Results 211 to 220 of about 1,170,104 (261)
Molecular Oncology, EarlyView.This study identifies somatostatin receptor 4 (Sstr4) as a critical tumor suppressor against skin and head/neck cancers (HNSCC, cSCC, and BCC). The loss of Sstr4 removes a check on cell growth, causing hyperactivation of the MAPK‐ERK signaling pathway (↑).
Ali Taqvi +6 more
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Molecular Oncology, EarlyView.Arginine methylation can be viewed as a persistence‐prone post‐translational modification regulated by a network of PRMTs. Competitive and compensatory interactions among PRMTs can redistribute methylation across substrate pools shaped by sequence, structural, spatial, and environmental layers, reinforcing RNA‐processing, chromatin, and signaling ...
So Hyun Kwon, Ji Min Lee
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FEBS Open Bio, EarlyView.Unique biological samples, such as site‐specific mutant proteins, are available only in limited quantities. Here, we present a polarization‐resolved transient infrared spectroscopy setup with referencing to improve signal‐to‐noise tailored towards tracing small signals. We provide an overview of characterizing the excitation conditions for polarization‐
Clark Zahn, Karsten Heyne
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Single‐molecule DNA flow‐stretch assays for high‐throughput DNA–protein interaction studies
FEBS Open Bio, EarlyView.We describe an optimised single‐molecule DNA flow‐stretch assay that visualises DNA–protein interactions in real time. Linear DNA fragments are tethered to a surface and stretched by buffer flow for fluorescence imaging. Using λ and φX174 DNA, this protocol enhances reproducibility and accessibility, providing a versatile approach for studying diverse ...
Ayush Kumar Ganguli +8 more
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Missing Value Monitoring to Address Missing Values in Quantitative Proteomics
2021Many classes of key functional proteins such as transcription factors or cell cycle proteins are present in the proteome at a very low concentration. These low-abundance proteins are almost entirely invisible to systematic quantitative analysis by classical data dependent proteomics methods (DDA).
Vittoria, Matafora, Angela, Bachi
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Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, 2017
Missing value is common in many machine learning problems and much effort has been made to handle missing data to improve the performance of the learned model. Sometimes, our task is not to train a model using those unlabeled/labeled data with missing value but process examples according to the values of some specified features.
Zhi-Lin Zhao 0001 +3 more
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Missing value is common in many machine learning problems and much effort has been made to handle missing data to improve the performance of the learned model. Sometimes, our task is not to train a model using those unlabeled/labeled data with missing value but process examples according to the values of some specified features.
Zhi-Lin Zhao 0001 +3 more
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2017 21st International Conference Information Visualisation (IV), 2017
Many real world data sets have data items with missing values. Values can be missing for many different reasons, such as sensor failure, respondents forgetting or refusing to answer a question in a survey, or a certain feature not being applicable to certain subsets of data.
Jonas Sjöbergh, Yuzuru Tanaka
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Many real world data sets have data items with missing values. Values can be missing for many different reasons, such as sensor failure, respondents forgetting or refusing to answer a question in a survey, or a certain feature not being applicable to certain subsets of data.
Jonas Sjöbergh, Yuzuru Tanaka
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Role Mining with Missing Values
2016 11th International Conference on Availability, Reliability and Security (ARES), 2016Over the years several organizations are migrating to Role-Based Access Control (RBAC) as a practical solution to regulate access to sensitive information. Role mining has been proposed to automatically extract RBAC policies from the current set of permissions assigned to users.
Sokratis Vavilis +3 more
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Clustering with Missing Values
Fundamenta Informaticae, 2013The paper presents the clustering algorithm for data with missing values. In this approach both marginalisation and imputation are applied. The result of the clustering is the type-2 fuzzy set / rough fuzzy set. This approach enables the distinction between original and imputed data.
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The Mathematical Gazette, 1949
The following theorems are closely connected in various ways; one common thread that runs through them is that in each a critical stage of the proof consists in filling in a missing value in the range of values for which the result is known to hold. Another common feature, of no logical importance but none the less the
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The following theorems are closely connected in various ways; one common thread that runs through them is that in each a critical stage of the proof consists in filling in a missing value in the range of values for which the result is known to hold. Another common feature, of no logical importance but none the less the
openaire +1 more source