Analytic framework for peptidomics applied to large-scale neuropeptide identification
نویسندگان
چکیده
منابع مشابه
Analytic framework for peptidomics applied to large-scale neuropeptide identification
Large-scale mass spectrometry-based peptidomics for drug discovery is relatively unexplored because of challenges in peptide degradation and identification following tissue extraction. Here we present a streamlined analytical pipeline for large-scale peptidomics. We developed an optimized sample preparation protocol to achieve fast, reproducible and effective extraction of endogenous peptides f...
متن کاملLarge-Scale Real-Time Object Identification Based on Analytic Features
Inspired by biological findings, we present a system that is able to robustly identify a large number of pre-trained objects in realtime. In contrast to related work, we do not restrict the objects’ pose to characteristic views but rotate them freely in hand in front of a cluttered background. We describe the essential system’s ingredients, like prototype-based figure-ground segmentation, extra...
متن کاملTurning Quantitative: An Analytic Scale to Do Critical Discourse Analysis
Critical Discourse Analysis (CDA) could be seen as a theory in qualitative more than in qualitative stud- ies. This might have led to difficulty in doing CDA. Accordingly, this study attempted to develop a quan- titative profile in the form of an analytic rubric. For this purpose, Fairclough’s model of CDA was select- ed as the research framework. The techniques used for structuring analy...
متن کاملMultilevel framework for large-scale global optimization
Large-scale global optimization (LSGO) algorithms are crucially important to handle real-world problems. Recently, cooperative co-evolution (CC) algorithms have successfully been applied for solving many large-scale practical problems. Many applications have imbalanced subcomponents where the size of subcomponents and their contribution to the objective function value are different. CC algorith...
متن کاملRegularization Framework for Large Scale Hierarchical Classification
In this paper, we propose a hierarchical regularization framework for large-scale hierarchical classification. In our framework, we use the regularization structure to share information across the hierarchy and enforce similarity between class-parameters that are located nearby in the hierarchy. To address the computational issues that arise, we propose a parallel-iterative optimization scheme ...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Nature Communications
سال: 2016
ISSN: 2041-1723
DOI: 10.1038/ncomms11436