iDNA6mA-Rice-DL: A local web server for identifying DNA N6-methyladenine sites in rice genome by deep learning method

2021 ◽  
pp. 2150019
Author(s):  
Shiqian He ◽  
Liang Kong ◽  
Jing Chen
Keyword(s):  
Deep Learning ◽  
Characteristic Curve ◽  
Rice Genome ◽  
Web Server ◽  
Learning Method ◽  
Independent Dataset ◽  
Intelligent Computing ◽  
Proposed Model ◽  
Genomic Scale ◽  
User Friendly

Accurate detection of N6-methyladenine (6mA) sites by biochemical experiments will help to reveal their biological functions, still, these wet experiments are laborious and expensive. Therefore, it is necessary to introduce a powerful computational model to identify the 6mA sites on a genomic scale, especially for plant genomes. In view of this, we proposed a model called iDNA6mA-Rice-DL for the effective identification of 6mA sites in rice genome, which is an intelligent computing model based on deep learning method. Traditional machine learning methods assume the preparation of the features for analysis. However, our proposed model automatically encodes and extracts key DNA features through an embedded layer and several groups of dense layers. We use an independent dataset to evaluate the generalization ability of our model. An area under the receiver operating characteristic curve (auROC) of 0.98 with an accuracy of 95.96% was obtained. The experiment results demonstrate that our model had good performance in predicting 6mA sites in the rice genome. A user-friendly local web server has been established. The Docker image of the local web server can be freely downloaded at https://hub.docker.com/r/his1server/idna6ma-rice-dl .

iRNAD: a computational tool for identifying D modification sites in RNA sequence

2019 ◽  
Vol 35 (23) ◽  
pp. 4922-4929 ◽  
Author(s):  
Zhao-Chun Xu ◽  
Peng-Mian Feng ◽  
Hui Yang ◽  
Wang-Ren Qiu ◽  
Wei Chen ◽  
...  
Keyword(s):  
Computational Models ◽  
Operating Characteristic ◽  
Cross Validation ◽  
Characteristic Curve ◽  
Support Vector ◽  
Final Model ◽  
Rna Sequence ◽  
Functional Roles ◽  
Proposed Model ◽  
User Friendly

Abstract Motivation Dihydrouridine (D) is a common RNA post-transcriptional modification found in eukaryotes, bacteria and a few archaea. The modification can promote the conformational flexibility of individual nucleotide bases. And its levels are increased in cancerous tissues. Therefore, it is necessary to detect D in RNA for further understanding its functional roles. Since wet-experimental techniques for the aim are time-consuming and laborious, it is urgent to develop computational models to identify D modification sites in RNA. Results We constructed a predictor, called iRNAD, for identifying D modification sites in RNA sequence. In this predictor, the RNA samples derived from five species were encoded by nucleotide chemical property and nucleotide density. Support vector machine was utilized to perform the classification. The final model could produce the overall accuracy of 96.18% with the area under the receiver operating characteristic curve of 0.9839 in jackknife cross-validation test. Furthermore, we performed a series of validations from several aspects and demonstrated the robustness and reliability of the proposed model. Availability and implementation A user-friendly web-server called iRNAD can be freely accessible at http://lin-group.cn/server/iRNAD, which will provide convenience and guide to users for further studying D modification.

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.

scholarly journals Malicious Text Identification: Deep Learning from Public Comments and Emails

Information ◽  
2020 ◽  
Vol 11 (6) ◽  
pp. 312 ◽  
Author(s):  
Asma Baccouche ◽  
Sadaf Ahmed ◽  
Daniel Sierra-Sosa ◽  
Adel Elmaghraby
Keyword(s):  
Social Media ◽  
Deep Learning ◽  
Language Processing ◽  
Short Term Memory ◽  
Good Alternative ◽  
Classification Problems ◽  
Short Term ◽  
Independent Dataset ◽  
Proposed Model ◽  
Long Short Term Memory

Identifying internet spam has been a challenging problem for decades. Several solutions have succeeded to detect spam comments in social media or fraudulent emails. However, an adequate strategy for filtering messages is difficult to achieve, as these messages resemble real communications. From the Natural Language Processing (NLP) perspective, Deep Learning models are a good alternative for classifying text after being preprocessed. In particular, Long Short-Term Memory (LSTM) networks are one of the models that perform well for the binary and multi-label text classification problems. In this paper, an approach merging two different data sources, one intended for Spam in social media posts and the other for Fraud classification in emails, is presented. We designed a multi-label LSTM model and trained it on the joint datasets including text with common bigrams, extracted from each independent dataset. The experiment results show that our proposed model is capable of identifying malicious text regardless of the source. The LSTM model trained with the merged dataset outperforms the models trained independently on each dataset.

scholarly journals Deep learning approaches for challenging species and gender identification of mosquito vectors

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Veerayuth Kittichai ◽  
Theerakamol Pengsakul ◽  
Kemmapon Chumchuen ◽  
Yudthana Samung ◽  
Patchara Sriwichai ◽  
...  
Keyword(s):  
Deep Learning ◽  
High Performance ◽  
Mosquito Species ◽  
Characteristic Curve ◽  
Confusion Matrix ◽  
Morphological Identification ◽  
Learning Approaches ◽  
Mosquito Vectors ◽  
Detection Rates ◽  
Proposed Model

AbstractMicroscopic observation of mosquito species, which is the basis of morphological identification, is a time-consuming and challenging process, particularly owing to the different skills and experience of public health personnel. We present deep learning models based on the well-known you-only-look-once (YOLO) algorithm. This model can be used to simultaneously classify and localize the images to identify the species of the gender of field-caught mosquitoes. The results indicated that the concatenated two YOLO v3 model exhibited the optimal performance in identifying the mosquitoes, as the mosquitoes were relatively small objects compared with the large proportional environment image. The robustness testing of the proposed model yielded a mean average precision and sensitivity of 99% and 92.4%, respectively. The model exhibited high performance in terms of the specificity and accuracy, with an extremely low rate of misclassification. The area under the receiver operating characteristic curve (AUC) was 0.958 ± 0.011, which further demonstrated the model accuracy. Thirteen classes were detected with an accuracy of 100% based on a confusion matrix. Nevertheless, the relatively low detection rates for the two species were likely a result of the limited number of wild-caught biological samples available. The proposed model can help establish the population densities of mosquito vectors in remote areas to predict disease outbreaks in advance.

scholarly journals Batik Classification using Deep Learning

2019 ◽  
Vol 8 (4) ◽  
pp. 11416-11421
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Data Analysis ◽  
Convolutional Neural Network ◽  
Image Data ◽  
High Accuracy ◽  
Learning Method ◽  
Philosophy Of Life ◽  
Proposed Model

Batik is one of the Indonesian cultural heritages that has been recognized by the global community. Indonesian batik has a vast diversity in motifs that illustrate the philosophy of life, the ancestral heritage and also reflects the origin of batik itself. Because of the manybatik motifs, problems arise in determining the type of batik itself. Therefore, we need a classification method that can classify various batik motifs automatically based on the batik images. The technique of image classification that is used widely now is deep learning method. This technique has been proven of its capacity in identifying images in high accuracy. Architecture that is widely used for the image data analysis is Convolutional Neural Network (CNN) because this architecture is able to detect and recognize objects in an image. This workproposes to use the method of CNN and VGG architecture that have been modified to overcome the problems of classification of the batik motifs. Experiments of using 2.448 batik images from 5 classes of batik motifs showed that the proposed model has successfully achieved an accuracy of 96.30%.

scholarly journals DeepScreening: a deep learning-based screening web server for accelerating drug discovery

Database ◽  
2019 ◽  
Vol 2019 ◽  
Author(s):  
Zhihong Liu ◽  
Jiewen Du ◽  
Jiansong Fang ◽  
Yulong Yin ◽  
Guohuan Xu ◽  
...  
Keyword(s):  
Deep Learning ◽  
Drug Discovery ◽  
Virtual Screening ◽  
De Novo ◽  
Web Server ◽  
Machine Learning Algorithms ◽  
Data Availability ◽  
Superior Performance ◽  
Learning Techniques ◽  
User Friendly

Abstract Deep learning contributes significantly to researches in biological sciences and drug discovery. Previous studies suggested that deep learning techniques have shown superior performance to other machine learning algorithms in virtual screening, which is a critical step to accelerate the drug discovery. However, the application of deep learning techniques in drug discovery and chemical biology are hindered due to the data availability, data further processing and lacking of the user-friendly deep learning tools and interface. Therefore, we developed a user-friendly web server with integration of the state of art deep learning algorithm, which utilizes either the public or user-provided dataset to help biologists or chemists perform virtual screening either the chemical probes or drugs for a specific target of interest. With DeepScreening, user could conveniently construct a deep learning model and generate the target-focused de novo libraries. The constructed classification and regression models could be subsequently used for virtual screening against the generated de novo libraries, or diverse chemical libraries in stock. From deep models training to virtual screening, and target focused de novo library generation, all those tasks could be finished with DeepScreening. We believe this deep learning-based web server will benefit to both biologists and chemists for probes or drugs discovery.

Human Activity Recognition using Fourier Transform Inspired Deep Learning Combination Model

2019 ◽  
Vol 9 (1) ◽  
pp. 16-31
Author(s):  
Kyungkoo Jun
Keyword(s):  
Fourier Transform ◽  
Deep Learning ◽  
Short Term Memory ◽  
Window Size ◽  
Sensor Data ◽  
Data Sets ◽  
Data Set ◽  
Proposed Model ◽  
Testing Data ◽  
Labeling Scheme

Background & Objective: This paper proposes a Fourier transform inspired method to classify human activities from time series sensor data. Methods: Our method begins by decomposing 1D input signal into 2D patterns, which is motivated by the Fourier conversion. The decomposition is helped by Long Short-Term Memory (LSTM) which captures the temporal dependency from the signal and then produces encoded sequences. The sequences, once arranged into the 2D array, can represent the fingerprints of the signals. The benefit of such transformation is that we can exploit the recent advances of the deep learning models for the image classification such as Convolutional Neural Network (CNN). Results: The proposed model, as a result, is the combination of LSTM and CNN. We evaluate the model over two data sets. For the first data set, which is more standardized than the other, our model outperforms previous works or at least equal. In the case of the second data set, we devise the schemes to generate training and testing data by changing the parameters of the window size, the sliding size, and the labeling scheme. Conclusion: The evaluation results show that the accuracy is over 95% for some cases. We also analyze the effect of the parameters on the performance.

Sign in / Sign up

Export Citation Format

Share Document