scholarly journals KinasePhos 3.0: Redesign and Expansion of the Prediction on Kinase-specific Phosphorylation Sites

2021 ◽  
Author(s):  
Renfei Ma ◽  
Shangfu Li ◽  
Wenshuo Li ◽  
Lantian Yao ◽  
Hsien-Da Huang ◽  
...  
Keyword(s):  
Large Scale ◽  
Phosphorylation Site ◽  
Phosphorylation Sites ◽  
Prediction Tool ◽  
Web Interface ◽  
Feature Importance ◽  
Individual Family ◽  
Executable File ◽  
Classification Information ◽  
The Individual

The purpose of this work is to enhance KinasePhos, a machine-learning-based kinase-specific phosphorylation site prediction tool. Experimentally verified kinase-specific phosphorylation data were collected from PhosphoSitePlus, UniProt, GPS 5.0, and Phospho.ELM. In total, 41,421 experimentally verified kinase-specific phosphorylation sites were identified. A total of 1380 unique kinases were identified, including 753 with existing classification information from KinBase and the remaining 627 annotated by building a phylogenetic tree. Based on this kinase classification, a total of 771 predictive models were built at the individual, family, and group levels, using at least 15 experimentally verified substrate sites in positive training datasets. The improved models were observed to be more effective than other prediction tools. For example, the prediction of sites phosphorylated by the Akt, CKT, and PKA families had accuracies of 94.5%, 92.5%, and 90.0%, respectively. The average prediction accuracy for all 771 models was 87.2%. For enhancing interpretability, the Shapley additive explanations (SHAP) method was employed to assess feature importance. The web interface of KinasePhos 3.0 has been redesigned with the goal of providing comprehensive annotations of kinase-specific phosphorylation sites on multiple proteins. Additionally, considering the large scale of phosphoproteomic data, a downloadable prediction tool is available at https://awi.cuhk.edu.cn/KinasePhos/index.html or https://github.com/tom-209/KinasePhos-3.0-executable-file.

Models, Practices and Methods of Reading: Evolution in Time and Space

2009 ◽  
pp. 59-64
Author(s):  
Yulia P. Melentyeva
Keyword(s):  
Large Scale ◽  
Social Groups ◽  
National Program ◽  
Time And Space ◽  
Lack Of Information ◽  
The Individual

In recent years as public in general and specialist have been showing big interest to the matters of reading. According to discussion and launch of the “Support and Development of Reading National Program”, many Russian libraries are organizing the large-scale events like marathons, lecture cycles, bibliographic trainings etc. which should draw attention of different social groups to reading. The individual forms of attraction to reading are used much rare. To author’s mind the main reason of such an issue has to be the lack of information about forms and methods of attraction to reading.

scholarly journals A deep learning based approach for prediction of Chlamydomonas reinhardtii phosphorylation sites

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Niraj Thapa ◽  
Meenal Chaudhari ◽  
Anthony A. Iannetta ◽  
Clarence White ◽  
Kaushik Roy ◽  
...  
Keyword(s):  
Deep Learning ◽  
Chlamydomonas Reinhardtii ◽  
Protein Phosphorylation ◽  
Short Term Memory ◽  
Phosphorylation Site ◽  
Specific Protein ◽  
Training Dataset ◽  
Phosphorylation Sites ◽  
Site Prediction ◽  
Model Combining

AbstractProtein phosphorylation, which is one of the most important post-translational modifications (PTMs), is involved in regulating myriad cellular processes. Herein, we present a novel deep learning based approach for organism-specific protein phosphorylation site prediction in Chlamydomonas reinhardtii, a model algal phototroph. An ensemble model combining convolutional neural networks and long short-term memory (LSTM) achieves the best performance in predicting phosphorylation sites in C. reinhardtii. Deemed Chlamy-EnPhosSite, the measured best AUC and MCC are 0.90 and 0.64 respectively for a combined dataset of serine (S) and threonine (T) in independent testing higher than those measures for other predictors. When applied to the entire C. reinhardtii proteome (totaling 1,809,304 S and T sites), Chlamy-EnPhosSite yielded 499,411 phosphorylated sites with a cut-off value of 0.5 and 237,949 phosphorylated sites with a cut-off value of 0.7. These predictions were compared to an experimental dataset of phosphosites identified by liquid chromatography-tandem mass spectrometry (LC–MS/MS) in a blinded study and approximately 89.69% of 2,663 C. reinhardtii S and T phosphorylation sites were successfully predicted by Chlamy-EnPhosSite at a probability cut-off of 0.5 and 76.83% of sites were successfully identified at a more stringent 0.7 cut-off. Interestingly, Chlamy-EnPhosSite also successfully predicted experimentally confirmed phosphorylation sites in a protein sequence (e.g., RPS6 S245) which did not appear in the training dataset, highlighting prediction accuracy and the power of leveraging predictions to identify biologically relevant PTM sites. These results demonstrate that our method represents a robust and complementary technique for high-throughput phosphorylation site prediction in C. reinhardtii. It has potential to serve as a useful tool to the community. Chlamy-EnPhosSite will contribute to the understanding of how protein phosphorylation influences various biological processes in this important model microalga.

scholarly journals PhosPhAt: a database of phosphorylation sites in Arabidopsis thaliana and a plant-specific phosphorylation site predictor

2007 ◽  
Vol 36 (Database) ◽  
pp. D1015-D1021 ◽  
Author(s):  
J. L. Heazlewood ◽  
P. Durek ◽  
J. Hummel ◽  
J. Selbig ◽  
W. Weckwerth ◽  
...  
Keyword(s):  
Arabidopsis Thaliana ◽  
Phosphorylation Site ◽  
Phosphorylation Sites

Flexibility Effects in a Single Link Robotic Manipulator

1993 ◽  
Author(s):  
C. Nataraj
Keyword(s):  
Large Scale ◽  
Robotic Manipulator ◽  
Flexible Beam ◽  
Rotating Speed ◽  
Weighted Residuals ◽  
Individual Interaction ◽  
Single Link ◽  
Rigid Mass ◽  
The Individual ◽  
Future Work

Abstract A single link robotic manipulator is modeled as a rotating flexible beam with a rigid mass at the tip and accurate energy expressions are derived. The resulting partial differential equations are solved using an approximate method of weighted residuals. From the solutions, coupling between axial and flexural deformations and the interactions with rigid body motions are rigorously analyzed. The emphasis in the current paper is not on an exhaustive analysis of existing systems but it is rather intended to compare and highlight the various flexibility effects in a relatively simple system. Hence, a nondimensional parametric analysis is performed to determine the effect of several parameters (including the rotating speed) on the errors and the individual interaction effects are discussed. Comparison with previous work in the field shows important phenomena often ignored or buried in large scale numerical analyses. Future work including application to multi-link robots is outlined.

Systemic Issues in the Opioid Epidemic: Supporting the Individual, Family, and Community

2018 ◽  
Vol 17 (5) ◽  
pp. 1214-1228 ◽  
Author(s):  
Kristin A. Vincenzes ◽  
Beth McMahon ◽  
Jennifer Lange ◽  
Kellie Forziat-Pytel
Keyword(s):  
Opioid Epidemic ◽  
Individual Family ◽  
The Individual

scholarly journals Identification of AMPK Phosphorylation Sites Reveals a Network of Proteins Involved in Cell Invasion and Facilitates Large-Scale Substrate Prediction

2015 ◽  
Vol 22 (5) ◽  
pp. 907-921 ◽  
Author(s):  
Bethany E. Schaffer ◽  
Rebecca S. Levin ◽  
Nicholas T. Hertz ◽  
Travis J. Maures ◽  
Michael L. Schoof ◽  
...  
Keyword(s):  
Cell Invasion ◽  
Large Scale ◽  
Phosphorylation Sites ◽  
Ampk Phosphorylation ◽  
Substrate Prediction

scholarly journals Phoenix 2: A locally installable large-scale 16S rRNA gene sequence analysis pipeline with Web interface

2013 ◽  
Vol 167 (4) ◽  
pp. 393-403 ◽  
Author(s):  
Jung Soh ◽  
Xiaoli Dong ◽  
Sean M. Caffrey ◽  
Gerrit Voordouw ◽  
Christoph W. Sensen
Keyword(s):  
Sequence Analysis ◽  
16S Rrna ◽  
16S Rrna Gene ◽  
Large Scale ◽  
Gene Sequence ◽  
Rrna Gene ◽  
Web Interface ◽  
Rrna Gene Sequence ◽  
Analysis Pipeline ◽  
Gene Sequence Analysis

scholarly journals Feature Importance Analysis for Local Climate Zone Classification Using a Residual Convolutional Neural Network with Multi-Source Datasets

2018 ◽  
Vol 10 (10) ◽  
pp. 1572 ◽  
Author(s):  
Chunping Qiu ◽  
Michael Schmitt ◽  
Lichao Mou ◽  
Pedram Ghamisi ◽  
Xiao Zhu
Keyword(s):  
Neural Network ◽  
Land Use ◽  
Convolutional Neural Network ◽  
Large Scale ◽  
Landsat 8 ◽  
Climate Zone ◽  
Local Climate ◽  
Feature Importance ◽  
Local Climate Zone ◽  
Sentinel 2

Global Local Climate Zone (LCZ) maps, indicating urban structures and land use, are crucial for Urban Heat Island (UHI) studies and also as starting points to better understand the spatio-temporal dynamics of cities worldwide. However, reliable LCZ maps are not available on a global scale, hindering scientific progress across a range of disciplines that study the functionality of sustainable cities. As a first step towards large-scale LCZ mapping, this paper tries to provide guidance about data/feature choice. To this end, we evaluate the spectral reflectance and spectral indices of the globally available Sentinel-2 and Landsat-8 imagery, as well as the Global Urban Footprint (GUF) dataset, the OpenStreetMap layers buildings and land use and the Visible Infrared Imager Radiometer Suite (VIIRS)-based Nighttime Light (NTL) data, regarding their relevance for discriminating different Local Climate Zones (LCZs). Using a Residual convolutional neural Network (ResNet), a systematic analysis of feature importance is performed with a manually-labeled dataset containing nine cities located in Europe. Based on the investigation of the data and feature choice, we propose a framework to fully exploit the available datasets. The results show that GUF, OSM and NTL can contribute to the classification accuracy of some LCZs with relatively few samples, and it is suggested that Landsat-8 and Sentinel-2 spectral reflectances should be jointly used, for example in a majority voting manner, as proven by the improvement from the proposed framework, for large-scale LCZ mapping.

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