NeuroNames 2002

Bowden, Douglas M.; Dubach, Mark

doi:10.1385/ni:1:1:043

Cited by 68 publications

(68 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…For each publication indexed, the reported groups and individuals are characterized with respect to the demographic, clinical and anatomic measurements reported. To facilitate interdatabase federation, many elements of the database are related to 'concept' entries in the Unified Medical Language System (UMLS) [Hole & Srinivasan, 2003] including the anatomic nomenclature (through NeuroNames [Bowden and Martin, 1995;Bowden and Dubach, 2003]] and diagnosis codes (DSM-4 and ICD-10).…”

Section: Methodsmentioning

confidence: 99%

MRI‐based morphometric analysis of typical and atypical brain development

Kennedy

Haselgrove

McInerney

2003

Ment. Retard. Dev. Disabil. Res. Rev.

View full text Add to dashboard Cite

The neuroinformatics landscape in which human brain morphometry occurs has advanced dramatically over the past few years. Rapid advancement in image acquisition methods, image analysis tools and interpretation of morphometric results make the study of in vivo anatomic analysis both challenging and rewarding. This has revolutionized our expectations for current and future diagnostic and investigative work with the developing brain. This paper will briefly cover the methods of morphometric analysis that available for neuroanatomic analysis, and tour some sample results from a prototype retrospective database of neuroanatomic volumetric information. From these observations, issues regarding the anatomic variability of developmental maturation of neuroanatomic structures in both typically and atypically developing populations can be discussed.

show abstract

Section: Methodsmentioning

confidence: 99%

MRI‐based morphometric analysis of typical and atypical brain development

Kennedy

Haselgrove

McInerney

2003

Ment. Retard. Dev. Disabil. Res. Rev.

View full text Add to dashboard Cite

show abstract

“…The UMLS has recently incorporated the NeuroNames ontology 53 as a source vocabulary. NeuroNames is a comprehensive resource for gross brain anatomy in the primate.…”

Section: A Case Study: the Cell Centered Database 45mentioning

confidence: 99%

“…One important use is to facilitate an understanding of what design properties of an intracellular network are necessary for its function. For example, von Dassow et al 53 used a simulation model of the gap and pair-rule gene network in Drosophila melanogaster to show that the structure of the network is sufficient to explain a great deal of the observed cellular patterning. In addition, they showed that the network behavior was robust to parameter variation upon the addition of hypothetical (but reasonable) elements to the known network.…”

Section: On Inferring Qualitative Connectionsmentioning

confidence: 99%

“…For example, very simple models have been used to provide insight into a genetic oscillator and a switch in E. coli. 56 Models have also been used to test designs for the control of cellular networks, as illustrated by 53 Endy and Yin in using their T7 model to propose a pharmaceutical strategy for preventing both T7 propagation and the development of drug resistance through mutation. 57 Given observed cell behavior, simulation models can be used to suggest the necessity of a given regulatory motif or the sufficiency of known interactions to produce the phenomenon.…”

Section: On Inferring Qualitative Connectionsmentioning

confidence: 99%

“…However, because noise arises in the pathway itself, the amount of internally generated noise increases with cascade length-suggesting that there is an optimal cascade length for attenuating noise. 53 It is not surprising that low-pass filters are components of biological systems. As noted above, biological systems operate homeostatically, 54 and the essential principle underlying homeostasis is that of negative feedback.…”

Section: Noise In Biological Phenomenamentioning

confidence: 99%

See 2 more Smart Citations

Catalyzing Inquiry at the Interface of Computing and Biology

Wooley¹,

Lin²

2005

View full text Add to dashboard Cite

Preface viiIn the last decade of the 20th century, computer science and biology both emerged as fields capable of remarkable and rapid change. Moreover, they evolved as fields of inquiry in ways that draw attention to their areas of intersection. The continuing advancements in technology and the pace of scientific research present the means for computing to help answer fundamental questions in the biological sciences and for biology to demonstrate that new approaches to computing are possible.Advances in the power and ease of use of computing and communications systems have fueled computational biology (e.g., genomics) and bioinformatics (e.g., database development and analysis). Modeling and simulation of biological entities such as cells have joined biologists and computer scientists (and mathematicians, physicists, and statisticians too) to work together on activities from pharmaceutical design to environmental analysis.On the other side, computer scientists have pondered the significance of biology for their field. For example, computer scientists have explored the use of DNA as a substrate for new computing hardware and the use of biological approaches in solving hard computing problems. Exploration of biological computation suggests a potential for insight into the nature of and alternative processes for computation, and it also gives rise to questions about hybrid systems that achieve some kind of synergy of biological and computational systems. And there is also the fact that biological systems exhibit characteristics such as adaptability, self-healing, evolution, and learning that would be desirable in the information technologies that humans use.Making the most of the research opportunities at the interface of computing and biology-what we are calling the BioComp interface-requires illuminating what they are and effectively engaging people from both computing and biology. As in other contexts, the challenges of interdisciplinary education and of collaboration are significant, and each will require attention, together with substantive work from both policy makers and researchers. At the start of the 1990s, attempts were made to stimulate mutual interest and collaboration among young researchers in computing and biology. Those early efforts yielded nontrivial successes, but in retrospect represented a Version 1.0 prototype for the potential in bringing the two fields together. Circumstances today seem much more favorable for progress. New research teams and training programs have been formed as individual investigators from the respective communities, government agencies, and private foundations have become increasingly engaged. Similarly, some larger groups of investigators from different backgrounds have been able to viii PREFACE obtain funding to work together to address cross-disciplinary research problems. It is against this background that the committee sees a Version 2.0 of the BioComp interface emerging that will yield unprecedented progress and advance.The range of possible activities at the Bio...

show abstract

Repeat needle biopsies combined with clinical observation are safe and accurate in the management of a solitary pulmonary nodule

Sortini

Maravegias

Feo

et al. 2005

Cancer

View full text Add to dashboard Cite

p53 Alterations and Other Tumor CharacteristicsJ ones et al. recently investigated p53 expression in cases of breast carcinoma obtained from 145 African-American (AA) and 177 white (W) patients. 1 Using immunohistochemistry, they found that breast carcinomas that develop in AA patients were more likely to be p53 positive compared with those in W patients (25% in AA patients vs. 7% in W patients). They also reported the following characteristics among the AA cases: later disease stage, higher tumor grade, and loss of estrogen receptor (ER) expression. We conducted a similar study involving breast carcinoma cases from 100 AA and 100 W patients. 2 We did not find any significant differences with regard to tumor grade or p53 expression between the two cohorts, and it appeared initially that our findings contradicted those in the study by Jones et al. However, on further review, the majority of these differences can be explained.Our cases were matched on age at diagnosis, stage of disease, and ER status. Matching was performed to minimize the confounding effects of demographic and clinicopathologic variables on the study endpoints, including prognosis. Therefore, the proportion of ER-positive cases was similar in both groups (73% in AA patients and 69% in W patients). It is well known that ER-positive breast carcinomas are more likely to be p53-negative, well differentiated tumors. 3 In fact, this inverse association between ER positivity and p53 positivity was noted in both our AA (odds ratio [OR] of 0.38) and W (OR of 0.13) cases. Therefore, grade distribution and p53 positivity (36% in AA patients and 38% in W patients) were similar among our AA and W cases. However, in the study by Jones et al., 1 the cases were not matched on ER status, disease stage, or patient age, and the proportion of ER-negative cases was found to be significantly higher among the AA cases (54% compared with 39% in the W cases), a finding that is consistent with previous reports. 4 Therefore, the findings of Jones et al. 1 of higher tumor grade and p53 expression may be explained in part by the higher prevalence of ER-negative tumors. However, the authors did include a multivariate analysis in which p53 positivity remained significantly more common among AA cases after adjusting for several variables, including ER status. It is not clear whether the contrasting results reported by our group and by Jones et al. are the result of actual differences in the two datasets or of errors that occurred during either matching or multivariate analysis. We hope that the types of studies performed by Jones et al. and

show abstract

NeuroNames 2002

Cited by 68 publications

References 4 publications

MRI‐based morphometric analysis of typical and atypical brain development

MRI‐based morphometric analysis of typical and atypical brain development

Catalyzing Inquiry at the Interface of Computing and Biology

Repeat needle biopsies combined with clinical observation are safe and accurate in the management of a solitary pulmonary nodule

Contact Info

Product

Resources

About