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Acharya S, Shinada NK, Koyama N, Ikemori M, Nishioka T, Hitaoka S, Hakura A, Asakura S, Matsuoka Y, Palaniappan SK. Asking the right questions for mutagenicity prediction from BioMedical text. NPJ Syst Biol Appl 2023; 9:63. [PMID: 38110446 PMCID: PMC10728128 DOI: 10.1038/s41540-023-00324-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2022] [Accepted: 11/28/2023] [Indexed: 12/20/2023] Open
Abstract
Assessing the mutagenicity of chemicals is an essential task in the drug development process. Usually, databases and other structured sources for AMES mutagenicity exist, which have been carefully and laboriously curated from scientific publications. As knowledge accumulates over time, updating these databases is always an overhead and impractical. In this paper, we first propose the problem of predicting the mutagenicity of chemicals from textual information in scientific publications. More simply, given a chemical and evidence in the natural language form from publications where the mutagenicity of the chemical is described, the goal of the model/algorithm is to predict if it is potentially mutagenic or not. For this, we first construct a golden standard data set and then propose MutaPredBERT, a prediction model fine-tuned on BioLinkBERT based on a question-answering formulation of the problem. We leverage transfer learning and use the help of large transformer-based models to achieve a Macro F1 score of >0.88 even with relatively small data for fine-tuning. Our work establishes the utility of large language models for the construction of structured sources of knowledge bases directly from scientific publications.
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Affiliation(s)
| | - Nicolas K Shinada
- The Systems Biology Institute, Tokyo, Japan
- SBX Corporation, Tokyo, Japan
| | - Naoki Koyama
- Global Drug Safety, Eisai Co., Ltd., Tokyo, Japan
| | - Megumi Ikemori
- Planning Operation, hhc Data Creation Center, Eisai Co., Ltd., Tokyo, Japan
| | - Tomoki Nishioka
- 5D Integration Unit, hhc Data Creation Center, Eisai Co., Ltd., Tokyo, Japan
| | - Seiji Hitaoka
- 5D Integration Unit, hhc Data Creation Center, Eisai Co., Ltd., Tokyo, Japan
| | | | | | - Yukiko Matsuoka
- The Systems Biology Institute, Tokyo, Japan
- SBX Corporation, Tokyo, Japan
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2
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Ukwuoma CC, Cai D, Heyat MBB, Bamisile O, Adun H, Al-Huda Z, Al-Antari MA. Deep learning framework for rapid and accurate respiratory COVID-19 prediction using chest X-ray images. JOURNAL OF KING SAUD UNIVERSITY. COMPUTER AND INFORMATION SCIENCES 2023; 35:101596. [PMID: 37275558 PMCID: PMC10211254 DOI: 10.1016/j.jksuci.2023.101596] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/19/2022] [Revised: 05/17/2023] [Accepted: 05/19/2023] [Indexed: 06/07/2023]
Abstract
COVID-19 is a contagious disease that affects the human respiratory system. Infected individuals may develop serious illnesses, and complications may result in death. Using medical images to detect COVID-19 from essentially identical thoracic anomalies is challenging because it is time-consuming, laborious, and prone to human error. This study proposes an end-to-end deep-learning framework based on deep feature concatenation and a Multi-head Self-attention network. Feature concatenation involves fine-tuning the pre-trained backbone models of DenseNet, VGG-16, and InceptionV3, which are trained on a large-scale ImageNet, whereas a Multi-head Self-attention network is adopted for performance gain. End-to-end training and evaluation procedures are conducted using the COVID-19_Radiography_Dataset for binary and multi-classification scenarios. The proposed model achieved overall accuracies (96.33% and 98.67%) and F1_scores (92.68% and 98.67%) for multi and binary classification scenarios, respectively. In addition, this study highlights the difference in accuracy (98.0% vs. 96.33%) and F_1 score (97.34% vs. 95.10%) when compared with feature concatenation against the highest individual model performance. Furthermore, a virtual representation of the saliency maps of the employed attention mechanism focusing on the abnormal regions is presented using explainable artificial intelligence (XAI) technology. The proposed framework provided better COVID-19 prediction results outperforming other recent deep learning models using the same dataset.
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Affiliation(s)
- Chiagoziem C Ukwuoma
- The College of Nuclear Technology and Automation Engineering, Chengdu University of Technology, Sichuan, 610059, China
| | - Dongsheng Cai
- The College of Nuclear Technology and Automation Engineering, Chengdu University of Technology, Sichuan, 610059, China
| | - Md Belal Bin Heyat
- IoT Research Center, College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, Guangdong 518060, China
| | - Olusola Bamisile
- Sichuan Industrial Internet Intelligent Monitoring and Application Engineering Technology Research Center, Chengdu University of Technology, China
| | - Humphrey Adun
- Department of Mechanical and Energy Systems Engineering, Cyprus International University, Nicosia, North Nicosia, Cyprus
| | - Zaid Al-Huda
- School of Computing and Artificial Intelligence, Southwest Jiaotong University, Chengdu, Sichuan, China
| | - Mugahed A Al-Antari
- Department of Artificial Intelligence, College of Software & Convergence Technology, Daeyang AI Center, Sejong University, Seoul 05006, Republic of Korea
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3
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Azimifar M, Nejatian S, Parvin H, Bagherifard K, Rezaei V. A structure-protecting kernelized semi-supervised space adjustment for classification. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2022. [DOI: 10.3233/jifs-200224] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
We introduce a semi-supervised space adjustment framework in this paper. In the introduced framework, the dataset contains two subsets: (a) training data subset (space-one data (SOD)) and (b) testing data subset (space-two data (STD)). Our semi-supervised space adjustment framework learns under three assumptions: (I) it is assumed that all data points in the SOD are labeled, and only a minority of the data points in the STD are labeled (we call the labeled space-two data as LSTD), (II) the size of LSTD is very small comparing to the size of SOD, and (III) it is also assumed that the data of SOD and the data of STD have different distributions. We denote the unlabeled space-two data by ULSTD, which is equal to STD - LSTD. The aim is to map the training data, i.e., the data from the training labeled data subset and those from LSTD (note that all labeled data are considered to be training data, i.e., SOD ∪ LSTD) into a shared space (ShS). The mapped SOD, ULSTD, and LSTD into ShS are named MSOD, MULSTD, and MLSTD, respectively. The proposed method does the mentioned mapping in such a way that structures of the data points in SOD and MSOD, in STD and MSTD, in ULSTD and MULSTD, and in LSTD and MLSTD are the same. In the proposed method, the mapping is proposed to be done by a principal component analysis transformation on kernelized data. In the proposed method, it is tried to find a mapping that (a) can maintain the neighbors of data points after the mapping and (b) can take advantage of the class labels that are known in STD during transformation. After that, we represent and formulate the problem of finding the optimal mapping into a non-linear objective function. To solve it, we transform it into a semidefinite programming (SDP) problem. We solve the optimization problem with an SDP solver. The examinations indicate the superiority of the learners trained in the data mapped by the proposed approach to the learners trained in the data mapped by the state of the art methods.
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Affiliation(s)
- Maryam Azimifar
- Department of Computer Science, Yasooj Branch, Islamic Azad University, Yasooj, IR
| | - Samad Nejatian
- Department of Electrical Engineering, Yasooj Branch, Islamic Azad University, Yasooj, IR
| | - Hamid Parvin
- Department of Computer Science, Nourabad Mamasani Branch, Islamic Azad University, Mamasani, IR
| | | | - Vahideh Rezaei
- Department of Mathematics, Yasooj Branch, Islamic Azad University, Yasooj, IR
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Du Q, Zhu H. Dynamic elite strategy mayfly algorithm. PLoS One 2022; 17:e0273155. [PMID: 36006908 PMCID: PMC9409577 DOI: 10.1371/journal.pone.0273155] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2022] [Accepted: 08/03/2022] [Indexed: 11/18/2022] Open
Abstract
The mayfly algorithm (MA), as a newly proposed intelligent optimization algorithm, is found that easy to fall into the local optimum and slow convergence speed. To address this, an improved mayfly algorithm based on dynamic elite strategy (DESMA) is proposed in this paper. Specifically, it first determines the specific space near the best mayfly in the current population, and dynamically sets the search radius. Then generating a certain number of elite mayflies within this range. Finally, the best one among the newly generated elite mayflies is selected to replace the best mayfly in the current population when the fitness value of elite mayfly is better than that of the best mayfly. Experimental results on 28 standard benchmark test functions from CEC2013 show that our proposed algorithm outperforms its peers in terms of accuracy speed and stability.
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Affiliation(s)
- Qianhang Du
- School of Computer and Information Engineering, Bengbu University, Bengbu, Anhui 233030, China
| | - Honghao Zhu
- School of Computer and Information Engineering, Bengbu University, Bengbu, Anhui 233030, China
- * E-mail:
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Zheng Q, Yang M, Wang D, Tian X, Su H. An intelligent wireless communication model based on multi-feature fusion and quantile regression neural network. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2021. [DOI: 10.3233/jifs-202430] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Throughout the wireless communication network planning process, efficient signal reception power estimation is of great significance for accurate 5 G network deployment. The wireless propagation model predicts the radio wave propagation characteristics within the target communication coverage area, making it possible to estimate cell coverage, inter-cell network interference, and communication rates, etc. In this paper, we develop a series of features by considering various factors in the signal transmission process, including the shadow coefficient, absorption coefficient in test area and base station area, distance attenuation coefficient, density, azimuth angle, relative height and ground feature index coefficient. Then we design a quantile regression neural network to predict reference signal receiving power (RSRP) by feeding the above features. The network structure is specially constructed to be generalized on various complex real environments. To prove the effectiveness of proposed features and deep learning model, extensive comparative ablation experiments are applied. Finally, we have achieved the precision rate (PR), recall rate (RR), and inadequate coverage recognition rate (PCRR) of 84.3%, 78.4%, and 81.2% on the public dataset, respectively. The comparison with a series of state-of-the-art machine learning methods illustrates the superiority of the proposed method.
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Affiliation(s)
- Qinghe Zheng
- School of Information Science and Engineering, Shandong University, Qingdao, China
| | - Mingqiang Yang
- School of Information Science and Engineering, Shandong University, Qingdao, China
| | - Deqiang Wang
- School of Information Science and Engineering, Shandong University, Qingdao, China
| | - Xinyu Tian
- School of Intelligent Engineering, Shandong Management University, Jinan, China
| | - Huake Su
- School of Microelectronics, Xidian University, Xian, China
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Wang J, Yang M, Ding Z, Zheng Q, Wang D, Kpalma K, Ren J. Detection of the Deep-Sea Plankton Community in Marine Ecosystem with Underwater Robotic Platform. SENSORS (BASEL, SWITZERLAND) 2021; 21:6720. [PMID: 34695933 PMCID: PMC8537131 DOI: 10.3390/s21206720] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/16/2021] [Revised: 09/24/2021] [Accepted: 10/05/2021] [Indexed: 11/29/2022]
Abstract
Variations in the quantity of plankton impact the entire marine ecosystem. It is of great significance to accurately assess the dynamic evolution of the plankton for monitoring the marine environment and global climate change. In this paper, a novel method is introduced for deep-sea plankton community detection in marine ecosystem using an underwater robotic platform. The videos were sampled at a distance of 1.5 m from the ocean floor, with a focal length of 1.5-2.5 m. The optical flow field is used to detect plankton community. We showed that for each of the moving plankton that do not overlap in space in two consecutive video frames, the time gradient of the spatial position of the plankton are opposite to each other in two consecutive optical flow fields. Further, the lateral and vertical gradients have the same value and orientation in two consecutive optical flow fields. Accordingly, moving plankton can be accurately detected under the complex dynamic background in the deep-sea environment. Experimental comparison with manual ground-truth fully validated the efficacy of the proposed methodology, which outperforms six state-of-the-art approaches.
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Affiliation(s)
- Jiaxing Wang
- School of Information Science and Engineering, Shandong University, Jinan 266237, China; (J.W.); (Q.Z.); (D.W.)
| | - Mingqiang Yang
- School of Information Science and Engineering, Shandong University, Jinan 266237, China; (J.W.); (Q.Z.); (D.W.)
- Shenzhen Research Institute, Shandong University, Shenzhen 518000, China
| | - Zhongjun Ding
- China National Deep Sea Center, Qingdao 266237, China
| | - Qinghe Zheng
- School of Information Science and Engineering, Shandong University, Jinan 266237, China; (J.W.); (Q.Z.); (D.W.)
| | - Deqiang Wang
- School of Information Science and Engineering, Shandong University, Jinan 266237, China; (J.W.); (Q.Z.); (D.W.)
| | | | - Jinchang Ren
- Centre for Excellence in Signal and Image Processing, Department of Electronic and Electrical Engineering, University of Strathclyde, Glasgow G1 1XQ, UK;
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7
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Filippov FitzHugh-Nagumo Neuron Model with Membrane Potential Threshold Control Policy. Neural Process Lett 2021. [DOI: 10.1007/s11063-021-10549-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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Fuzzy segmentation and black widow-based optimal SVM for skin disease classification. Med Biol Eng Comput 2021; 59:2019-2035. [PMID: 34417956 DOI: 10.1007/s11517-021-02415-w] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2020] [Accepted: 07/05/2021] [Indexed: 10/20/2022]
Abstract
The skin, which has seven layers, is the main human organ and external barrier. According to the World Health Organization (WHO), skin cancer is the fourth leading cause of non-fatal disease risk. In medicinal fields, skin disease classification is a major challenging issue due to inaccurate outputs, overfitting, larger computational cost, and so on. We presented a novel approach of support vector machine-based black widow optimization (SVM-BWO) for skin disease classification. Five different kinds of skin disease images are taken such as psoriasis, paederus, herpes, melanoma, and benign with healthy images which are chosen for this work. The pre-processing step is handled to remove the noises from the original input images. Thereafter, the novel fuzzy set segmentation algorithm subsequently segments the skin lesion region. From this, the color, gray-level co-occurrence matrix texture, and shape features are extracted for further process. Skin disease is classified with the usage of the SVM-BWO algorithm. The implementation works are handled in MATLAB-2018a, thereby the dataset images were collected from ISIC-2018 datasets. Experimentally, various kinds of performance analyses with state-of-the-art techniques are performed. Anyway, the proposed methodology outperforms better classification accuracy of 92% than other methods. Workflow diagram of the proposed methodology.
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Chen G, Hu Z, Guan N, Wang X. Finding therapeutic music for anxiety using scoring model. INT J INTELL SYST 2021. [DOI: 10.1002/int.22460] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Affiliation(s)
- Gong Chen
- Shenzhen Research Institute The Hong Kong Polytechnic University Shenzhen China
- Department of Computing The Hong Kong Polytechnic University, Hung Hom Kowloon China
| | - Zhejing Hu
- Department of Computing The Hong Kong Polytechnic University, Hung Hom Kowloon China
| | - Nianhong Guan
- The Third Affiliated Hospital Sun Yat‐Sen University Guangzhou China
| | - Xiaoying Wang
- The Third Affiliated Hospital Sun Yat‐Sen University Guangzhou China
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10
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Attention-Based Deep Gated Fully Convolutional End-to-End Architectures for Time Series Classification. Neural Process Lett 2021. [DOI: 10.1007/s11063-021-10484-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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11
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Lima BVA, Neto ADD, Silva LES, Machado VP. Deep semi‐supervised classification based in deep clustering and cross‐entropy. INT J INTELL SYST 2021. [DOI: 10.1002/int.22446] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Affiliation(s)
- Bruno Vicente Alves Lima
- Departament of Computer and Automation Federal University of Rio Grande do Norte Natal Rio Grande do Norte Brazil
| | - Adrião Duarte Dória Neto
- Departament of Computer and Automation Federal University of Rio Grande do Norte Natal Rio Grande do Norte Brazil
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12
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Zheng W, Yan L, Gou C, Wang F. Fighting fire with fire: A spatial–frequency ensemble relation network with generative adversarial learning for adversarial image classification. INT J INTELL SYST 2021. [DOI: 10.1002/int.22372] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Affiliation(s)
- Wenbo Zheng
- School of Software Engineering, Xi'an Jiaotong University Xi'an China
- The State Key Laboratory for Management and Control of Complex Systems Institute of Automation, Chinese Academy of Sciences Beijing China
| | - Lan Yan
- The State Key Laboratory for Management and Control of Complex Systems Institute of Automation, Chinese Academy of Sciences Beijing China
- School of Artificial Intelligence, University of Chinese Academy of Sciences Beijing China
| | - Chao Gou
- School of Intelligent Systems Engineering, Sun Yat‐sen University Guangzhou China
| | - Fei‐Yue Wang
- The State Key Laboratory for Management and Control of Complex Systems Institute of Automation, Chinese Academy of Sciences Beijing China
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Wang B, Huang C, Guo Y, Tao J. Land Cover Classification based on Deep Convolutional Neural Network with Feature-based Data Augmentation. J Imaging Sci Technol 2021. [DOI: 10.2352/j.imagingsci.technol.2021.65.1.010504] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/01/2022]
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14
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Spectrum interference-based two-level data augmentation method in deep learning for automatic modulation classification. Neural Comput Appl 2020. [DOI: 10.1007/s00521-020-05514-1] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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15
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Zhang L, Liang Z. Robust Two-Dimensional Linear Discriminant Analysis via Information Divergence. Neural Process Lett 2020. [DOI: 10.1007/s11063-020-10359-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2022]
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