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Journal of Biomedical Science and Engineering 2020年第7期13卷 168-174页

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

Emergence of drug resistant bacteria is one of the serious problems in today’s public health. However, the relationship between genomic mutation of bacteria and the phenotypic difference of them is still unclear. In this paper, based on the mutation information in whole genome sequences of 96 MRSA strains, two kinds of phenotypes (pathogenicity and drug resistance) were learnt and predicted by machine learning algorithms. As a result of effective feature selection by cross entropy based sparse logistic regression, these phenotypes could be predicted in sufficiently high accuracy (100% and 97.87%, respectively) with less than 10 features. It means that we could develop a novel rapid test method in the future for checking MRSA phenotypes.

关键词： MRSA Phenotype Classification Feature Selection High-Dimensional Binary Data Cross Entropy

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

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

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

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

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