Deep-learning method for data association in particle tracking

2020 ◽  
Vol 36 (19) ◽  
pp. 4935-4941 ◽  
Author(s):  
Yao Yao ◽  
Ihor Smal ◽  
Ilya Grigoriev ◽  
Anna Akhmanova ◽  
Erik Meijering
Keyword(s):  
Deep Learning ◽  
Particle Tracking ◽  
Short Term Memory ◽  
Data Association ◽  
Time Lapse ◽  
Supplementary Information ◽  
Great Promise ◽  
Learning Method ◽  
Biological Studies ◽  
Comprehensive Evaluations

Abstract Motivation Biological studies of dynamic processes in living cells often require accurate particle tracking as a first step toward quantitative analysis. Although many particle tracking methods have been developed for this purpose, they are typically based on prior assumptions about the particle dynamics, and/or they involve careful tuning of various algorithm parameters by the user for each application. This may make existing methods difficult to apply by non-expert users and to a broader range of tracking problems. Recent advances in deep-learning techniques hold great promise in eliminating these disadvantages, as they can learn how to optimally track particles from example data. Results Here, we present a deep-learning-based method for the data association stage of particle tracking. The proposed method uses convolutional neural networks and long short-term memory networks to extract relevant dynamics features and predict the motion of a particle and the cost of linking detected particles from one time point to the next. Comprehensive evaluations on datasets from the particle tracking challenge demonstrate the competitiveness of the proposed deep-learning method compared to the state of the art. Additional tests on real-time-lapse fluorescence microscopy images of various types of intracellular particles show the method performs comparably with human experts. Availability and implementation The software code implementing the proposed method as well as a description of how to obtain the test data used in the presented experiments will be available for non-commercial purposes from https://github.com/yoyohoho0221/pt_linking. Supplementary information Supplementary data are available at Bioinformatics online.

Precise estimation of residue relative solvent accessible area from Cα atom distance matrix using a deep learning method

2021 ◽  
Author(s):  
Jianzhao Gao ◽  
Shuangjia Zheng ◽  
Mengting Yao ◽  
Peikun Wu
Keyword(s):  
Deep Learning ◽  
Protein Structure ◽  
Protein Function ◽  
Pearson Correlation ◽  
Correlation Coefficients ◽  
Distance Matrix ◽  
Supplementary Information ◽  
Learning Method ◽  
Solvent Accessible Area ◽  
Accessible Area

Abstract Motivation The solvent accessible surface is an essential structural property measure related to the protein structure and protein function. Relative solvent accessible area (RSA) is a standard measure to describe the degree of residue exposure in the protein surface or inside of protein. However, this computation will fail when the residues information is missing. Results In this article, we proposed a novel method for estimation RSA using the Cα atom distance matrix with the deep learning method (EAGERER). The new method, EAGERER, achieves Pearson correlation coefficients of 0.921–0.928 on two independent test datasets. We empirically demonstrate that EAGERER can yield better Pearson correlation coefficients than existing RSA estimators, such as coordination number, half sphere exposure and SphereCon. To the best of our knowledge, EAGERER represents the first method to estimate the solvent accessible area using limited information with a deep learning model. It could be useful to the protein structure and protein function prediction. Availabilityand implementation The method is free available at https://github.com/cliffgao/EAGERER. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Traditional Bangladeshi Sports Video Classification Using Deep Learning Method

2021 ◽  
Vol 11 (5) ◽  
pp. 2149
Author(s):  
Moumita Sen Sarma ◽  
Kaushik Deb ◽  
Pranab Kumar Dhar ◽  
Takeshi Koshiba
Keyword(s):  
Deep Learning ◽  
Short Term Memory ◽  
Easy Access ◽  
Learning Method ◽  
Cultural Significance ◽  
Sports Video ◽  
Temporal Features ◽  
Average Accuracy ◽  
Proposed Model

Sports activities play a crucial role in preserving our health and mind. Due to the rapid growth of sports video repositories, automatized classification has become essential for easy access and retrieval, content-based recommendations, contextual advertising, etc. Traditional Bangladeshi sport is a genre of sports that bears the cultural significance of Bangladesh. Classification of this genre can act as a catalyst in reviving their lost dignity. In this paper, the Deep Learning method is utilized to classify traditional Bangladeshi sports videos by extracting both the spatial and temporal features from the videos. In this regard, a new Traditional Bangladeshi Sports Video (TBSV) dataset is constructed containing five classes: Boli Khela, Kabaddi, Lathi Khela, Kho Kho, and Nouka Baich. A key contribution of this paper is to develop a scratch model by incorporating the two most prominent deep learning algorithms: convolutional neural network (CNN) and long short term memory (LSTM). Moreover, the transfer learning approach with the fine-tuned VGG19 and LSTM is used for TBSV classification. Furthermore, the proposed model is assessed over four challenging datasets: KTH, UCF-11, UCF-101, and UCF Sports. This model outperforms some recent works on these datasets while showing 99% average accuracy on the TBSV dataset.

DeepMSPeptide: peptide detectability prediction using deep learning

2019 ◽  
Author(s):  
Guillermo Serrano ◽  
Elizabeth Guruceaga ◽  
Victor Segura
Keyword(s):  
Deep Learning ◽  
Protein Detection ◽  
Amino Acid Sequences ◽  
Supplementary Information ◽  
Learning Method ◽  
Supplementary Data ◽  
Stochastic Nature ◽  
Bioinformatic Tool ◽  
Peptide Detectability ◽  
Detection And Quantification

Abstract Summary The protein detection and quantification using high-throughput proteomic technologies is still challenging due to the stochastic nature of the peptide selection in the mass spectrometer, the difficulties in the statistical analysis of the results and the presence of degenerated peptides. However, considering in the analysis only those peptides that could be detected by mass spectrometry, also called proteotypic peptides, increases the accuracy of the results. Several approaches have been applied to predict peptide detectability based on the physicochemical properties of the peptides. In this manuscript, we present DeepMSPeptide, a bioinformatic tool that uses a deep learning method to predict proteotypic peptides exclusively based on the peptide amino acid sequences. Availability and implementation DeepMSPeptide is available at https://github.com/vsegurar/DeepMSPeptide. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals A Novel Hybrid Deep Learning Method for Fault Diagnosis of Rotating Machinery Based on Extended WDCNN and Long Short-Term Memory

Sensors ◽  
2021 ◽  
Vol 21 (19) ◽  
pp. 6614
Author(s):  
Yangde Gao ◽  
Cheol Hong Kim ◽  
Jong-Myon Kim
Keyword(s):  
Deep Learning ◽  
Fault Diagnosis ◽  
Short Term Memory ◽  
Rotating Machinery ◽  
Identification Accuracy ◽  
Learning Method ◽  
Short Term ◽  
Deep Convolutional Neural Networks ◽  
Term Memory ◽  
Long Short Term Memory

Deep learning (DL) plays a very important role in the fault diagnosis of rotating machinery. To enhance the self-learning capacity and improve the intelligent diagnosis accuracy of DL for rotating machinery, a novel hybrid deep learning method (NHDLM) based on Extended Deep Convolutional Neural Networks with Wide First-layer Kernels (EWDCNN) and long short-term memory (LSTM) is proposed for complex environments. First, the EWDCNN method is presented by extending the convolution layer of WDCNN, which can further improve automatic feature extraction. The LSTM then changes the geometric architecture of the EWDCNN to produce a novel hybrid method (NHDLM), which further improves the performance for feature classification. Compared with CNN, WDCNN, and EWDCNN, the proposed NHDLM method has the greatest performance and identification accuracy for the fault diagnosis of rotating machinery.

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