variation in expression of approximately 8,000 unique genes among the 60 cell lines used in the National Cancer Institute's screen for anti-cancer drugs provided a novel molecular characterization of this important group of human cell lines and their relationships to tumours in vivo.
Abstract: We used cDNA microarrays to explore the variation in expression of approximately 8,000 unique genes among the 60 cell lines used in the National Cancer Institute's screen for anti-cancer drugs. Classification of the cell lines based solely on the observed patterns of gene expression revealed a correspondence to the ostensible origins of the tumours from which the cell lines were derived. The consistent relationship between the gene expression patterns and the tissue of origin allowed us to recognize outliers whose previous classification appeared incorrect. Specific features of the gene expression patterns appeared to be related to physiological properties of the cell lines, such as their doubling time in culture, drug metabolism or the interferon response. Comparison of gene expression patterns in the cell lines to those observed in normal breast tissue or in breast tumour specimens revealed features of the expression patterns in the tumours that had recognizable counterparts in specific cell lines, reflecting the tumour, stromal and inflammatory components of the tumour tissue. These results provided a novel molecular characterization of this important group of human cell lines and their relationships to tumours in vivo.
2,192 citations
Authors
Showing all 127468 results
Name | H-index | Papers | Citations |
---|---|---|---|
Eric S. Lander | 301 | 826 | 525976 |
George M. Whitesides | 240 | 1739 | 269833 |
Yi Cui | 220 | 1015 | 199725 |
Yi Chen | 217 | 4342 | 293080 |
David Miller | 203 | 2573 | 204840 |
David Baltimore | 203 | 876 | 162955 |
Edward Witten | 202 | 602 | 204199 |
Irving L. Weissman | 201 | 1141 | 172504 |
Hongjie Dai | 197 | 570 | 182579 |
Robert M. Califf | 196 | 1561 | 167961 |
Frank E. Speizer | 193 | 636 | 135891 |
Thomas C. Südhof | 191 | 653 | 118007 |
Gad Getz | 189 | 520 | 247560 |
Mark Hallett | 186 | 1170 | 123741 |
John P. A. Ioannidis | 185 | 1311 | 193612 |