A Feature Extraction Method in Large Scale Prediction of Human Protein-Protein Interactions using Physicochemical Properties into Bi-gram

2020 ◽  
Author(s):  
Charlemagne N'Diffon Kopoin ◽  
N. Takpe Tchimou ◽  
Bernard Kouassi Saha ◽  
Michel Babri
Keyword(s):  
Feature Extraction ◽  
Physicochemical Properties ◽  
Protein Interactions ◽  
Extraction Method ◽  
Large Scale ◽  
Human Protein ◽  
Protein Protein Interactions ◽  
Feature Extraction Method

scholarly journals Research on Crop Leaf Disease Identification Method Based on LM-BP Neural Network

2019 ◽  
Vol 131 ◽  
pp. 01118
Author(s):  
Fan Tongke
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Deep Learning ◽  
Bp Neural Network ◽  
Extraction Method ◽  
Large Scale ◽  
Disease Diagnosis ◽  
Learning Technology ◽  
Feature Extraction Method ◽  
Disease Identification

Aiming at the problem of disease diagnosis of large-scale crops, this paper combines machine vision and deep learning technology to propose an algorithm for constructing disease recognition by LM_BP neural network. The images of multiple crop leaves are collected, and the collected pictures are cut by image cutting technology, and the data are obtained by the color distance feature extraction method. The data are input into the disease recognition model, the feature weights are set, and the model is repeatedly trained to obtain accurate results. In this model, the research on corn disease shows that the model is simple and easy to implement, and the data are highly reliable.

scholarly journals Fault Feature Extraction for Anchor Bolt Loosening of Escalator Based on EWT and Bispectrum Analysis

CONVERTER ◽  
2021 ◽  
pp. 681-695
Author(s):  
Zheng Yan
Keyword(s):  
Feature Extraction ◽  
Public Transportation ◽  
Extraction Method ◽  
Large Scale ◽  
Anchor Bolt ◽  
Feature Extraction Method ◽  
Feature Vectors ◽  
Empirical Wavelet Transform ◽  
Bolt Loosening ◽  
Fault Feature Extraction

Escalator is an essential large-scale public transportation equipment. Once the failure occurs, it will inevitably affect the operation and even cause safety accidents.  As an important part of the structure of escalator, the loosening of the anchor bolt will lead to abnormal operation of escalator.  Aiming at the current difficultyin extracting the fault features of anchor bolt loosening, a fault feature extraction method of escalator anchor loosening is constructed based on empirical wavelet transform (EWT) and bispectrum analysis. First, perform EWT decomposition of the original footing vibration acceleration signal to obtain a series of empirical mode functions(EMFs).Then, for each empirical mode function, the bispectrum was calculated by using bispectrum analysis method, and six texture features of the bispectrum were extracted as fault feature vectors by means of gray-gradient co-occurrence matrix.  Finally, the extracted multi-scale fault feature vectors and bi-directional longshort-term memory (BI-LSTM) were used to classify and identify the four types of fault signals with different degrees of foot loosening, and the fault types of foot loosening were determined. The results show that the feature extraction method based on empirical wavelet decomposition and bispectrum analysis can more effectively identify the loosening level of anchor bolts.

scholarly journals Flexible web-based integration of distributed large-scale human protein interaction maps

2007 ◽  
Vol 4 (1) ◽  
pp. 40-50 ◽  
Author(s):  
Gautam Chaurasia ◽  
Yasir Iqbal ◽  
Christian Hänig ◽  
Hanspeter Herzel ◽  
Erich E. Wanker ◽  
...  
Keyword(s):  
Protein Interaction ◽  
Protein Interactions ◽  
Large Scale ◽  
Human Interaction ◽  
Human Protein ◽  
Protein Interaction Data ◽  
Interaction Data ◽  
Protein Protein Interactions ◽  
Human Interactome ◽  
Protein Protein Interaction

Summary Protein-protein interactions constitute the backbone of many molecular processes. This has motivated the recent construction of several large-scale human protein-protein interaction maps [1-10]. Although these maps clearly offer a wealth of information, their use is challenging: complexity, rapid growth, and fragmentation of interaction data hamper their usability. To overcome these hurdles, we have developed a publicly accessible database termed UniHI (Unified Human Interactome) for integration of human protein-protein interaction data. This database is designed to provide biomedical researchers a common platform for exploring previously disconnected human interaction maps. UniHI offers researchers flexible integrated tools for accessing comprehensive information about the human interactome. Several features included in the UniHI allow users to perform various types of network-oriented and functional analysis. At present, UniHI contains over 160,000 distinct interactions between 17,000 unique proteins from ten major interaction maps derived by both computational and experimental approaches [1-10]. Here we describe the details of the implementation and maintenance of UniHI and discuss the challenges that have to be addressed for a successful integration of interaction data.

scholarly journals Large‐scale mapping of human protein–protein interactions by mass spectrometry

2007 ◽  
Vol 3 (1) ◽  
pp. 89 ◽  
Author(s):  
Rob M Ewing ◽  
Peter Chu ◽  
Fred Elisma ◽  
Hongyan Li ◽  
Paul Taylor ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Protein Interactions ◽  
Large Scale ◽  
Human Protein ◽  
Protein Protein Interactions

Target-Templated de novo Design of Macrocyclic D-/L-Peptides: Inhibitors of the PD-1/PD-L1 Interaction

2020 ◽  
Author(s):  
Salvador Guardiola ◽  
Monica Varese ◽  
Xavier Roig ◽  
Jesús Garcia ◽  
Ernest Giralt
Keyword(s):  
Protein Interactions ◽  
Cyclic Peptides ◽  
General Framework ◽  
Large Scale ◽  
De Novo ◽  
Inhibitory Effect ◽  
Original Text ◽  
Protein Protein Interactions ◽  
Retraction Notice ◽  
Pharmaceutical Properties

<p>NOTE: This preprint has been retracted by consensus from all authors. See the retraction notice in place above; the original text can be found under "Version 1", accessible from the version selector above.</p><p><br></p><p>------------------------------------------------------------------------</p><p><br></p><p>Peptides, together with antibodies, are among the most potent biochemical tools to modulate challenging protein-protein interactions. However, current structure-based methods are largely limited to natural peptides and are not suitable for designing target-specific binders with improved pharmaceutical properties, such as macrocyclic peptides. Here we report a general framework that leverages the computational power of Rosetta for large-scale backbone sampling and energy scoring, followed by side-chain composition, to design heterochiral cyclic peptides that bind to a protein surface of interest. To showcase the applicability of our approach, we identified two peptides (PD-<i>i</i>3 and PD-<i>i</i>6) that target PD-1, a key immune checkpoint, and work as protein ligand decoys. A comprehensive biophysical evaluation confirmed their binding mechanism to PD-1 and their inhibitory effect on the PD-1/PD-L1 interaction. Finally, elucidation of their solution structures by NMR served as validation of our <i>de novo </i>design approach. We anticipate that our results will provide a general framework for designing target-specific drug-like peptides.<i></i></p>

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