A surface intrinsic feature based method (SIFBM) for the characterization of optical microstructure

2008 ◽  
Author(s):  
C. F. Cheung ◽  
L. B. Kong ◽  
W. B. Lee ◽  
S. To
Keyword(s):  
Optical Microstructure ◽  
Feature Based ◽  
Intrinsic Feature

scholarly journals Characterization of saliva microbiota’s functional feature based on metagenomic sequencing

SpringerPlus ◽  
2016 ◽  
Vol 5 (1) ◽  
Author(s):  
Fang Yang ◽  
Kang Ning ◽  
Xiaowei Zeng ◽  
Qian Zhou ◽  
Xiaoquan Su ◽  
...  
Keyword(s):  
Metagenomic Sequencing ◽  
Functional Feature ◽  
Feature Based

scholarly journals neoDL: A novel neoantigen intrinsic feature-based deep learning model identifies IDH wild-type glioblastomas with the longest survival

2020 ◽  
Author(s):  
Ting Sun ◽  
Yufei He ◽  
Wendong Li ◽  
Guang Liu ◽  
Lin Li ◽  
...  
Keyword(s):  
Deep Learning ◽  
Asian Population ◽  
Learning Model ◽  
Good Prognosis ◽  
Wild Type ◽  
Term Survival ◽  
Feature Based ◽  
Leave One Out ◽  
Deep Learning Model ◽  
Intrinsic Feature

AbstractBackgroundIDH wild-type glioblastoma (GBM) is the most aggressive tumor in the central nervous system in spite of extensive therapies. Neoantigen based personalized immune therapies achieve promising results in melanoma and lung cancer, but few neoantigen based models perform well in IDH wild-type GBM. Unlike the neoantigen load and occurrence that are well studied and often found useless, the association between neoantigen intrinsic features and prognosis remain unclear in IDH wild-type GBM.ResultsWe presented a novel neoantigen intrinsic feature-based deep learning model (neoDL) to stratify IDH wild-type GBMs into subgroups with different survivals. We first calculated a total of 2928 intrinsic features for each neoantigen and filtered out those not associated with survival, followed by applying neoDL in the TCGA data cohort. Leave one out cross validation (LOOCV) in the TCGA demonstrated that neoDL successfully classified IDH wild-type GBMs into different prognostic subgroups, which was further validated in an independent data cohorts from Asian population. Long-term survival IDH wild-type GBMs identified by neoDL were found characterized by 12 protective neoantigen intrinsic features and enriched in development and cell cycle.ConclusionsOur results provide a novel model, neoDL, that can be therapeutically exploited to identify IDH wild-type GBM with good prognosis who will most likely benefit from neoantigen based personalized immunetherapy.

Feature based characterization of worn surfaces for a sliding test

2010 ◽  
Vol 43 (5-6) ◽  
pp. 1186-1192 ◽  
Author(s):  
Q. Hao ◽  
D. Bianchi ◽  
M. Kaestner ◽  
E. Reithmeier
Keyword(s):  
Feature Based ◽  
Sliding Test ◽  
Worn Surfaces

A Framework for Characterizing Terminological Systems

2006 ◽  
Vol 45 (03) ◽  
pp. 253-266 ◽  
Author(s):  
N. F. de Keizer ◽  
A. Abu-Hanna ◽  
R. Cornet
Keyword(s):  
Clinical Setting ◽  
Snomed Ct ◽  
Uniform Approach ◽  
Feature Based ◽  
Mutual Comparison ◽  
Respective Type ◽  
Content Coverage ◽  
Independent Features

Summary Objectives: The notion of a terminological system (TS) is complex due to the broad range of systems, applications, and clinical domains. A uniform approach to describe the characteristics of TSs is lacking. This impedes furthering understanding, applicability, mutual comparison and development of TSs. For these reasons we propose a terminological systems characterization framework. Methods: Relevant issues pertaining to TSs and terminology servers have been extracted from literature describing requirements and functionality of TSs. From these issues, features have been distilled and further refined. A categorization has been developed to provide a convenient arrangement of these features. Results: The framework distinguishes between application-dependent and application-independent features of TSs. Definitions are provided for measures of content coverage, which was identified as the only application-dependent feature. Application-independent features are categorized along two axes: their respective type of TS and the particular element within that system, i.e. the formalism, the content, or the functionality. For each feature we provide an explicit question, the answer to which yields a feature value. The framework has been applied to SNOMED CT and the CLUE browser. Conclusions: We present and apply a framework to support a feature-based characterization of terminological systems. Standardized methods for content coverage studies reduce the effort of assessing the applicability of a TS for a specific clinical setting. A two-axial categorization provides a convenient arrangement of the large number of application-independent features. Application of the framework increases comparability of terminological systems. This framework may also help TS developers determine how their system can be improved.

scholarly journals neoDL: a novel neoantigen intrinsic feature-based deep learning model identifies IDH wild-type glioblastomas with the longest survival

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Ting Sun ◽  
Yufei He ◽  
Wendong Li ◽  
Guang Liu ◽  
Lin Li ◽  
...  
Keyword(s):  
Deep Learning ◽  
Asian Population ◽  
Learning Model ◽  
Good Prognosis ◽  
Wild Type ◽  
Term Survival ◽  
Feature Based ◽  
Leave One Out ◽  
Deep Learning Model ◽  
Intrinsic Feature

Abstract Background Neoantigen based personalized immune therapies achieve promising results in melanoma and lung cancer, but few neoantigen based models perform well in IDH wild-type GBM, and the association between neoantigen intrinsic features and prognosis remain unclear in IDH wild-type GBM. We presented a novel neoantigen intrinsic feature-based deep learning model (neoDL) to stratify IDH wild-type GBMs into subgroups with different survivals. Results We first derived intrinsic features for each neoantigen associated with survival, followed by applying neoDL in TCGA data cohort(AUC = 0.988, p value < 0.0001). Leave one out cross validation (LOOCV) in TCGA demonstrated that neoDL successfully classified IDH wild-type GBMs into different prognostic subgroups, which was further validated in an independent data cohort from Asian population. Long-term survival IDH wild-type GBMs identified by neoDL were found characterized by 12 protective neoantigen intrinsic features and enriched in development and cell cycle. Conclusions The model can be therapeutically exploited to identify IDH wild-type GBM with good prognosis who will most likely benefit from neoantigen based personalized immunetherapy. Furthermore, the prognostic intrinsic features of the neoantigens inferred from this study can be used for identifying neoantigens with high potentials of immunogenicity.

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