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Alsuradi H, Shen J, Park W, Eid M. Evaluation of perceived urgency from single-trial EEG data elicited by upper-body vibration feedback using deep learning. Sci Rep 2024; 14:19604. [PMID: 39179642 PMCID: PMC11344029 DOI: 10.1038/s41598-024-70508-1] [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: 05/27/2024] [Accepted: 08/19/2024] [Indexed: 08/26/2024] Open
Abstract
Notification systems that convey urgency without adding cognitive burden are crucial in human-computer interaction. Haptic feedback systems, particularly those utilizing vibration feedback, have emerged as a compelling solution, capable of providing desirable levels of urgency depending on the application. High-risk applications require an evaluation of the urgency level elicited during critical notifications. Traditional evaluations of perceived urgency rely on subjective self-reporting and performance metrics, which, while useful, are not real-time and can be distracting from the task at hand. In contrast, EEG technology offers a direct, non-intrusive method of assessing the user's cognitive state. Leveraging deep learning, this study introduces a novel approach to evaluate perceived urgency from single-trial EEG data, induced by vibration stimuli on the upper body, utilizing our newly collected urgency-via-vibration dataset. The proposed model combines a 2D convolutional neural network with a temporal convolutional network to capture spatial and temporal EEG features, outperforming several established EEG models. The proposed model achieves an average classification accuracy of 83% through leave-one-subject-out cross-validation across three urgency classes (not urgent, urgent, and very urgent) from a single trial of EEG data. Furthermore, explainability analysis showed that the prefrontal brain region, followed by the central brain region, are the most influential in predicting the urgency level. A follow-up neural statistical analysis revealed an increase in event-related synchronization (ERS) in the theta frequency band (4-7 Hz) with the increased level of urgency, which is associated with high arousal and attention in the neuroscience literature. A limitation of this study is that the proposed model's performance was tested only the urgency-via-vibration dataset, which may affect the generalizability of the findings.
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Affiliation(s)
- Haneen Alsuradi
- Engineering Division, New York University Abu Dhabi, Abu Dhabi, UAE
- Center for Artificial Intelligence and Robotics, New York University Abu Dhabi, Abu Dhabi, UAE
| | - Jiacheng Shen
- Computer Science Department, New York University Shanghai, Shanghai, China
| | - Wanjoo Park
- Engineering Division, New York University Abu Dhabi, Abu Dhabi, UAE
| | - Mohamad Eid
- Engineering Division, New York University Abu Dhabi, Abu Dhabi, UAE.
- Center for Artificial Intelligence and Robotics, New York University Abu Dhabi, Abu Dhabi, UAE.
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Castro-Martínez JA, Vargas E, Díaz-Beltrán L, Esteban FJ. Comparative Analysis of Shapley Values Enhances Transcriptomics Insights across Some Common Uterine Pathologies. Genes (Basel) 2024; 15:723. [PMID: 38927658 PMCID: PMC11203383 DOI: 10.3390/genes15060723] [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: 04/11/2024] [Revised: 05/20/2024] [Accepted: 05/29/2024] [Indexed: 06/28/2024] Open
Abstract
Uterine pathologies pose a challenge to women's health on a global scale. Despite extensive research, the causes and origin of some of these common disorders are not well defined yet. This study presents a comprehensive analysis of transcriptome data from diverse datasets encompassing relevant uterine pathologies such as endometriosis, endometrial cancer and uterine leiomyomas. Leveraging the Comparative Analysis of Shapley values (CASh) technique, we demonstrate its efficacy in improving the outcomes of the classical differential expression analysis on transcriptomic data derived from microarray experiments. CASh integrates the microarray game algorithm with Bootstrap resampling, offering a robust statistical framework to mitigate the impact of potential outliers in the expression data. Our findings unveil novel insights into the molecular signatures underlying these gynecological disorders, highlighting CASh as a valuable tool for enhancing the precision of transcriptomics analyses in complex biological contexts. This research contributes to a deeper understanding of gene expression patterns and potential biomarkers associated with these pathologies, offering implications for future diagnostic and therapeutic strategies.
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Affiliation(s)
- José A. Castro-Martínez
- Systems Biology Unit, Department of Experimental Biology, Faculty of Experimental Sciences, University of Jaén, 23071 Jaén, Spain; (J.A.C.-M.); (E.V.); (L.D.-B.)
| | - Eva Vargas
- Systems Biology Unit, Department of Experimental Biology, Faculty of Experimental Sciences, University of Jaén, 23071 Jaén, Spain; (J.A.C.-M.); (E.V.); (L.D.-B.)
| | - Leticia Díaz-Beltrán
- Systems Biology Unit, Department of Experimental Biology, Faculty of Experimental Sciences, University of Jaén, 23071 Jaén, Spain; (J.A.C.-M.); (E.V.); (L.D.-B.)
- Clinical Research Unit, Department of Medical Oncology, University Hospital of Jaén, 23007 Jaén, Spain
| | - Francisco J. Esteban
- Systems Biology Unit, Department of Experimental Biology, Faculty of Experimental Sciences, University of Jaén, 23071 Jaén, Spain; (J.A.C.-M.); (E.V.); (L.D.-B.)
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Frade MCM, Beltrame T, Gois MDO, Pinto A, Tonello SCGDM, Torres RDS, Catai AM. Toward characterizing cardiovascular fitness using machine learning based on unobtrusive data. PLoS One 2023; 18:e0282398. [PMID: 36862737 PMCID: PMC9980797 DOI: 10.1371/journal.pone.0282398] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2022] [Accepted: 02/14/2023] [Indexed: 03/03/2023] Open
Abstract
Cardiopulmonary exercise testing (CPET) is a non-invasive approach to measure the maximum oxygen uptake ([Formula: see text]), which is an index to assess cardiovascular fitness (CF). However, CPET is not available to all populations and cannot be obtained continuously. Thus, wearable sensors are associated with machine learning (ML) algorithms to investigate CF. Therefore, this study aimed to predict CF by using ML algorithms using data obtained by wearable technologies. For this purpose, 43 volunteers with different levels of aerobic power, who wore a wearable device to collect unobtrusive data for 7 days, were evaluated by CPET. Eleven inputs (sex, age, weight, height, and body mass index, breathing rate, minute ventilation, total hip acceleration, walking cadence, heart rate, and tidal volume) were used to predict the [Formula: see text] by support vector regression (SVR). Afterward, the SHapley Additive exPlanations (SHAP) method was used to explain their results. SVR was able to predict the CF, and the SHAP method showed that the inputs related to hemodynamic and anthropometric domains were the most important ones to predict the CF. Therefore, we conclude that the cardiovascular fitness can be predicted by wearable technologies associated with machine learning during unsupervised activities of daily living.
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Affiliation(s)
| | - Thomas Beltrame
- Department of Physical Therapy, Federal University of São Carlos, São Carlos, São Paulo, Brazil
- Samsung R&D Institute Brazil–SRBR, Campinas, São Paulo, Brazil
- * E-mail:
| | | | - Allan Pinto
- Brazilian Synchrotron Light Laboratory (LNLS), Brazilian Center for Research in Energy and Materials (CNPEM), Campinas, São Paulo, Brazil
| | | | - Ricardo da Silva Torres
- Department of ICT and Natural Sciences, Faculty of Information Technology and Electrical Engineering, NTNU—Norwegian University of Science and Technology, Ålesund, Norway
| | - Aparecida Maria Catai
- Department of Physical Therapy, Federal University of São Carlos, São Carlos, São Paulo, Brazil
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Game-theoretic link relevance indexing on genome-wide expression dataset identifies putative salient genes with potential etiological and diapeutics role in colorectal cancer. Sci Rep 2022; 12:13409. [PMID: 35927308 PMCID: PMC9352798 DOI: 10.1038/s41598-022-17266-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2021] [Accepted: 07/22/2022] [Indexed: 11/08/2022] Open
Abstract
Diapeutics gene markers in colorectal cancer (CRC) can help manage mortality caused by the disease. We applied a game-theoretic link relevance Index (LRI) scoring on the high-throughput whole-genome transcriptome dataset to identify salient genes in CRC and obtained 126 salient genes with LRI score greater than zero. The biomarkers database lacks preliminary information on the salient genes as biomarkers for all the available cancer cell types. The salient genes revealed eleven, one and six overrepresentations for major Biological Processes, Molecular Function, and Cellular components. However, no enrichment with respect to chromosome location was found for the salient genes. Significantly high enrichments were observed for several KEGG, Reactome and PPI terms. The survival analysis of top protein-coding salient genes exhibited superior prognostic characteristics for CRC. MIR143HG, AMOTL1, ACTG2 and other salient genes lack sufficient information regarding their etiological role in CRC. Further investigation in LRI methodology and salient genes to augment the existing knowledge base may create new milestones in CRC diapeutics.
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Mathematical indices for the influence of risk factors on the lethality of a disease. J Math Biol 2021; 83:74. [PMID: 34878616 PMCID: PMC8653415 DOI: 10.1007/s00285-021-01700-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2021] [Revised: 10/21/2021] [Accepted: 11/19/2021] [Indexed: 11/15/2022]
Abstract
We develop a theoretical model to measure the relative relevance of different pathologies of the lethality of a disease in society. This approach allows a ranking of diseases to be determined, which can assist in establishing priorities for vaccination campaigns or prevention strategies. Among all possible measurements, we identify three families of rules that satisfy a combination of relevant properties: neutrality, irrelevance, and one of three composition concepts. One of these families includes, for instance, the Shapley value of the associated cooperative game. The other two families also include simple and intuitive indices. As an illustration, we measure the relative relevance of several pathologies in lethality due to COVID-19.
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Neog Bora P, Baruah VJ, Borkotokey S, Gogoi L, Mahanta P, Sarmah A, Kumar R, Moretti S. Identifying the Salient Genes in Microarray Data: A Novel Game Theoretic Model for the Co-Expression Network. Diagnostics (Basel) 2020; 10:E586. [PMID: 32823765 PMCID: PMC7460294 DOI: 10.3390/diagnostics10080586] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2020] [Revised: 08/05/2020] [Accepted: 08/10/2020] [Indexed: 12/16/2022] Open
Abstract
Microarray techniques are used to generate a large amount of information on gene expression. This information can be statistically processed and analyzed to identify the genes useful for the diagnosis and prognosis of genetic diseases. Game theoretic tools are applied to analyze the gene expression data. Gene co-expression networks are increasingly used to explore the system-level functionality of genes, where the roles of the genes in building networks in addition to their independent activities are also considered. In this paper, we develop a novel microarray network game by constructing a gene co-expression network and defining a game on this network. The notion of the Link Relevance Index (LRI) for this network game is introduced and characterized. The LRI successfully identifies the relevant cancer biomarkers. It also enables identifying salient genes in the colon cancer dataset. Network games can more accurately describe the interactions among genes as their basic premises are to consider the interactions among players prescribed by a network structure. LRI presents a tool to identify the underlying salient genes involved in cancer or other metabolic syndromes.
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Affiliation(s)
- Papori Neog Bora
- Department of Mathematics, Dibrugarh University, Dibrugarh 786004, India;
| | - Vishwa Jyoti Baruah
- Centre for Biotechnology and Bioinformatics, Dibrugarh University, Dibrugarh 786004, India
| | - Surajit Borkotokey
- Department of Mathematics, Dibrugarh University, Dibrugarh 786004, India;
| | - Loyimee Gogoi
- Department of Applied Mathematics, Northwestern Polytechnical University, Xi’an 710072, China;
| | - Priyakshi Mahanta
- Centre for Computer Science and Applications, Dibrugarh University, Dibrugarh 786004, India; (P.M.); (A.S.)
| | - Ankumon Sarmah
- Centre for Computer Science and Applications, Dibrugarh University, Dibrugarh 786004, India; (P.M.); (A.S.)
| | - Rajnish Kumar
- Economics Group, Queen’s Management School, Queen’s University, Belfast BT9 5EE, UK
| | - Stefano Moretti
- Université Paris-Dauphine, PSL Research University, CNRS, LAMSADE, 75016 Paris, France;
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Bostani R, Mirzaie M. Molecular Network Analysis in Rabies Pathogenesis Using Cooperative Game Theory. IRANIAN JOURNAL OF BIOTECHNOLOGY 2020; 18:e2551. [PMID: 33850945 PMCID: PMC8035416 DOI: 10.30498/ijb.2020.2551] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
Abstract
Background and Purpose Recently, many researchers from different fields of science have been used networks to analyze complex relational big data. The identification of which nodes are more important than the others, known as centrality analysis, is a key issue in biological network analysis. Although, several centralities have been introduced degree, closeness, and betweenness centralities are the most popular. These centralities are based on the individual position of each node and the cooperation and synergies between nodes have been ignored. Objectives Since in many cases, the network function is a consequence of cooperation and interaction between nodes, classical centralities were extended to a group of nodes instead of only individual nodes using cooperative game theory concepts. In this study, we analyze the protein interaction network inferred in rabies disease and rank gene products based on group centrality measurements to identify the novel gene candidates. Materials and Methods For this purpose, we used a game-theoretic approach at three scenarios, where the power of a coalition of genes assessed using different criteria including the neighbors of genes in the network, and predefined importance of the genes in its neighborhood. The Shapley value of such a game was considered as a new centrality. In this study, we analyze the network of gene products implicates rabies. The network has 1059 nodes and 8844 edges and centrality analysis was performed using CINNA package in R software. Results Based on three scenarios, we selected genes among the highest Shapley value that had low ranking from classical centralities. The enrichment analysis among the selected genes in scenario 1 indicates important pathways in rabies pathogenesis. Pair-wise correlation analysis reveals that changing the weights of nodes at different scenarios can significantly affect the results of ranking genes in the network. Conclusion A prior knowledge about the disease and the topology of the network, enable us to design an appropriate game and consequently infer some biological important nodes (genes) in the network. Obviously, a single centrality cannot capture all significant features embedded in the network.
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Affiliation(s)
- Razieh Bostani
- Department of Applied Mathematics, Faculty of Mathematical Sciences, Tarbiat Modares University, Tehran, Iran
| | - Mehdi Mirzaie
- Department of Applied Mathematics, Faculty of Mathematical Sciences, Tarbiat Modares University, Tehran, Iran
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