scholarly journals Predicting Protein Interactions Using a Deep Learning Method-Stacked Sparse Autoencoder Combined with a Probabilistic Classification Vector Machine

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
Yanbin Wang ◽  
Zhuhong You ◽  
Liping Li ◽  
Li Cheng ◽  
Xi Zhou ◽  
...  
Keyword(s):  
Deep Learning ◽  
Protein Interactions ◽  
Learning Algorithm ◽  
Computational Method ◽  
Support Vector ◽  
Sequence Information ◽  
Deep Learning Algorithm ◽  
Probabilistic Classification ◽  
Sparse Autoencoder ◽  
Stacked Sparse Autoencoder

Protein-protein interactions (PPIs), as an important molecular process within cells, are of pivotal importance in the biochemical function of cells. Although high-throughput experimental techniques have matured, enabling researchers to detect large amounts of PPIs, it has unavoidable disadvantages, such as having a high cost and being time consuming. Recent studies have demonstrated that PPIs can be efficiently detected by computational methods. Therefore, in this study, we propose a novel computational method to predict PPIs using only protein sequence information. This method was developed based on a deep learning algorithm-stacked sparse autoencoder (SSAE) combined with a Legendre moment (LM) feature extraction technique. Finally, a probabilistic classification vector machine (PCVM) classifier is used to implement PPI prediction. The proposed method was performed on human, unbalanced-human, H. pylori, and S. cerevisiae datasets with 5-fold cross-validation and yielded very high predictive accuracies of 98.58%, 97.71%, 93.76%, and 96.55%, respectively. To further evaluate the performance of our method, we compare it with the support vector machine- (SVM-) based method. The experimental results indicate that the PCVM-based method is obviously preferable to the SVM-based method. Our results have proven that the proposed method is practical, effective, and robust.

scholarly journals Deep learning-based framework for the distinction of membranous nephropathy: a new approach through hyperspectral imagery

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Tianqi Tu ◽  
Xueling Wei ◽  
Yue Yang ◽  
Nianrong Zhang ◽  
Wei Li ◽  
...  
Keyword(s):  
Deep Learning ◽  
Renal Biopsy ◽  
Membranous Nephropathy ◽  
Learning Algorithm ◽  
Hyperspectral Imagery ◽  
Chinese Patients ◽  
Support Vector ◽  
Deep Learning Algorithm ◽  
The Difference ◽  
Complex Deposition

Abstract Background Common subtypes seen in Chinese patients with membranous nephropathy (MN) include idiopathic membranous nephropathy (IMN) and hepatitis B virus-related membranous nephropathy (HBV-MN). However, the morphologic differences are not visible under the light microscope in certain renal biopsy tissues. Methods We propose here a deep learning-based framework for processing hyperspectral images of renal biopsy tissue to define the difference between IMN and HBV-MN based on the component of their immune complex deposition. Results The proposed framework can achieve an overall accuracy of 95.04% in classification, which also leads to better performance than support vector machine (SVM)-based algorithms. Conclusion IMN and HBV-MN can be correctly separated via the deep learning framework using hyperspectral imagery. Our results suggest the potential of the deep learning algorithm as a new method to aid in the diagnosis of MN.

Using geometric constraints to improve performance of image classifiers for automatic segmentation of traffic signs

GEOMATICA ◽  
2021 ◽  
pp. 1-23
Author(s):  
Roholah Yazdan ◽  
Masood Varshosaz ◽  
Saied Pirasteh ◽  
Fabio Remondino
Keyword(s):  
Deep Learning ◽  
Learning Algorithm ◽  
Automatic Segmentation ◽  
Geometric Constraints ◽  
Support Vector ◽  
Svm Classifier ◽  
Data Set ◽  
Traffic Signs ◽  
Deep Learning Algorithm ◽  
Comparison Results

Automatic detection and recognition of traffic signs from images is an important topic in many applications. At first, we segmented the images using a classification algorithm to delineate the areas where the signs are more likely to be found. In this regard, shadows, objects having similar colours, and extreme illumination changes can significantly affect the segmentation results. We propose a new shape-based algorithm to improve the accuracy of the segmentation. The algorithm works by incorporating the sign geometry to filter out the wrong pixels from the classification results. We performed several tests to compare the performance of our algorithm against those obtained by popular techniques such as Support Vector Machine (SVM), K-Means, and K-Nearest Neighbours. In these tests, to overcome the unwanted illumination effects, the images are transformed into colour spaces Hue, Saturation, and Intensity, YUV, normalized red green blue, and Gaussian. Among the traditional techniques used in this study, the best results were obtained with SVM applied to the images transformed into the Gaussian colour space. The comparison results also suggested that by adding the geometric constraints proposed in this study, the quality of sign image segmentation is improved by 10%–25%. We also comparted the SVM classifier enhanced by incorporating the geometry of signs with a U-Shaped deep learning algorithm. Results suggested the performance of both techniques is very close. Perhaps the deep learning results could be improved if a more comprehensive data set is provided.

scholarly journals Deep learning-based framework for the distinction of membranous nephropathy: A new approach through hyperspectral imagery

2021 ◽  
Author(s):  
Tianqi Tu ◽  
Xueling Wei ◽  
Yue Yang ◽  
Nianrong Zhang ◽  
Wei Li ◽  
...  
Keyword(s):  
Deep Learning ◽  
Renal Biopsy ◽  
Membranous Nephropathy ◽  
Learning Algorithm ◽  
Hyperspectral Imagery ◽  
Chinese Patients ◽  
Support Vector ◽  
Deep Learning Algorithm ◽  
The Difference ◽  
Complex Deposition

Abstract Background: Common subtypes seen in Chinese patients with membranous nephropathy (MN) include idiopathic membranous nephropathy (IMN) and hepatitis B virus-related membranous nephropathy (HBV-MN). However, the morphologic differences are not visible under the light microscope in certain renal biopsy tissues. Methods: We propose here a deep learning-based framework for processing hyperspectral images of renal biopsy tissue to define the difference between IMN and HBV-MN based on the component of their immune complex deposition. Results: The proposed framework can achieve an overall accuracy of 95.04% in classification, which also leads to better performance than support vector machine (SVM)-based algorithms. Conclusion: IMN and HBV-MN can be correctly separated via the deep learning framework using hyperspectral imagery. Our results suggest the potential of the deep learning algorithm as a new method to aid in the diagnosis of MN.

scholarly journals Deep learning-based framework for the distinction of membranous nephropathy: a new approach through hyperspectral imagery

2020 ◽  
Author(s):  
Tianqi Tu ◽  
Xueling Wei ◽  
Yue Yang ◽  
Nianrong Zhang ◽  
Wei Li ◽  
...  
Keyword(s):  
Deep Learning ◽  
Renal Biopsy ◽  
Membranous Nephropathy ◽  
Learning Algorithm ◽  
Hyperspectral Imagery ◽  
Chinese Patients ◽  
Support Vector ◽  
Deep Learning Algorithm ◽  
Biopsy Tissue ◽  
The Difference

Abstract Background：Common subtypes seen in Chinese patients with membranous nephropathy (MN) include idiopathic membranous nephropathy (IMN) and hepatitis B virus-related membranous nephropathy (HBV-MN). However, in some cases, the morphologic differences are not visible under the light microscope in the renal biopsy tissue.Methods：We proposed a deep learning-based framework for processing hyperspectral images of renal biopsy tissue to define the difference between IMN and HBV-MN based on the component of their immune complex deposition.Results: The proposed framework can achieve an overall accuracy of 95.04% for multiclass classification, which also proven to obtain a better performance compared to the support vector machine (SVM)-based algorithms.Conclusion：IMN and HBV-MN could be correctly separated via the deep learning framework using hyperspectral imagery. Our results suggest the potential of the deep learning algorithm as a new method to aid the diagnosis process of MN.

scholarly journals A deep learning framework for improving protein interaction prediction using sequence properties

2019 ◽  
Author(s):  
Yi Guo ◽  
Xiang Chen
Keyword(s):  
Deep Learning ◽  
Cellular Networks ◽  
Protein Interactions ◽  
Computational Method ◽  
Sequence Information ◽  
Protein Protein Interactions ◽  
Learning Framework ◽  
Amino Acid Properties ◽  
Wide Scale ◽  
Almost All

AbstractMotivationAlmost all critical functions and processes in cells are sustained by the cellular networks of protein-protein interactions (PPIs), understanding these is therefore crucial in the investigation of biological systems. Despite all past efforts, we still lack high-quality PPI data for constructing the networks, which makes it challenging to study the functions of association of proteins. High-throughput experimental techniques have produced abundant data for systematically studying the cellular networks of a biological system and the development of computational method for PPI identification.ResultsWe have developed a deep learning-based framework, named iPPI, for accurately predicting PPI on a proteome-wide scale depended only on sequence information. iPPI integrates the amino acid properties and compositions of protein sequence into a unified prediction framework using a hybrid deep neural network. Extensive tests demonstrated that iPPI can greatly outperform the state-of-the-art prediction methods in identifying PPIs. In addition, the iPPI prediction score can be related to the strength of protein-protein binding affinity and further showed the biological relevance of our deep learning framework to identify PPIs.Availability and ImplementationiPPI is available as an open-source software and can be downloaded from https://github.com/model-lab/[email protected]

scholarly journals Deep learning-based framework for the distinction of membranous nephropathy: A new approach through hyperspectral imagery

2020 ◽  
Author(s):  
Tianqi Tu ◽  
Xueling Wei ◽  
Yue Yang ◽  
Nianrong Zhang ◽  
Wei Li ◽  
...  
Keyword(s):  
Deep Learning ◽  
Renal Biopsy ◽  
Membranous Nephropathy ◽  
Learning Algorithm ◽  
Hyperspectral Imagery ◽  
Chinese Patients ◽  
Support Vector ◽  
Deep Learning Algorithm ◽  
Biopsy Tissue ◽  
The Difference

Abstract Background Common subtypes seen in Chinese patients with membranous nephropathy (MN) include idiopathic membranous nephropathy (IMN) and hepatitis B virus-related membranous nephropathy (HBV-MN). However, in some cases, the morphologic differences are not visible under the light microscope in the renal biopsy tissue. Methods We proposed a deep learning-based framework for processing hyperspectral images of renal biopsy tissue to define the difference between IMN and HBV-MN based on the component of their immune complex deposition. Results The proposed framework can achieve an overall accuracy of 95.04% for multiclass classification, which also proven to obtain a better performance compared to the support vector machine (SVM)-based algorithms. Conclusion IMN and HBV-MN could be correctly separated via the deep learning framework using hyperspectral imagery. Our results suggest the potential of the deep learning algorithm as a new method to aid the diagnosis process of MN.

A deep learning algorithm using a fully connected sparse autoencoder neural network for landslide susceptibility prediction

Landslides ◽  
2019 ◽  
Vol 17 (1) ◽  
pp. 217-229 ◽  
Author(s):  
Faming Huang ◽  
Jing Zhang ◽  
Chuangbing Zhou ◽  
Yuhao Wang ◽  
Jinsong Huang ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Landslide Susceptibility ◽  
Learning Algorithm ◽  
Deep Learning Algorithm ◽  
Sparse Autoencoder ◽  
Fully Connected

scholarly journals Distinguish virulent and temperate phage-derived sequences in metavirome data with a deep learning approach

2020 ◽  
Author(s):  
Shufang Wu ◽  
Zhencheng Fang ◽  
Jie Tan ◽  
Mo Li ◽  
Chunhui Wang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Dna Sequences ◽  
Learning Algorithm ◽  
Direct Detection ◽  
Temperate Phage ◽  
Computational Method ◽  
Computational Time ◽  
Link Type ◽  
Deep Learning Algorithm ◽  
Culture Independent

ABSTRACTBackgroundProkaryotic viruses referred to as phages can be divided into virulent and temperate phages. Distinguishing virulent and temperate phage-derived sequences in metavirome data is important for their role in interactions with bacterial hosts and regulations of microbial communities. However there is no experimental or computational approach to classify sequences of these two in culture-independent metavirome effectively, we present a new computational method DeePhage, which can directly and rapidly judge each read or contig as a virulent or temperate phage-derived fragment.FindingsDeePhage utilizes a “one-hot” encoding form to have an overall and detailed representation of DNA sequences. Sequence signatures are detected via a deep learning algorithm, namely a convolutional neural network to extract valuable local features. DeePhage makes better performance than the most related method PHACTS. The accuracy of DeePhage on five-fold validation reach as high as 88%, nearly 30% higher than PHACTS. Evaluation on real metavirome shows DeePhage annotated 54.4% of reliable contigs while PHACTS annotated 44.5%. While running on the same machine, DeePhage reduces computational time than PHACTS by 810 times. Besides, we proposed a new strategy to explore phage transformations in the microbial community by direct detection of the temperate viral fragments from metagenome and metavirome. The detectable transformation of temperate phages provided us a new insight into the potential treatment for human disease.ConclusionsDeePhage is the first tool that can rapidly and efficiently identify two kinds of phage fragments especially for metagenomics analysis with satisfactory performance. DeePhage is freely available via http://cqb.pku.edu.cn/ZhuLab/DeePhage or https://github.com/shufangwu/DeePhage.

scholarly journals Deep learning diagnostic and risk-stratification pattern detection for COVID-19 in digital lung auscultations: clinical protocol for a case–control and prospective cohort study

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Alban Glangetas ◽  
Mary-Anne Hartley ◽  
Aymeric Cantais ◽  
Delphine S. Courvoisier ◽  
David Rivollet ◽  
...  
Keyword(s):  
Deep Learning ◽  
Risk Stratification ◽  
Learning Algorithm ◽  
Signs And Symptoms ◽  
Support Vector ◽  
Nasopharyngeal Swab ◽  
University Hospitals ◽  
Deep Learning Algorithm ◽  
Audio Recordings ◽  
Lung Auscultation

Abstract Background Lung auscultation is fundamental to the clinical diagnosis of respiratory disease. However, auscultation is a subjective practice and interpretations vary widely between users. The digitization of auscultation acquisition and interpretation is a particularly promising strategy for diagnosing and monitoring infectious diseases such as Coronavirus-19 disease (COVID-19) where automated analyses could help decentralise care and better inform decision-making in telemedicine. This protocol describes the standardised collection of lung auscultations in COVID-19 triage sites and a deep learning approach to diagnostic and prognostic modelling for future incorporation into an intelligent autonomous stethoscope benchmarked against human expert interpretation. Methods A total of 1000 consecutive, patients aged ≥ 16 years and meeting COVID-19 testing criteria will be recruited at screening sites and amongst inpatients of the internal medicine department at the Geneva University Hospitals, starting from October 2020. COVID-19 is diagnosed by RT-PCR on a nasopharyngeal swab and COVID-positive patients are followed up until outcome (i.e., discharge, hospitalisation, intubation and/or death). At inclusion, demographic and clinical data are collected, such as age, sex, medical history, and signs and symptoms of the current episode. Additionally, lung auscultation will be recorded with a digital stethoscope at 6 thoracic sites in each patient. A deep learning algorithm (DeepBreath) using a Convolutional Neural Network (CNN) and Support Vector Machine classifier will be trained on these audio recordings to derive an automated prediction of diagnostic (COVID positive vs negative) and risk stratification categories (mild to severe). The performance of this model will be compared to a human prediction baseline on a random subset of lung sounds, where blinded physicians are asked to classify the audios into the same categories. Discussion This approach has broad potential to standardise the evaluation of lung auscultation in COVID-19 at various levels of healthcare, especially in the context of decentralised triage and monitoring. Trial registration: PB_2016-00500, SwissEthics. Registered on 6 April 2020.

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