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Csikász-Nagy A, Fichó E, Noto S, Reguly I. Computational tools to predict context-specific protein complexes. Curr Opin Struct Biol 2024; 88:102883. [PMID: 38986166 DOI: 10.1016/j.sbi.2024.102883] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2024] [Revised: 05/21/2024] [Accepted: 06/19/2024] [Indexed: 07/12/2024]
Abstract
Interactions between thousands of proteins define cells' protein-protein interaction (PPI) network. Some of these interactions lead to the formation of protein complexes. It is challenging to identify a protein complex in a haystack of protein-protein interactions, and it is even more difficult to predict all protein complexes of the complexome. Simulations and machine learning approaches try to crack these problems by looking at the PPI network or predicted protein structures. Clustering of PPI networks led to the first protein complex predictions, while most recently, atomistic models of protein complexes and deep-learning-based structure prediction methods have also emerged. The simulation of PPI level interactions even enables the quantitative prediction of protein complexes. These methods, the required data sources, and their potential future developments are discussed in this review.
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Affiliation(s)
- Attila Csikász-Nagy
- Cytocast Hungary Kft, Budapest, Hungary; Faculty of Information Technology and Bionics, Pázmány Péter Catholic University, Budapest, Hungary.
| | | | - Santiago Noto
- Cytocast Hungary Kft, Budapest, Hungary; Escola de Matemática Aplicada, Fundação Getúlio Vargas, Rio de Janeiro, Brazil
| | - István Reguly
- Cytocast Hungary Kft, Budapest, Hungary; Faculty of Information Technology and Bionics, Pázmány Péter Catholic University, Budapest, Hungary
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2
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Li X, Chen X, Rezaeipanah A. Automatic breast cancer diagnosis based on hybrid dimensionality reduction technique and ensemble classification. J Cancer Res Clin Oncol 2023; 149:7609-7627. [PMID: 36995408 DOI: 10.1007/s00432-023-04699-x] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2023] [Accepted: 03/17/2023] [Indexed: 03/31/2023]
Abstract
INTRODUCTION Feature selection in the face of high-dimensional data can reduce overfitting and learning time, and at the same time improve the accuracy and efficiency of the system. Since there are many irrelevant and redundant features in breast cancer diagnosis, removing such features leads to more accurate prediction and reduced decision time when dealing with large-scale data. Meanwhile, ensemble classifiers are powerful techniques to improve the prediction performance of classification models, where several individual classifier models are combined to achieve higher accuracy. METHODS In this paper, an ensemble classifier algorithm based on multilayer perceptron neural network is proposed for the classification task, in which the parameters (e.g., number of hidden layers, number of neurons in each hidden layer, and weights of links) are adjusted based on an evolutionary approach. Meanwhile, this paper uses a hybrid dimensionality reduction technique based on principal component analysis and information gain to address this problem. RESULTS The effectiveness of the proposed algorithm was evaluated based on the Wisconsin breast cancer database. In particular, the proposed algorithm provides an average of 17% better accuracy compared to the best results obtained from the existing state-of-the-art methods. CONCLUSION Experimental results show that the proposed algorithm can be used as an intelligent medical assistant system for breast cancer diagnosis.
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Affiliation(s)
- Xingyuan Li
- Depiecement of Oncology, The PLA Navy Anqing Hospital, Anqing, 246000, Anhui, China
| | - Xi Chen
- Department of Thyroid and Breast Surgery, Anqing Municipal Hospital, Anqing, 246000, Anhui, China.
| | - Amin Rezaeipanah
- Department of Computer Engineering, Persian Gulf University, Bushehr, Iran.
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3
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Zhu W, Yuan SS, Li J, Huang CB, Lin H, Liao B. A First Computational Frame for Recognizing Heparin-Binding Protein. Diagnostics (Basel) 2023; 13:2465. [PMID: 37510209 PMCID: PMC10377868 DOI: 10.3390/diagnostics13142465] [Citation(s) in RCA: 22] [Impact Index Per Article: 22.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2023] [Revised: 07/13/2023] [Accepted: 07/21/2023] [Indexed: 07/30/2023] Open
Abstract
Heparin-binding protein (HBP) is a cationic antibacterial protein derived from multinuclear neutrophils and an important biomarker of infectious diseases. The correct identification of HBP is of great significance to the study of infectious diseases. This work provides the first HBP recognition framework based on machine learning to accurately identify HBP. By using four sequence descriptors, HBP and non-HBP samples were represented by discrete numbers. By inputting these features into a support vector machine (SVM) and random forest (RF) algorithm and comparing the prediction performances of these methods on training data and independent test data, it is found that the SVM-based classifier has the greatest potential to identify HBP. The model could produce an auROC of 0.981 ± 0.028 on training data using 10-fold cross-validation and an overall accuracy of 95.0% on independent test data. As the first model for HBP recognition, it will provide some help for infectious diseases and stimulate further research in related fields.
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Affiliation(s)
- Wen Zhu
- Key Laboratory of Computational Science and Application of Hainan Province, Haikou 571158, China
- Key Laboratory of Data Science and Intelligence Education, Hainan Normal University, Ministry of Education, Haikou 571158, China
- School of Mathematics and Statistics, Hainan Normal University, Haikou 571158, China
| | - Shi-Shi Yuan
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Jian Li
- School of Basic Medical Sciences, Chengdu University, Chengdu 610106, China
| | - Cheng-Bing Huang
- School of Computer Science and Technology, ABa Teachers University, Chengdu 623002, China
| | - Hao Lin
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Bo Liao
- Key Laboratory of Computational Science and Application of Hainan Province, Haikou 571158, China
- Key Laboratory of Data Science and Intelligence Education, Hainan Normal University, Ministry of Education, Haikou 571158, China
- School of Mathematics and Statistics, Hainan Normal University, Haikou 571158, China
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4
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Tuerhong A, Silamujiang M, Xianmuxiding Y, Wu L, Mojarad M. An ensemble classifier method based on teaching-learning-based optimization for breast cancer diagnosis. J Cancer Res Clin Oncol 2023:10.1007/s00432-023-04861-5. [PMID: 37202580 DOI: 10.1007/s00432-023-04861-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2023] [Accepted: 05/13/2023] [Indexed: 05/20/2023]
Abstract
INTRODUCTION Epidemiological studies show that breast cancer is the most common cancer in women in the world. Breast cancer treatment can be very effective, especially when the disease is detected in the early stages. The goal can be achieved by using large-scale breast cancer data with the machine learning models METHODS: This paper proposes a new intelligent approach using an optimized ensemble classifier for breast cancer diagnosis. The classification is done by proposing a new intelligent Group Method of Data Handling (GMDH) neural network-based ensemble classifier. This method improves the performance of the machine learning technique by using a Teaching-Learning-Based Optimization (TLBO) algorithm to optimize the hyperparameters of the classifier. Meanwhile, we use TLBO as an evolutionary method to address the problem of appropriate feature selection in breast cancer data. RESULTS The simulation results show that the proposed method has a better accuracy between 7 and 26% compared to the best results of the existing equivalent algorithms. CONCLUSION According to the obtained results, we suggest the proposed algorithm as an intelligent medical assistant system for breast cancer diagnosis.
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Affiliation(s)
- Adila Tuerhong
- Department of Cardio-Oncology, Affiliated Tumor Hospital of Xinjiang Medical University, Urumqi, 830011, Xinjiang, China
| | - Mutalipu Silamujiang
- Department of Traumatic Orthopedic, The Sixth Affiliated Hospital of Xinjiang Medical University, Urumqi, 830002, Xinjiang, China
| | - Yilixiati Xianmuxiding
- Department of Emergency, Affiliated Tumor Hospital of Xinjiang Medical University, Urumqi, 830011, Xinjiang, China
| | - Li Wu
- Department of Cardio-Oncology, Affiliated Tumor Hospital of Xinjiang Medical University, Urumqi, 830011, Xinjiang, China.
| | - Musa Mojarad
- Department of Computer Engineering, Firoozabad Branch, Islamic Azad University, Firoozabad, Iran.
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Lin BM, Cho H, Liu C, Roach J, Ribeiro AA, Divaris K, Wu D. BZINB Model-Based Pathway Analysis and Module Identification Facilitates Integration of Microbiome and Metabolome Data. Microorganisms 2023; 11:766. [PMID: 36985339 PMCID: PMC10056694 DOI: 10.3390/microorganisms11030766] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2023] [Revised: 03/04/2023] [Accepted: 03/12/2023] [Indexed: 03/19/2023] Open
Abstract
Integration of multi-omics data is a challenging but necessary step to advance our understanding of the biology underlying human health and disease processes. To date, investigations seeking to integrate multi-omics (e.g., microbiome and metabolome) employ simple correlation-based network analyses; however, these methods are not always well-suited for microbiome analyses because they do not accommodate the excess zeros typically present in these data. In this paper, we introduce a bivariate zero-inflated negative binomial (BZINB) model-based network and module analysis method that addresses this limitation and improves microbiome-metabolome correlation-based model fitting by accommodating excess zeros. We use real and simulated data based on a multi-omics study of childhood oral health (ZOE 2.0; investigating early childhood dental caries, ECC) and find that the accuracy of the BZINB model-based correlation method is superior compared to Spearman's rank and Pearson correlations in terms of approximating the underlying relationships between microbial taxa and metabolites. The new method, BZINB-iMMPath, facilitates the construction of metabolite-species and species-species correlation networks using BZINB and identifies modules of (i.e., correlated) species by combining BZINB and similarity-based clustering. Perturbations in correlation networks and modules can be efficiently tested between groups (i.e., healthy and diseased study participants). Upon application of the new method in the ZOE 2.0 study microbiome-metabolome data, we identify that several biologically-relevant correlations of ECC-associated microbial taxa with carbohydrate metabolites differ between healthy and dental caries-affected participants. In sum, we find that the BZINB model is a useful alternative to Spearman or Pearson correlations for estimating the underlying correlation of zero-inflated bivariate count data and thus is suitable for integrative analyses of multi-omics data such as those encountered in microbiome and metabolome studies.
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Affiliation(s)
- Bridget M. Lin
- Department of Biostatistics, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Hunyong Cho
- Department of Biostatistics, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Chuwen Liu
- Department of Biostatistics, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Jeff Roach
- Research Computing, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Apoena Aguiar Ribeiro
- Division of Diagnostic Sciences, Adams School of Dentistry, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Kimon Divaris
- Division of Pediatric and Public Health, Adams School of Dentistry, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Di Wu
- Department of Biostatistics, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- Division of Oral and Craniofacial Health Sciences, Adams School of Dentistry, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
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6
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Advancement of Management Information System for Discovering Fraud in Master Card based Intelligent Supervised Machine Learning and Deep Learning during SARS-CoV2. Inf Process Manag 2023; 60:103231. [PMID: 36510563 PMCID: PMC9729587 DOI: 10.1016/j.ipm.2022.103231] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2022] [Revised: 11/09/2022] [Accepted: 12/06/2022] [Indexed: 12/14/2022]
Abstract
During coronavirus (SARS-CoV2) the number of fraudulent transactions is expanding at a rate of alarming (7352421 online transaction records). Additionally, the Master Card (MC) usage is increasing. To avoid massive losses, companies of finance must constantly improve their management information systems for discovering fraud in MC. In this paper, an approach of advancement management information system for discovering of MC fraud was developed using sequential modeling of data depend on intelligent forecasting methods such as deep Learning and intelligent supervised machine learning (ISML). The Long Short-Term Memory Network (LSTM), Logistic Regression (LR), and Random Forest (RF) were used. The dataset is separated into two parts: the training and testing data, with a ratio of 8:2. Also, the advancement of management information system has been evaluated using 10-fold cross validation depend on recall, f1-score, precision, Mean Absolute Error (MAE), Receiver Operating Curve (ROC), and Root Mean Square Error (RMSE). Finally various techniques of resampling used to forecast if a transaction of MC is genuine/fraudulent. Performance for without re-sampling, with under-sampling, and with over-sampling is measured for each Algorithm. Highest performance of without re-sampling was 0.829 for RF algorithm-F score. While for under-sampling, it was 0.871 for LSTM algorithm-RMSE. Further, for over-sampling, it was 0.921 for both RF algorithm-Precision and LSTM algorithm-F score. The results from running advancement of management information system revealed that using resampling technique with deep learning LSTM generated the best results than intelligent supervised machine learning.
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7
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Albu AI, Bocicor MI, Czibula G. MM-StackEns: A new deep multimodal stacked generalization approach for protein-protein interaction prediction. Comput Biol Med 2023; 153:106526. [PMID: 36623437 DOI: 10.1016/j.compbiomed.2022.106526] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2022] [Revised: 12/13/2022] [Accepted: 12/31/2022] [Indexed: 01/05/2023]
Abstract
Accurate in-silico identification of protein-protein interactions (PPIs) is a long-standing problem in biology, with important implications in protein function prediction and drug design. Current computational approaches predominantly use a single data modality for describing protein pairs, which may not fully capture the characteristics relevant for identifying PPIs. Another limitation of existing methods is their poor generalization to proteins outside the training graph. In this paper, we aim to address these shortcomings by proposing a new ensemble approach for PPI prediction, which learns information from two modalities, corresponding to pairs of sequences and to the graph formed by the training proteins and their interactions. Our approach uses a siamese neural network to process sequence information, while graph attention networks are employed for the network view. For capturing the relationships between the proteins in a pair, we design a new feature fusion module, based on computing the distance between the distributions corresponding to the two proteins. The prediction is made using a stacked generalization procedure, in which the final classifier is represented by a Logistic Regression model trained on the scores predicted by the sequence and graph models. Additionally, we show that protein sequence embeddings obtained using pretrained language models can significantly improve the generalization of PPI methods. The experimental results demonstrate the good performance of our approach, which surpasses all the related work on two Yeast data sets, while outperforming the majority of literature approaches on two Human data sets and on independent multi-species data sets.
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Affiliation(s)
- Alexandra-Ioana Albu
- Department of Computer Science, Babeş-Bolyai University, 1 Mihail Kogalniceanu Street, Cluj-Napoca, 400084, Romania.
| | - Maria-Iuliana Bocicor
- Department of Computer Science, Babeş-Bolyai University, 1 Mihail Kogalniceanu Street, Cluj-Napoca, 400084, Romania.
| | - Gabriela Czibula
- Department of Computer Science, Babeş-Bolyai University, 1 Mihail Kogalniceanu Street, Cluj-Napoca, 400084, Romania.
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8
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Lin B, Cho H, Liu C, Roach J, Ribeiro AA, Divaris K, Wu D. BZINB model-based pathway analysis and module identification facilitates integration of microbiome and metabolome data. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.01.30.526301. [PMID: 36778424 PMCID: PMC9915478 DOI: 10.1101/2023.01.30.526301] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
Integration of multi-omics data is a challenging but necessary step to advance our understanding of the biology underlying human health and disease processes. To date, investigations seeking to integrate multi-omics (e.g., microbiome and metabolome) employ simple correlation-based network analyses; however, these methods are not always well-suited for microbiome analyses because they do not accommodate the excess zeros typically present in these data. In this paper, we introduce a bivariate zero-inflated negative binomial (BZINB) model-based network and module analysis method that addresses this limitation and improves microbiome-metabolome correlation-based model fitting by accommodating excess zeros. We use real and simulated data based on a multi-omics study of childhood oral health (ZOE 2.0; investigating early childhood dental disease, ECC) and find that the accuracy of the BZINB model-based correlation method is superior compared to Spearman’s rank and Pearson correlations in terms of approximating the underlying relationships between microbial taxa and metabolites. The new method, BZINB-iMMPath facilitates the construction of metabolite-species and species-species correlation networks using BZINB and identifies modules of (i.e., correlated) species by combining BZINB and similarity-based clustering. Perturbations in correlation networks and modules can be efficiently tested between groups (i.e., healthy and diseased study participants). Upon application of the new method in the ZOE 2.0 study microbiome-metabolome data, we identify that several biologically-relevant correlations of ECC-associated microbial taxa with carbohydrate metabolites differ between healthy and dental caries-affected participants. In sum, we find that the BZINB model is a useful alternative to Spearman or Pearson correlations for estimating the underlying correlation of zero-inflated bivariate count data and thus is suitable for integrative analyses of multi-omics data such as those encountered in microbiome and metabolome studies.
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Affiliation(s)
- Bridget Lin
- Department of Biostatistics, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, United States
| | - Hunyong Cho
- Department of Biostatistics, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, United States
| | - Chuwen Liu
- Department of Biostatistics, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, United States
| | - Jeff Roach
- Research Computing, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, United States
| | - Apoena Aguiar Ribeiro
- Division of Diagnostic Sciences, Adams School of Dentistry, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, United States
| | - Kimon Divaris
- Division of Pediatric and Public Health, Adams School of Dentistry, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, United States,Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, United States
| | - Di Wu
- Department of Biostatistics, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, United States,Division of Oral and Craniofacial Health Sciences, Adams School of Dentistry, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599,Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, United States,Correspondence: ; Tel: +1-919-537-3277
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9
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An Efficient and Autonomous Scheme for Solving IoT Service Placement Problem Using the Improved Archimedes Optimization Algorithm. JOURNAL OF KING SAUD UNIVERSITY - COMPUTER AND INFORMATION SCIENCES 2023. [DOI: 10.1016/j.jksuci.2023.02.015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/23/2023]
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10
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Chen H, Cai Y, Ji C, Selvaraj G, Wei D, Wu H. AdaPPI: identification of novel protein functional modules via adaptive graph convolution networks in a protein-protein interaction network. Brief Bioinform 2023; 24:6918779. [PMID: 36526282 DOI: 10.1093/bib/bbac523] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2022] [Revised: 10/10/2022] [Accepted: 11/02/2022] [Indexed: 12/23/2022] Open
Abstract
Identifying unknown protein functional modules, such as protein complexes and biological pathways, from protein-protein interaction (PPI) networks, provides biologists with an opportunity to efficiently understand cellular function and organization. Finding complex nonlinear relationships in underlying functional modules may involve a long-chain of PPI and pose great challenges in a PPI network with an unevenly sparse and dense node distribution. To overcome these challenges, we propose AdaPPI, an adaptive convolution graph network in PPI networks to predict protein functional modules. We first suggest an attributed graph node presentation algorithm. It can effectively integrate protein gene ontology attributes and network topology, and adaptively aggregates low- or high-order graph structural information according to the node distribution by considering graph node smoothness. Based on the obtained node representations, core cliques and expansion algorithms are applied to find functional modules in PPI networks. Comprehensive performance evaluations and case studies indicate that the framework significantly outperforms state-of-the-art methods. We also presented potential functional modules based on their confidence.
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11
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Yenurkar G, Mal S. Future forecasting prediction of Covid-19 using hybrid deep learning algorithm. MULTIMEDIA TOOLS AND APPLICATIONS 2022; 82:22497-22523. [PMID: 36415331 PMCID: PMC9672606 DOI: 10.1007/s11042-022-14219-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/16/2021] [Revised: 10/27/2022] [Accepted: 11/01/2022] [Indexed: 06/01/2023]
Abstract
Due the quick spread of coronavirus disease 2019 (COVID-19), identification of that disease, prediction of mortality rate and recovery rate are considered as one of the critical challenges in the whole world. The occurrence of COVID-19 dissemination beyond the world is analyzed in this research and an artificial-intelligence (AI) based deep learning algorithm is suggested to detect positive cases of COVID19 patients, mortality rate and recovery rate using real-world datasets. Initially, the unwanted data like prepositions, links, hashtags etc., are removed using some pre-processing techniques. After that, term frequency inverse-term frequency (TF-IDF) andBag of Words (BoW) techniques are utilized to extract the features from pre-processed dataset. Then, Mayfly Optimization (MO) algorithm is performed to pick the relevant features from the set of features. Finally, two deep learning procedures, ResNet model and GoogleNet model, are hybridized to achieve the prediction process. Our system examines two different kinds of publicly available text datasets to identify COVID-19 disease as well as to predict mortality rate and recovery rate using those datasets. There are four different datasets are taken to analyse the performance, in which the proposed method achieves 97.56% accuracy which is 1.40% greater than Linear Regression (LR) and Multinomial Naive Bayesian (MNB), 3.39% higher than Random Forest (RF) and Stochastic gradient boosting (SGB) as well as 5.32% higher than Decision tree (DT) and Bagging techniques if first dataset. When compared to existing machine learning models, the simulation result indicates that a proposed hybrid deep learning method is valuable in corona virus identification and future mortality forecast study.
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Affiliation(s)
- Ganesh Yenurkar
- School of Computing Science & Engineering, VIT Bhopal University, Bhopal, India
- Yeshwantrao Chavan College of Engineering, Wanadongri, Nagpur, India
| | - Sandip Mal
- School of Computing Science & Engineering, VIT Bhopal University, Bhopal, India
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12
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Li B, Wang M, Hopcroft JE, He K. HoSIM: Higher-order Structural Importance based method for multiple local community detection. Knowl Based Syst 2022. [DOI: 10.1016/j.knosys.2022.109853] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/31/2022]
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13
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Optimal deploying IoT services on the fog computing: A metaheuristic-based multi-objective approach. JOURNAL OF KING SAUD UNIVERSITY - COMPUTER AND INFORMATION SCIENCES 2022. [DOI: 10.1016/j.jksuci.2022.10.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
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14
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Javed I, Butt MA, Khalid S, Shehryar T, Amin R, Syed AM, Sadiq M. Face mask detection and social distance monitoring system for COVID-19 pandemic. MULTIMEDIA TOOLS AND APPLICATIONS 2022; 82:14135-14152. [PMID: 36196269 PMCID: PMC9522539 DOI: 10.1007/s11042-022-13913-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/06/2022] [Revised: 07/04/2022] [Accepted: 09/12/2022] [Indexed: 06/16/2023]
Abstract
Coronavirus triggers several respirational infections such as sneezing, coughing, and pneumonia, which transmit humans to humans through airborne droplets. According to the guidelines of the World Health Organization, the spread of COVID-19 can be mitigated by avoiding public interactions in proximity and following standard operating procedures (SOPs) including wearing a face mask and maintaining social distancing in schools, shopping malls, and crowded areas. However, enforcing the adaptation of these SOPs on a larger scale is still a challenging task. With the emergence of deep learning-based visual object detection networks, numerous methods have been proposed to perform face mask detection on public spots. However, these methods require a huge amount of data to ensure robustness in real-time applications. Also, to the best of our knowledge, there is no standard outdoor surveillance-based dataset available to ensure the efficacy of face mask detection and social distancing methods in public spots. To this end, we present a large-scale dataset comprising of 10,000 outdoor images categorized into a binary class labeling i.e., face mask, and non-face masked people to accelerate the development of automated face mask detection and social distance measurement on public spots. Alongside, we also present an end-to-end pipeline to perform real-time face mask detection and social distance measurement in an outdoor environment. Initially, existing state-of-the-art single and multi-stage object detection networks are fine-tuned on the proposed dataset to evaluate their performance in terms of accuracy and inference time. Based on better performance, YOLO-v3 architecture is further optimized by tuning its feature extraction and region proposal generation layers to improve the performance in real-time applications. Our results indicate that the presented pipeline performed better than the baseline version, showing an improvement of 5.3% in terms of accuracy.
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Affiliation(s)
- Iram Javed
- Department of Computer Science and Information Technology, Mirpur University of Science and Technology, Azad Jammu and Kashmir, Pakistan
| | | | - Samina Khalid
- Department of Computer Science and Information Technology, Mirpur University of Science and Technology, Azad Jammu and Kashmir, Pakistan
| | - Tehmina Shehryar
- Department of Software Engineering, Mirpur University of Science and Technology, Azad Jammu and Kashmir, Pakistan
| | - Rashid Amin
- Department of Computer Science, University of Chakwal, Chakwal, 48800 Pakistan
| | | | - Marium Sadiq
- Department of Computer Science and Information Technology, Mirpur University of Science and Technology, Azad Jammu and Kashmir, Pakistan
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15
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Malik H, Anees T, Din M, Naeem A. CDC_Net: multi-classification convolutional neural network model for detection of COVID-19, pneumothorax, pneumonia, lung Cancer, and tuberculosis using chest X-rays. MULTIMEDIA TOOLS AND APPLICATIONS 2022; 82:13855-13880. [PMID: 36157356 PMCID: PMC9485026 DOI: 10.1007/s11042-022-13843-7] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/17/2022] [Revised: 06/30/2022] [Accepted: 09/06/2022] [Indexed: 05/27/2023]
Abstract
Coronavirus (COVID-19) has adversely harmed the healthcare system and economy throughout the world. COVID-19 has similar symptoms as other chest disorders such as lung cancer (LC), pneumothorax, tuberculosis (TB), and pneumonia, which might mislead the clinical professionals in detecting a new variant of flu called coronavirus. This motivates us to design a model to classify multi-chest infections. A chest x-ray is the most ubiquitous disease diagnosis process in medical practice. As a result, chest x-ray examinations are the primary diagnostic tool for all of these chest infections. For the sake of saving human lives, paramedics and researchers are working tirelessly to establish a precise and reliable method for diagnosing the disease COVID-19 at an early stage. However, COVID-19's medical diagnosis is exceedingly idiosyncratic and varied. A multi-classification method based on the deep learning (DL) model is developed and tested in this work to automatically classify the COVID-19, LC, pneumothorax, TB, and pneumonia from chest x-ray images. COVID-19 and other chest tract disorders are diagnosed using a convolutional neural network (CNN) model called CDC Net that incorporates residual network thoughts and dilated convolution. For this study, we used this model in conjunction with publically available benchmark data to identify these diseases. For the first time, a single deep learning model has been used to diagnose five different chest ailments. In terms of classification accuracy, recall, precision, and f1-score, we compared the proposed model to three CNN-based pre-trained models, such as Vgg-19, ResNet-50, and inception v3. An AUC of 0.9953 was attained by the CDC Net when it came to identifying various chest diseases (with an accuracy of 99.39%, a recall of 98.13%, and a precision of 99.42%). Moreover, CNN-based pre-trained models Vgg-19, ResNet-50, and inception v3 achieved accuracy in classifying multi-chest diseases are 95.61%, 96.15%, and 95.16%, respectively. Using chest x-rays, the proposed model was found to be highly accurate in diagnosing chest diseases. Based on our testing data set, the proposed model shows significant performance as compared to its competitor methods. Statistical analyses of the datasets using McNemar's, and ANOVA tests also showed the robustness of the proposed model.
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Affiliation(s)
- Hassaan Malik
- Department of Computer Science, University of Management and Technology, Lahore, 54000 Pakistan
| | - Tayyaba Anees
- Department of Software Engineering, University of Management and Technology, Lahore, 54000 Pakistan
| | - Muizzud Din
- Department of Computer Science, Ghazi University, Dera Ghazi Khan, 32200 Pakistan
| | - Ahmad Naeem
- Department of Computer Science, University of Management and Technology, Lahore, 54000 Pakistan
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16
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Liu H, Zhuang Y, Song E, Xu X, Hung CC. A bidirectional multilayer contrastive adaptation network with anatomical structure preservation for unpaired cross-modality medical image segmentation. Comput Biol Med 2022; 149:105964. [PMID: 36007288 DOI: 10.1016/j.compbiomed.2022.105964] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2022] [Revised: 07/16/2022] [Accepted: 08/13/2022] [Indexed: 11/03/2022]
Abstract
Multi-modal medical image segmentation has achieved great success through supervised deep learning networks. However, because of domain shift and limited annotation information, unpaired cross-modality segmentation tasks are still challenging. The unsupervised domain adaptation (UDA) methods can alleviate the segmentation degradation of cross-modality segmentation by knowledge transfer between different domains, but current methods still suffer from the problems of model collapse, adversarial training instability, and mismatch of anatomical structures. To tackle these issues, we propose a bidirectional multilayer contrastive adaptation network (BMCAN) for unpaired cross-modality segmentation. The shared encoder is first adopted for learning modality-invariant encoding representations in image synthesis and segmentation simultaneously. Secondly, to retain the anatomical structure consistency in cross-modality image synthesis, we present a structure-constrained cross-modality image translation approach for image alignment. Thirdly, we construct a bidirectional multilayer contrastive learning approach to preserve the anatomical structures and enhance encoding representations, which utilizes two groups of domain-specific multilayer perceptron (MLP) networks to learn modality-specific features. Finally, a semantic information adversarial learning approach is designed to learn structural similarities of semantic outputs for output space alignment. Our proposed method was tested on three different cross-modality segmentation tasks: brain tissue, brain tumor, and cardiac substructure segmentation. Compared with other UDA methods, experimental results show that our proposed BMCAN achieves state-of-the-art segmentation performance on the above three tasks, and it has fewer training components and better feature representations for overcoming overfitting and domain shift problems. Our proposed method can efficiently reduce the annotation burden of radiologists in cross-modality image analysis.
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Affiliation(s)
- Hong Liu
- Center for Biomedical Imaging and Bioinformatics, School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, 430074, China.
| | - Yuzhou Zhuang
- Institute of Artificial Intelligence, Huazhong University of Science and Technology, Wuhan, 430074, China.
| | - Enmin Song
- Center for Biomedical Imaging and Bioinformatics, School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, 430074, China.
| | - Xiangyang Xu
- Center for Biomedical Imaging and Bioinformatics, School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, 430074, China.
| | - Chih-Cheng Hung
- Center for Machine Vision and Security Research, Kennesaw State University, Marietta, MA, 30060, USA.
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17
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EDCWRN: efficient deep clustering with the weight of representations and the help of neighbors. APPL INTELL 2022. [DOI: 10.1007/s10489-022-03895-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
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18
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Li T, Rezaeipanah A, Tag El Din EM. An ensemble agglomerative hierarchical clustering algorithm based on clusters clustering technique and the novel similarity measurement. JOURNAL OF KING SAUD UNIVERSITY - COMPUTER AND INFORMATION SCIENCES 2022. [DOI: 10.1016/j.jksuci.2022.04.010] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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19
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Guo Y, Li Y, Qian Y. Local government debt risk assessment: A deep learning-based perspective. Inf Process Manag 2022. [DOI: 10.1016/j.ipm.2022.102948] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
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20
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Nasiri E, Berahmand K, Samei Z, Li Y. Impact of Centrality Measures on the Common Neighbors in Link Prediction for Multiplex Networks. BIG DATA 2022; 10:138-150. [PMID: 35333606 DOI: 10.1089/big.2021.0254] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Complex networks are representations of real-world systems that can be better modeled as multiplex networks, where the same nodes develop multi-type connections. One of the important concerns about these networks is link prediction, which has many applications in social networks and recommender systems. In this article, similarity-based methods such as common neighbors (CNs) are the mainstream. However, in the CN method, the contribution of each CN in the likelihood of new connections is equally taken into account. In this work, we propose a new link prediction method namely Weighted Common Neighbors (WCN), which is based on CNs and various types of Centrality measures (including degree, k-core, closeness, betweenness, Eigenvector, and PageRank) to predict the formation of new links in multiplex networks. So, in this model, each CN has a different impact on the node connection likelihood. Moreover, we investigate the impact of interlayer information on improving the performance of link prediction in the target layer. Using Area under the ROC Curve and precision as evaluation metrics, we perform a comprehensive experimental evaluation of our proposed method on seven real multiplex networks. The results validate the improved performance of our proposed method compared with existing methods, and we show that the performance of proposed methods is significantly improved while using interlayer information in multiplex networks.
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Affiliation(s)
- Elahe Nasiri
- Department of Information Technology and Communications, Azarbaijan Shahid Madani University, Tabriz, Iran
| | - Kamal Berahmand
- School of Computer Science, Faculty of Science, Queensland University of Technology (QUT), Brisbane, Australia
| | - Zeynab Samei
- Department of Computer Science, Institute for Research in Fundamental Sciences (IPM), Tehran, Iran
| | - Yuefeng Li
- School of Computer Science, Faculty of Science, Queensland University of Technology (QUT), Brisbane, Australia
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21
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Rostami M, Forouzandeh S, Berahmand K, Soltani M, Shahsavari M, Oussalah M. Gene selection for microarray data classification via multi-objective graph theoretic-based method. Artif Intell Med 2022; 123:102228. [PMID: 34998517 DOI: 10.1016/j.artmed.2021.102228] [Citation(s) in RCA: 28] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2020] [Revised: 11/23/2021] [Accepted: 11/27/2021] [Indexed: 12/20/2022]
Abstract
In recent decades, the improvement of computer technology has increased the growth of high-dimensional microarray data. Thus, data mining methods for DNA microarray data classification usually involve samples consisting of thousands of genes. One of the efficient strategies to solve this problem is gene selection, which improves the accuracy of microarray data classification and also decreases computational complexity. In this paper, a novel social network analysis-based gene selection approach is proposed. The proposed method has two main objectives of the relevance maximization and redundancy minimization of the selected genes. In this method, on each iteration, a maximum community is selected repetitively. Then among the existing genes in this community, the appropriate genes are selected by using the node centrality-based criterion. The reported results indicate that the developed gene selection algorithm while increasing the classification accuracy of microarray data, will also decrease the time complexity.
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Affiliation(s)
- Mehrdad Rostami
- Centre of Machine Vision and Signal Processing, Faculty of Information Technology, University of Oulu, Oulu, Finland.
| | - Saman Forouzandeh
- Department of Computer Engineering, University of Applied Science and Technology, Center of Tehran Municipality ICT org., Tehran, Iran
| | - Kamal Berahmand
- School of Computer Sciences, Science and Engineering Faculty, Queensland University of Technology (QUT), Brisbane, Australia.
| | - Mina Soltani
- Department of Nutrition, Kashan University of Medical Sciences, Kashan, Iran
| | - Meisam Shahsavari
- Department of engineering physics, Tsinghua University, Beijing, China
| | - Mourad Oussalah
- Centre of Machine Vision and Signal Processing, Faculty of Information Technology, University of Oulu, Oulu, Finland; Research Unit of Medical Imaging, Physics, and Technology, Faculty of Medicine, University of Oulu, Finland.
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22
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Hu X, Feng C, Ling T, Chen M. Deep learning frameworks for protein–protein interaction prediction. Comput Struct Biotechnol J 2022; 20:3223-3233. [PMID: 35832624 PMCID: PMC9249595 DOI: 10.1016/j.csbj.2022.06.025] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2022] [Revised: 05/27/2022] [Accepted: 06/12/2022] [Indexed: 11/26/2022] Open
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23
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Yang Z, Cheng J. Recommendation algorithm based on attributed multiplex heterogeneous network. PeerJ Comput Sci 2021; 7:e822. [PMID: 35036537 PMCID: PMC8725661 DOI: 10.7717/peerj-cs.822] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2021] [Accepted: 11/29/2021] [Indexed: 06/14/2023]
Abstract
In the field of deep learning, the processing of large network models on billions or even tens of billions of nodes and numerous edge types is still flawed, and the accuracy of recommendations is greatly compromised when large network embeddings are applied to recommendation systems. To solve the problem of inaccurate recommendations caused by processing deficiencies in large networks, this paper combines the attributed multiplex heterogeneous network with the attention mechanism that introduces the softsign and sigmoid function characteristics and derives a new framework SSN_GATNE-T (S represents the softsign function, SN represents the attention mechanism introduced by the Softsign function, and GATNE-T represents the transductive embeddings learning for attribute multiple heterogeneous networks). The attributed multiplex heterogeneous network can help obtain more user-item information with more attributes. No matter how many nodes and types are included in the model, our model can handle it well, and the improved attention mechanism can help annotations to obtain more useful information via a combination of the two. This can help to mine more potential information to improve the recommendation effect; in addition, the application of the softsign function in the fully connected layer of the model can better reduce the loss of potential user information, which can be used for accurate recommendation by the model. Using the Adam optimizer to optimize the model can not only make our model converge faster, but it is also very helpful for model tuning. The proposed framework SSN_GATNE-T was tested for two different types of datasets, Amazon and YouTube, using three evaluation indices, ROC-AUC (receiver operating characteristic-area under curve), PR-AUC (precision recall-area under curve) and F1 (F1-score), and found that SSN_GATNE-T improved on all three evaluation indices compared to the mainstream recommendation models currently in existence. This not only demonstrates that the framework can deal well with the shortcomings of obtaining accurate interaction information due to the presence of a large number of nodes and edge types of the embedding of large network models, but also demonstrates the effectiveness of addressing the shortcomings of large networks to improve recommendation performance. In addition, the model is also a good solution to the cold start problem.
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