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Combined Use of k-Mer Numerical Features and Position-Specific Categorical Features in Fixed-Length DNA Sequence Classification

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Journal of Biomedical Science and Engineering 2017年第8期10卷 390-401页

作者： Dau phan ngoc Giang Nguyen Favorisen Rosyking Lumbanraja Mohammad Reza Faisal Bahriddin Abapihi Bedy Purnama Mera Kartika Delimayanti Mamoru Kubo Kenji Satou Graduate School of Natural Science and Technology Kanazawa University Kanazawa Japan Institute of Science and Engineering Kanazawa University Kanazawa Japan

To classify DNA sequences, k-mer frequency is widely used since it can convert variable-length sequences into fixed-length and numerical feature vectors. However, in case of fixed-length DNA sequence classification, subsequences starting at a specific position of the given sequence can also be used as categorical features. Through the performance evaluation on six datasets of fixed-length DNA sequences, our algorithm based on the above idea achieved comparable or better performance than other state-of-the art algorithms.

关键词： Sequence Classification Numerical and Categorical Features Feature Selection

DNA Sequence Classification by Convolutional Neural Network

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Journal of Biomedical Science and Engineering 2016年第5期9卷 280-286页

作者： ngoc Giang Nguyen Vu Anh Tran Duc Luu Ngo Dau phan Favorisen Rosyking Lumbanraja Mohammad Reza Faisal Bahriddin Abapihi Mamoru Kubo Kenji Satou Graduate School of Natural Science and Technology Kanazawa University Kanazawa Japan Institute of Science and Engineering Kanazawa University Kanazawa Japan

In recent years, a deep learning model called convolutional neural network with an ability of extracting features of high-level abstraction from minimum preprocessing data has been widely used. In this research, we proposed a new approach in classifying DNA sequences using the convolutional neural network while considering these sequences as text data. We used one-hot vectors to represent sequences as input to the model;therefore, it conserves the essential position information of each nucleotide in sequences. Using 12 DNA sequence datasets, we evaluated our proposed model and achieved significant improvements in all of these datasets. This result has shown a potential of using convolutional neural network for DNA sequence to solve other sequence problems in bioinformatics.

关键词： DNA Sequence Classification Deep Learning Convolutional Neural Network

Improved Protein Phosphorylation Site Prediction by a New Combination of Feature Set and Feature Selection

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Journal of Biomedical Science and Engineering 2018年第6期11卷 144-157页

作者： Favorisen Rosyking Lumbanraja ngoc Giang Nguyen Dau phan Mohammad Reza Faisal Bahriddin Abapihi Bedy Purnama Mera Kartika Delimayanti Mamoru Kubo Kenji Satou Graduate School of Natural Science and Technology Kanazawa University Kanazawa Japan Institute of Science and Engineering Kanazawa University Kanazawa Japan

Phosphorylation of protein is an important post-translational modification that enables activation of various enzymes and receptors included in signaling pathways. To reduce the cost of identifying phosphorylation site by laborious experiments, computational prediction of it has been actively studied. In this study, by adopting a new set of features and applying feature selection by Random Forest with grid search before training by Support Vector Machine, our method achieved better or comparable performance of phosphorylation site prediction for two different data sets.

关键词： Protein Phosphorylation Phosphorylation Site Prediction Sequence Feature Feature Selection with Grid Search

Improving Protein Sequence Classification Performance Using Adjacent and Overlapped Segments on Existing Protein Descriptors

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Journal of Biomedical Science and Engineering 2018年第6期11卷 126-143页

作者： Mohammad Reza Faisal Bahriddin Abapihi ngoc Giang Nguyen Bedy Purnama Mera Kartika Delimayanti Dau phan Favorisen Rosyking Lumbanraja Mamoru Kubo Kenji Satou Graduate School of Natural Science and Technology Kanazawa University Kanazawa Japan Institute of Science and Engineering Kanazawa University Kanazawa Japan

In protein sequence classification research, it is popular to convert a variable length sequence of protein into a fixed length numerical vector by using various descriptors, for instance, composition of k-mer composition. Such position-independent descriptors are useful since they are applicable to any length of sequence;however, positional information of subsequence is discarded even though it might have high contribution to classification performance. To solve this problem, we divided the original sequence into some segments, and then calculated the numerical features for them. It enables us to partially introduce positional information (for instance, compositions of serine in anterior and posterior segments of a sequence). Through comprehensive experiments on the number of segments and length of overlapping region, we found our classification approach with sequence segmentation and feature selection is effective to improve the performance. We evaluated our approach on three protein classification problems and achieved significant improvement in all cases which have a dataset with sufficient amino acid in each sequence. This result has shown the great potential of using additional segments in protein sequence classification to solve other sequence problems in bioinformatics.

关键词： Protein Sequence Classification Protein Descriptor Sequence Segmentation Feature Selection

法国Gironde河口盐水入侵与泥沙输送的三维数值研究

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人民珠江 2007年第1期 27-34页

作者： Kim Dan NGUYEN ngoc Vinh phan Sylvain GILLOU 彭晓飞卡昂大学数学与力学系卡昂大学吕萨克联合会

运用可计算河口流场的全三维数值模型研究Gironde河口的河口环流与盐水入侵问题。本模型应用有限差分法求解浅水方程,同时采用模态分裂技术获取关于水位的泊松方程。而湍流计算则采用二阶K-L模型求解。模型采用Gironde河口两次不同实测... 详细信息

运用可计算河口流场的全三维数值模型研究Gironde河口的河口环流与盐水入侵问题。本模型应用有限差分法求解浅水方程,同时采用模态分裂技术获取关于水位的泊松方程。而湍流计算则采用二阶K-L模型求解。模型采用Gironde河口两次不同实测数据进行率定参数与验证。通过对潮汐与径流作用的详细分析以研究该河口动力结构及影响河口环流盐水入侵的主要影响因素。

关键词：河口流态三维数值模拟有限差分

Applying Deep Learning Models to Mouse Behavior Recognition

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Journal of Biomedical Science and Engineering 2019年第2期12卷 183-196页

作者： ngoc Giang Nguyen Dau phan Favorisen Rosyking Lumbanraja Mohammad Reza Faisal Bahriddin Abapihi Bedy Purnama Mera Kartika Delimayanti Kunti Robiatul Mahmudah Mamoru Kubo Kenji Satou Graduate School of Natural Science and Technology Kanazawa University Kanazawa Japan Institute of Science and Engineering Kanazawa University Kanazawa Japan

In many animal-related studies, a high-performance animal behavior recognition system can help researchers reduce or get rid of the limitation of human assessments and make the experiments easier to reproduce. Recently, although deep learning models are holding state-of-the-art performances in human action recognition tasks, these models are not well-studied in applying to animal behavior recognition tasks. One reason is the lack of extensive datasets which are required to train these deep models for good performances. In this research, we investigated two current state-of-the-art deep learning models in human action recognition tasks, the I3D model and the R(2 + 1)D model, in solving a mouse behavior recognition task. We compared their performances with other models from previous researches and the results showed that the deep learning models that pre-trained using human action datasets then fine-tuned using the mouse behavior dataset can outperform other models from previous researches. It also shows promises of applying these deep learning models to other animal behavior recognition tasks without any significant modification in the models’ architecture, all we need to do is collecting proper datasets for the tasks and fine-tuning the pre-trained models using the collected data.

关键词： Mouse Behavior Recognition Deep Learning I3D Models R(2 + 1)D Models

Feature Analysis and Classification of Particle Data from Two-Dimensional Video Disdrometer

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Advances in Remote Sensing 2015年第1期4卷 1-14页

作者： Sergey Gavrilov Mamoru Kubo Vu Anh Tran Duc Luu Ngo ngoc Giang Nguyen Lan Anh T. Nguyen Favorisen Rosyking Lumbanraja Dau phan Kenji Satou Graduate School of Natural Science and Technology Kanazawa University Kanazawa Japan Institute of Science and Engineering Kanazawa University Kanazawa Japan Department of Computer Science Hue University of Education Hue Vietnam

We developed a ground observation system for solid precipitation using two-dimensional video disdrometer (2DVD). Among 16,010 particles observed by the system, around 10% of them were randomly sampled and manually classified into five classes which are snowflake, snowflake-like, intermediate, graupel-like, and graupel. At first, each particle was represented as a vector of 72 features containing fractal dimension and box-count to represent the complexity of particle shape. Feature analysis on the dataset clarified the importance of fractal dimension and box-count features for characterizing particles varying from snowflakes to graupels. On the other hand, performance evaluation of two-class classification by Support Vector Machine (SVM) was conducted. The experimental results revealed that, by selecting only 10 features out of 72, the average accuracy of classifying particles into snowflakes and graupels could reach around 95.4%, which had not been achieved by previous studies.

关键词： Solid Precipitation Particle Classification 2DVD Fractal Dimension PCA

Monitoring Heart Rate Variability Based on Self-powered ECG Sensor Tag

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Journal of Electronic & Information Systems 2022年第2期4卷 10-20页

作者： Nhat Minh Tran ngoc-giao Pham Thang Viet Tran Department of Telecommunication Sai Gon UniversityVietnam Department of Computing Fundamentals FPT UniversityHanoiVietnam Department of Science and Technology Nguyen Tat Thanh UniversityHo Chi Minh CityVietnam

This paper proposes a batteryless sensing and computational device to collect and process electrocardiography(ECG)signals for monitoring heart rate variability(HRV).The proposed system comprises of a passive UHF radio frequency identification(RFID)tag,an extreme low power microcontroller,a low-power ECG circuit,and a radio frequency(RF)energy *** microcontroller and ECG circuits consume less power of only~30μA and~3 mA,***,the proposed RF harvester operating at frequency band of 902 MHz~928 MHz can sufficiently collect available energy from the RFID reader to supply power to the system within a maximum distance of~2 *** extract R-peak of the ECG signal,a robust algorithm that consumes less time processing is also *** information of R-peaks is stored into an Electronic Product Code(EPC)Class 1st Generation 1st compliant ID of the tag and read by the *** reader is functioned to collected the R-peak data with sampling rate of 100ms;therefore,the user application can monitor fully range of *** performance of the proposed system shows that this study can provide a good solution in paving the way to new classes of healthcare applications.

关键词： Batteryless ECG sensor Heart rate monitoring UHF RFID

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垂直绿化综合效应研究

黑龙江生态工程职业学院学报 2017年第4期30卷 13-17页

作者： Tang Van Tai Tran ngoc Binh Nguyen Nhu Anh phan Hong Nhung 东南大学土木工程学院江苏南京210096 越南荣市医学大学越南议案省农业管理所南京工业大学江苏南京211800

除了美观上的效果,垂直绿化缓解热岛效应,减少水土流失,防尘减噪,节能环保,延长建筑寿命等作用也被越来越多的人意识到,有关垂直绿化各方面定性的研究也不断得到开展。以南京的三种墙面绿化形式—攀援式墙面绿化、铺贴式墙面绿化和组合... 详细信息

除了美观上的效果,垂直绿化缓解热岛效应,减少水土流失,防尘减噪,节能环保,延长建筑寿命等作用也被越来越多的人意识到,有关垂直绿化各方面定性的研究也不断得到开展。以南京的三种墙面绿化形式—攀援式墙面绿化、铺贴式墙面绿化和组合移动式墙面绿化作为研究对象,通过测定垂直绿化的降温、减噪和心理效应探讨垂直绿化对建筑环境的综合影响。

关键词：垂直绿化热效应噪音效应