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COMBING: clustering in Oncology for Mathematical and Biological Identification of Novel Gene Signatures

IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2022年第6期19卷 3317-3331页

作者： Battistella, Enzo Vakalopoulou, Maria Sun, Roger Estienne, Theo Lerousseau, Marvin Nikolaev, Sergey Andres, Emilie Alvarez Carre, Alexandre Niyoteka, Stephane Robert, Charlotte Paragios, Nikos Deutsch, Eric Univ Paris Saclay Cent Supelec Math & Informat Complexite & Syst F-91190 Gif Sur Yvette France Univ Paris Saclay Cent Supelec Inria F-91190 Gif Sur Yvette France Gustave Roussy Canc Campus Noesia Ctr Artificial Intelligence Radiat Therapy Gustave Roussy Cent Supelec TheraPanacea F-94805 Villejuif France Univ Paris Saclay Inst Gustave Roussy INSERM U1030Mol Radiotherapy & Innovat Therapeut F-94800 Villejuif France Univ Paris 07 St Louis Hosp F-75010 Paris France

Precisionmedicine is a paradigm shift in healthcare relying heavily on genomics data. However, the complexity of biological interactions, the large number of genes aswell as the lack of comparisons on the analysis of data, remain a tremendous bottleneck regarding clinical adoption. In this paper, we introduce a novel, automatic and unsupervised framework to discover low-dimensional gene biomarkers. Our method is based on the LP-Stability algorithm, a high dimensional center-based unsupervised clustering algorithm. It offers modularity as concerns metric functions and scalability, while being able to automatically determine the best number of clusters. Our evaluation includes both mathematical and biological criteria to define a quantitativemetric. The recovered signature is applied to a variety of biological tasks, including screening of biological pathways and functions, and characterization relevance on tumor types and subtypes. Quantitative comparisons among different distance metrics, commonly used clusteringmethods and a referential gene signature used in the literature, confirmstate of the art performance of our approach. In particular, our signature, based on 27 genes, reports at least 30 times bettermathematical significance (average Dunn's Index) and 25% better biological significance (average Enrichment in Protein-Protein Interaction) than those produced by other referential clustering methods. Finally, our signature reports promising results on distinguishing immune inflammatory and immune desert tumors, while reporting a high balanced accuracy of 92% on tumor types classification and averaged balanced accuracy of 68% on tumor subtypes classification, which represents, respectively 7% and 9% higher performance compared to the referential signature.

关键词： clustering algorithms Biology Tumors Measurement Genomics Bioinformatics Indexes clustering predictive signature biomarkers genomics multi-tumor association

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Color Template Matching Based on Fuzzy Density clustering for Vision Sensor Based Shoe Detection in Human-Robot Coexisting Environment

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IEEE SENSORS JOURNAL 2022年第14期22卷 14320-14327页

作者： Brahma, Debarshi Paral, Pritam Chatterjee, Amitava Rakshit, Anjan Jadavpur Univ Dept Elect Engn Kolkata 700032 India

Fast-Match Algorithm is a fast and randomized template matching algorithm based on affine transformations. However, it is not suitable for color images as they are converted into greyscale images, and hence the color information is lost. An improved algorithm named Color Fast Affine Template Matching (CFAsT-Match), was thus proposed which utilizes all the three color channels (R, G, B). Our previous works have demonstrated how CFAsT-Match can be effectively used in shoe detection problem, wherein a robot can successfully track a target person from consecutive frames captured by an onboard camera. This paper proposes a new technique which extends the template matching algorithm using the Fuzzy Density based clustering Algorithm instead of the traditional DBSCAN Algorithm. Three variants of Fuzzy based DBSCAN algorithms have been implemented here and a novel similarity measure called Fuzzy-based-colour-sum-of-absolute-differences (FCSAD) has been proposed. Experimental results firmly demonstrate that Fuzzy DBSCAN based CFAsT-Match algorithms can comfortably outperform recently proposed crisp DBSCAN based CFAsT-Match algorithms for shoe detection in human-robot coexisting environments.

关键词： clustering algorithms Footwear Sensors Robots Image color analysis Color Target tracking Vision-sensing shoe-detection CFAsT-Match Fuzzy DBSCAN

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An Improved Data Fusion Algorithm Based on Cluster Head Election and Grey Prediction

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IEEE ACCESS 2024年 12卷 22746-22758页

作者： Wang, Jun Wang, Ning Sun, Bingnan Cao, Kerang Jung, Hoekyung El-Meligy, Mohammed A. Sharaf, Mohamed Shenyang Univ Chem Technol Coll Comp Sci & Technol Shenyang 110142 Peoples R China Key Lab Intelligent Technol Chem Proc Ind Liaoning Coll Engn Dept Comp Engn Shenyang 110142 Peoples R China Paichai Univ Comp Engn Dept Daejeon 35345 South Korea King Saud Univ Coll Engn Ind Engn Dept Riyadh 11421 Saudi Arabia

In traditional Wireless Sensor Network routing protocols, data collected through timed interval sensing tends to have high temporal redundancy, which leads to unnecessary energy drain. To alleviate this problem and enable sensor networks to save energy to some extent, a practical solution is to utilize prediction-based data fusion methods. To this end, this paper first proposes a Low Energy Adaptive clustering Hierarchy-Energy-Kopt-N algorithm, an optimization algorithm explicitly designed to address the cluster-head election phase of the Low Energy Adaptive clustering Hierarchy protocol. Then, a data collection model using data prediction techniques - the Grey Data Prediction Model is formatted. Combining these improvements, a new data fusion algorithm that relies on data prediction, Grey-Clusters-Leach (GCL), is proposed. Simulation experiments demonstrate that the network energy drain of the GCL algorithm is reduced by 18%, 35%, 21.5% and 20%, and the network operation critical period life is extended by 3%, 35%, 22%, and 5% compared to the EQDC LEACH, LEACH-E, and SEP algorithms, respectively. GCL can effectively manage the size and number of clusters and reduce the number of packet transmissions by 20%.

关键词： Prediction algorithms Data models Adaptation models Wireless sensor networks clustering algorithms Predictive models Integrated circuit modeling Wireless sensor network cluster head election grey model data fusion

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Two-Orientations Finite Markov Real-Time Local Anomaly Detection via Pixel-by-Pixel Processing for Hyperspectral Imagery

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IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 2024年 17卷 14219-14236页

作者： Liu, Shihui Song, Meiping Dalian Maritime Univ Informat & Technol Coll Ctr Hyperspectral Imaging Remote Sensing Dalian 116026 Peoples R China Qingdao Univ Technol Sch Informat & Control Engn Qingdao 266520 Peoples R China Guangzhou Acad Fine Arts Sch Innovat Design Guangzhou 510006 Peoples R China

Real-time local anomaly detection needs to be performed simultaneously with hyperspectral image acquisition. However, the discussion on the scope of using existing data, mainly focuses on line-by-line data acquisition methods and does not address pixel-by-pixel acquisition methods. In addition, localized information from a single line has a limited impact on the pixels captured by the whisk broom hyperspectral imaging sensor. Therefore, reasonable utilization of local information from different directions around the sample data to be measured is key to improving the detection performance of pixel-by-pixel processing. Based on the whisk broom hyperspectral imaging sensor data acquisition, the new pixel-by-pixel local correlation matrix R-based anomaly detection real-time processing method is proposed in this article. A two-orientations finite Markov model is established, which uses bidirectional local information to judge the anomaly of current pixels. Based on the evaluation of the current pixels, two distinct local sample correlation matrix R are designed, and two different recursive equations for correlation matrix R anomaly detection are derived. This approach enables real-time pixel-by-pixel processing, reduces the need for large amounts of data, and enhances detection efficiency. Neither version of real-time RAD has been studied in the past. According to the experimental findings, the proposed algorithm outperforms others in real-time anomaly detection across various real hyperspectral image datasets.

关键词： Anomaly detection Hyperspectral imaging Real-time systems Data acquisition Imaging Estimation clustering algorithms Hyperspectral image pixel-by-pixel acquisition real-time local anomaly detection two-orientations finite Markov

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Identifying Protein Complexes From Protein-Protein Interaction Networks Based on Fuzzy clustering and GO Semantic Information

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IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2022年第5期19卷 2882-2893页

作者： Pan, Xiangyu Hu, Lun Hu, Pengwei You, Zhu-Hong Wuhan Univ Technol Sch Comp Sci & Technol Wuhan 430070 Peoples R China Chinese Acad Sci Xinjiang Tech Inst Phys & Chem Urumqi 830011 Peoples R China Northwestern Polytech Univ Sch Comp Sci Xian 710072 Peoples R China

Protein complexes are of great significance to provide valuable insights into the mechanisms of biological processes of proteins. A variety of computational algorithms have thus been proposed to identify protein complexes in a protein-protein interaction network. However, few of them can perform their tasks by taking into account both network topology and protein attribute information in a unified fuzzy-based clustering framework. Since proteins in the same complex are similar in terms of their attribute information and the consideration of fuzzy clustering can also make it possible for us to identify overlapping complexes, we target to propose such a novel fuzzy-based clustering framework, namely FCAN-PCI, for an improved identification accuracy. To do so, the semantic similarity between the attribute information of proteins is calculated and we then integrate it into a well-established fuzzy clustering model together with the network topology. After that, a momentum method is adopted to accelerate the clustering procedure. FCAN-PCI finally applies a heuristical search strategy to identify overlapping protein complexes. A series of extensive experiments have been conducted to evaluate the performance of FCAN-PCI by comparing it with state-of-the-art identification algorithms and the results demonstrate the promising performance of FCAN-PCI.

关键词： Proteins Semantics clustering algorithms Task analysis Topology Ontologies Search problems Protein complex identification fuzzy clustering protein-protein interaction network gene ontology

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Enhancing DoS Detection in WSNs Using Enhanced Ant Colony Optimization Algorithm

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IEEE ACCESS 2024年 12卷 134651-134671页

作者： Al-Rawashdeh, Rana Aljughaiman, Ahmed Albuali, Abdullah Alsenani, Yousef Alnaeem, Mohammed King Fahd Univ Petr & Minerals Coll Comp & Math Dept Informat & Comp Sci Dhahran 31261 Saudi Arabia King Faisal Univ Coll Comp Sci & Informat Technol Dept Comp Networks & Commun Al Hasa 31982 Saudi Arabia King Abdulaziz Univ Coll Comp & Informat Technol Dept Informat Syst Jeddah 21589 Saudi Arabia

The increasing use of Wireless Sensor Networks (WSNs) is leading to network traffic growth as data exchange among WSN nodes increases. Protecting WSNs from Denial of Service (DoS) attacks is essential for enhancing data security and avoiding interruptions that can harm productivity and reputation. Detecting a DoS attack quickly is crucial to minimize its impact on the targeted system or network. To meet this requirement, it is critical to have an effective DoS attack detection mechanism that ensures system or network availability and safeguards data and resources. The suggested approach focuses on enhancing DoS attack detection, reducing anomalies, and offering an efficient way to protect WSNs from DoS attacks. A new framework has been proposed to improve DoS attack detection by using optimization techniques and Machine Learning (ML) algorithms to detect and manage DoS attacks effectively. This system integrates Ant Colony Optimization (ACO) with ML algorithms to propose the Enhanced Ant Colony Optimization (EACO) technique. The proposed system has been compared to ACO through the Support Vector Machine (SVM) and K-Nearest Neighbor (KNN) algorithms to assess their performance in identifying DoS attacks. The results from the assessment show that when the EACO algorithm is paired with ML algorithms, it can achieve accuracy, sensitivity, specificity, and F1 scores between 87.6% and 99.8%. Furthermore, the EACO surpasses ACO in terms of accuracy, sensitivity, specificity, F1 score, precision, and Negative Predictive Value (NPV) by about 3.64%, 38.6%, 1.11%, 27.53%, 16.35%, and 2.78%, respectively.

关键词： Wireless sensor networks Telecommunication traffic Protocols Support vector machines clustering algorithms Classification algorithms Ant colony optimization Denial-of-service attack Ant colony optimization (ACO) denial of service (DoS) enhanced ant colony optimization (EACO) low energy adaptive clustering hierarchy (LEACH) wireless sensor network (WSN) security

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Adaptive Density Peak clustering Algorithm 24

Adaptive Density Peak Clustering Algorithm

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2024 Guangdong-Hong Kong-Macao Greater Bay Area International Conference on Digital Economy and Artificial Intelligence, DEAI 2024

作者： Wang, Yane Samonte, Mary Jane School of Information Technology Mapua University Manila Philippines Xi'an Siyuan University Shaanxi Xi'an710000 China School of Information Technology Mapua University Manila0900 Philippines

ISBN: (纸本)9798400717147

In view of the situation that the cutoff distance needs to be manually specified in the density peak algorithm and the cluster center is wrongly selected, an adaptive density peak clustering algorithm is proposed. This algorithm can select the cutoff distance according to the natural distribution of the sample and redefine the local density. Effectively realize the selection of the initial central cluster, and then complete the clustering of the remaining samples based on the reintroduced similar clustering Sim-D. Comparing this algorithm with other algorithms on artificial data sets and UCI data sets, the ADPC algorithm achieves 100% correct clustering on artificial data sets, and its partial clustering effect on UCI data sets is also significantly better than the other four. clustering Algorithm. © 2024 ACM.

关键词： clustering algorithms

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Machine Learning-Based Network Intrusion Detection Optimization for Cloud Computing Environments

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IEEE TRANSACTIONS ON CONSUMER ELECTRONICS 2024年第4期70卷 7449-7460页

作者： Samriya, Jitendra Kumar Kumar, Surendra Kumar, Mohit Wu, Huaming Gill, Sukhpal Singh Indian Inst Informat Technol Sonepat Dept CSE Sonepat 131001 India GLA Univ Dept Comp Engn & Applicat Mathura 281406 India Dr BR Ambedkar Natl Inst Technol Dept IT Jalandhar 144011 India Tianjin Univ Ctr Appl Math Tianjin 300072 Peoples R China Queen Mary Univ London Sch Elect Engn & Comp Sci London E1 4NS England

Cloud computing is an emerging choice among businesses all over the world since it provides flexible and world wide Web computer capabilities as a customizable service. Because of the dispersed nature of cloud services, security is a major problem. Since it is extremely accessible to intruders for any kind of assault, privacy and security are major hurdles to the on-demand service's success. A massive increase in network traffic has opened the path for increasingly difficult and broad security vulnerabilities. The use of traditional Intrusion Detection Systems (IDS) to prevent these attempts has proven ineffective. Therefore, this paper proposes a novel Network Intrusion Detection System (NIDS) based on a Machine Learning (ML) model known as the Support Vector Machine (SVM) and eXtreme Gradient Boosting (XGBoost) techniques. Furthermore, the hyperparameter optimization technique based on the Crow Search Algorithm is being utilized to optimize the NIDS' performance. Besides, the XGBoost-based feature selection technique is used to improve the classification accuracy of NIDS's method. Finally, the performance of the proposed system is evaluated using the NSL-KDD and UNR-IDD datasets, and the experiment results show that it performs better than baselines and has the potential to be used in modern NIDS.

关键词： Support vector machines Machine learning algorithms Accuracy Feature extraction clustering algorithms Training Security Cloud computing machine learning network intrusion detection performance optimization

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Instance Selection via Voronoi Neighbors for Binary Classification Tasks

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IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2024年第8期36卷 3921-3933页

作者： Fu, Ying Liu, Kaibo Zhu, Wenbin South China Univ Technol Sch Business Adm Guangzhou 510000 Guangdong Peoples R China Univ Wisconsin Dept Ind & Syst Engn Madison WI 53706 USA

Large datasets available in many applications have enabled the training of binary classifiers to match or even outperform humans. However, the large volume of data introduces computational burden during the training and calibration of model parameters. Since the optimal decision surface (ODS) of a classification task is often determined by a few nearby instances, a novel PDOC-V method is proposed to identify them. A Bayesian probability model is adopted to describe the ODS. An instance is close to the ODS if its probability of belonging to the positive and negative classes is similar. The probabilities of an instance are estimated by partitioning the input space into cells containing a single instance via the Voronoi diagram and inspecting its Voronoi neighbors. A randomized ray shooting algorithm is adopted to accelerate our algorithm. In many natural datasets, the spatial distribution of instances is often uneven. For such datasets, our method is more robust than existing distance-based instance selection methods. Comprehensive experiments suggest that common classifiers trained on instances selected by PDOC-V can accurately recover the ODS. Moreover, for many natural datasets, common classifiers trained on 10% - 20% of instances can achieve more than 98% of the full set performance.

关键词： Training Support vector machines Task analysis Noise measurement clustering algorithms Bayes methods Image coding Binary classification data reduction dataset condensation instance selection pattern selection Voronoi diagram

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Community Detection in Social Networks Using a Local Approach Based on Node Ranking

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IEEE ACCESS 2024年 12卷 92892-92905页

作者： Sheykhzadeh, Jafar Zarei, Bagher Soleimanian Gharehchopogh, Farhad Islamic Azad Univ Dept Comp Engn Urmia Branch Orumiyeh *** Iran Islamic Azad Univ Fac Comp & Informat Technol Engn Shabestar Branch Shabestar *** Iran

Community detection is crucial for analyzing the structure of social networks and extracting hidden information from them. The goal is to find groups of nodes (communities) with high intra-group and low inter-group communications. This problem is NP-hard, and most existing algorithms are global with high computational complexity, especially for large networks. Recently, local methods with acceptable computational complexity have been developed, but many have low accuracy and are non-deterministic. This paper introduces a new local algorithm, LCD-SN, which identifies communities based on first- and second-degree neighbor nodes. Unlike other local algorithms, LCD-SN is highly accurate, definitive, and not dependent on initial seed nodes. Additionally, a new index is proposed to determine the importance of network nodes using their local characteristics (first- and second-degree neighbors). Using this index, LCD-SN first identifies important nodes, forms initial communities with these nodes and their first-degree neighbors, and then obtains final communities through post-processing. Experiments show that LCD-SN is effective in identifying communities in social networks.

关键词： Indexes Social networking (online) clustering algorithms Partitioning algorithms Accuracy Chaotic communication Time complexity Community structure detection modularity nodes ranking social network analysis

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