scholarly journals proABC-2: PRediction of AntiBody contacts v2 and its application to information-driven docking

2020 ◽  
Vol 36 (20) ◽  
pp. 5107-5108
Author(s):  
Francesco Ambrosetti ◽  
Tobias Hegelund Olsen ◽  
Pier Paolo Olimpieri ◽  
Brian Jiménez-García ◽  
Edoardo Milanetti ◽  
...  
Keyword(s):  
Neural Network ◽  
Monoclonal Antibodies ◽  
Random Forest ◽  
Convolutional Neural Network ◽  
Rational Design ◽  
Supplementary Information ◽  
Supplementary Data ◽  
Computational Approaches ◽  
Fundamental Step ◽  
Neural Network Algorithm

Abstract Motivation Monoclonal antibodies are essential tools in the contemporary therapeutic armory. Understanding how these recognize their antigen is a fundamental step in their rational design and engineering. The rising amount of publicly available data is catalyzing the development of computational approaches able to offer valuable, faster and cheaper alternatives to classical experimental methodologies used for the study of antibody–antigen complexes. Results Here, we present proABC-2, an update of the original random-forest antibody paratope predictor, based on a convolutional neural network algorithm. We also demonstrate how the predictions can be fruitfully used to drive the docking in HADDOCK. Availability and implementation The proABC-2 server is freely available at: https://wenmr.science.uu.nl/proabc2/. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals proABC-2: PRediction Of AntiBody Contacts v2 and its application to information-driven docking

2020 ◽  
Author(s):  
F. Ambrosetti ◽  
T. H. Olsen ◽  
P. P. Olimpieri ◽  
B. Jiménez-García ◽  
E. Milanetti ◽  
...  
Keyword(s):  
Neural Network ◽  
Monoclonal Antibodies ◽  
Random Forest ◽  
Convolutional Neural Network ◽  
Rational Design ◽  
Computational Approaches ◽  
Fundamental Step ◽  
Link Type ◽  
Network Algorithm ◽  
Neural Network Algorithm

AbstractMonoclonal antibodies (mAbs) are essential tools in the contemporary therapeutic armoury. Understanding how these recognize their antigen is a fundamental step in their rational design and engineering. The rising amount of publicly available data is catalysing the development of computational approaches able to offer valuable, faster and cheaper alternatives to classical experimental methodologies used for the study of antibody-antigen complexes.Here we present proABC-2, an update of the original random-forest antibody paratope predictor, based on a convolutional neural network algorithm. We also demonstrate how the predictions can be fruitfully used to drive the docking in HADDOCK.The proABC-2 server is freely available at: https://bianca.science.uu.nl/proabc2/.

Estimating ground-level PM2.5 using micro-satellite images by a convolutional neural network and random forest approach

2020 ◽  
Vol 230 ◽  
pp. 117451 ◽  
Author(s):  
Tongshu Zheng ◽  
Michael H. Bergin ◽  
Shijia Hu ◽  
Joshua Miller ◽  
David E. Carlson
Keyword(s):  
Neural Network ◽  
Random Forest ◽  
Convolutional Neural Network ◽  
Satellite Images ◽  
Ground Level

Classification of Batik Authenticity Using Convolutional Neural Network Algorithm with Transfer Learning Method

2021 ◽  
Author(s):  
Farrel Athaillah Putra ◽  
Dwi Anggun Cahyati Jamil ◽  
Briliantino Abhista Prabandanu ◽  
Suhaili Faruq ◽  
Firsta Adi Pradana ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Transfer Learning ◽  
Learning Method ◽  
Network Algorithm ◽  
Neural Network Algorithm

scholarly journals An Improved Convolutional Neural Network Algorithm and Its Application in Multilabel Image Labeling

2019 ◽  
Vol 2019 ◽  
pp. 1-12 ◽  
Author(s):  
Jianfang Cao ◽  
Chenyan Wu ◽  
Lichao Chen ◽  
Hongyan Cui ◽  
Guoqing Feng
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Image Annotation ◽  
Low Frequency ◽  
Dual Channel ◽  
Extended Training ◽  
Pascal Voc ◽  
Proposed Model ◽  
Neural Network Algorithm ◽  
Similar Accuracy

In today’s society, image resources are everywhere, and the number of available images can be overwhelming. Determining how to rapidly and effectively query, retrieve, and organize image information has become a popular research topic, and automatic image annotation is the key to text-based image retrieval. If the semantic images with annotations are not balanced among the training samples, the low-frequency labeling accuracy can be poor. In this study, a dual-channel convolution neural network (DCCNN) was designed to improve the accuracy of automatic labeling. The model integrates two convolutional neural network (CNN) channels with different structures. One channel is used for training based on the low-frequency samples and increases the proportion of low-frequency samples in the model, and the other is used for training based on all training sets. In the labeling process, the outputs of the two channels are fused to obtain a labeling decision. We verified the proposed model on the Caltech-256, Pascal VOC 2007, and Pascal VOC 2012 standard datasets. On the Pascal VOC 2012 dataset, the proposed DCCNN model achieves an overall labeling accuracy of up to 93.4% after 100 training iterations: 8.9% higher than the CNN and 15% higher than the traditional method. A similar accuracy can be achieved by the CNN only after 2,500 training iterations. On the 50,000-image dataset from Caltech-256 and Pascal VOC 2012, the performance of the DCCNN is relatively stable; it achieves an average labeling accuracy above 93%. In contrast, the CNN reaches an accuracy of only 91% even after extended training. Furthermore, the proposed DCCNN achieves a labeling accuracy for low-frequency words approximately 10% higher than that of the CNN, which further verifies the reliability of the proposed model in this study.

scholarly journals Deep Learning-Based Classification of Weld Surface Defects

2019 ◽  
Vol 9 (16) ◽  
pp. 3312 ◽  
Author(s):  
Zhu ◽  
Ge ◽  
Liu
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Random Forest ◽  
Convolutional Neural Network ◽  
Surface Defects ◽  
Recognition Method ◽  
High Level ◽  
Intelligent Recognition ◽  
Non Destructive

In order to realize the non-destructive intelligent identification of weld surface defects, an intelligent recognition method based on deep learning is proposed, which is mainly formed by convolutional neural network (CNN) and forest random. First, the high-level features are automatically learned through the CNN. Random forest is trained with extracted high-level features to predict the classification results. Secondly, the weld surface defects images are collected and preprocessed by image enhancement and threshold segmentation. A database of weld surface defects is established using pre-processed images. Finally, comparative experiments are performed on the weld surface defects database. The results show that the accuracy of the method combined with CNN and random forest can reach 0.9875, and it also demonstrates the method is effective and practical.

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