{"id":545,"date":"2023-05-14T12:42:13","date_gmt":"2023-05-14T10:42:13","guid":{"rendered":"https:\/\/sano.empressia.dev\/?post_type=people&p=545"},"modified":"2025-07-16T15:40:52","modified_gmt":"2025-07-16T13:40:52","slug":"jose-sousa","status":"publish","type":"people","link":"https:\/\/sano.science\/people\/jose-sousa\/","title":{"rendered":"Jose Sousa"},"excerpt":{"rendered":"

Research Team Leader of Computational Intelligence\u200b<\/p>\n","protected":false},"featured_media":18409,"template":"","people_teams":[19,32],"class_list":["post-545","people","type-people","status-publish","has-post-thumbnail","hentry","people_teams-research","people_teams-computational-intelligence"],"yoast_head":"\nJose Sousa - Centre for Computational Personalized Medicine<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/sano.science\/people\/jose-sousa\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Jose Sousa\" \/>\n<meta property=\"og:description\" content=\"Research Team Leader of Computational Intelligence\u200b\" \/>\n<meta property=\"og:url\" content=\"https:\/\/sano.science\/people\/jose-sousa\/\" \/>\n<meta property=\"og:site_name\" content=\"Centre for Computational Personalized Medicine\" \/>\n<meta property=\"article:publisher\" content=\"https:\/\/www.facebook.com\/sano.science\/\" \/>\n<meta property=\"article:modified_time\" content=\"2025-07-16T13:40:52+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/sano.science\/wp-content\/uploads\/2023\/05\/Jose-Sousa-Sano-1024x1024.png\" \/>\n\t<meta property=\"og:image:width\" content=\"1024\" \/>\n\t<meta property=\"og:image:height\" content=\"1024\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/png\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:site\" content=\"@sanoscience\" \/>\n<meta name=\"twitter:label1\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data1\" content=\"1 minute\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\\\/\\\/schema.org\",\"@graph\":[{\"@type\":\"WebPage\",\"@id\":\"https:\\\/\\\/sano.science\\\/people\\\/jose-sousa\\\/\",\"url\":\"https:\\\/\\\/sano.science\\\/people\\\/jose-sousa\\\/\",\"name\":\"Jose Sousa - 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He obtained his PhD under Prof. Ricardo Machado (University of Minho, Portugal) and Prof. Jose Mendes (University of Aveiro, Portugal) supervision at University of Minho, Portugal on developing complex network models to study software usage alignment with the project requirements. Previously and during his PhD he worked as Information Systems Director at I3S, a research institution of University of Porto (i3.sup.pt) where he deployed and managed all the IT infrastructure as well as deploying and developing software to support management and research operations. He was a collaborator of HDRUK \u2013 Swansea\/QUB substantial site as AI researcher and actively working with QUB Centre for Public Health as part of the QUB support to Northern Ireland Public Health response to pandemic where he has developed self-learning AI on publicly available and self-reporting data. He also has worked on genetic alignment modelling and on mining socio-technical systems.<\/p>\n","social_media":[{"icon":{"ID":11992,"id":11992,"title":"orcid","filename":"orcid.svg","filesize":15590,"url":"https:\/\/sano.science\/wp-content\/uploads\/2023\/05\/orcid.svg","link":"https:\/\/sano.science\/people\/maciej-malawski\/orcid\/","alt":"","author":"5","description":"","caption":"","name":"orcid","status":"inherit","uploaded_to":531,"date":"2023-07-06 11:21:29","modified":"2023-07-06 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of scientific expertise","content":"<p>He is the Research Leader of the Personal Health Data Science Group at SANO developing fundamental research on AI machine self-learning applied to non-communicable diseases. Previously he was the Manager of the Advanced Informatics Core Technology Unit in the Faculty of Medicine, Health and Life Sciences (FHMLS) at QUB. He obtained his PhD under Prof. Ricardo Machado (University of Minho, Portugal) and Prof. Jose Mendes (University of Aveiro, Portugal) supervision at University of Minho, Portugal on developing complex network models to study software usage alignment with the project requirements [1.11]. Previously and during his PhD he worked as Information Systems Manager at I3S, a research institution of University of Porto (i3.sup.pt) where he deployed and managed all the IT infrastructure as well as deploying and developing software to support management and research operations.<\/p>\n"},{"title":"Highlights of accomplishments","content":"<p>He is presently collaborator of the DEMON Network as an AI researcher and actively working with International Public Health Centres [<strong>1.12<\/strong>] and AI Research Teams such as CLAIRE AI as part of the response to COVID-19 [<strong>1.13<\/strong>][<strong>1.14<\/strong>] where he is developing self-learning AI on publicly available and self-reporting data. He also has work on genetic alignment modelling [<strong>1.15<\/strong>], and on mining of socio-technical systems [<strong>1.16<\/strong>].<\/p>\n"},{"title":"Selected Publications","content":"<ul>\n<li>[<strong>1.11<\/strong>] Jos\u00e9 L.R. Sousa, Ricardo J. Machado, J.F.F. Mendes (<strong>2012<\/strong>), Modelling Organizational Information Systems Using \u201cComplex Networks\u201d Concepts, Proceedings of the 8<sup>th<\/sup> International Conference on the Quality of Information and Communications Technology (QUATIC), 365-370, IEEE Computer Society Press;<\/li>\n<li><strong>[1.12] Sousa, J.<\/strong>, Barata, J., Woerden, H. C. van & Kee, F. COVID-19 Symptoms app analysis to foresee healthcare impacts: Evidence from Northern Ireland. Applied Soft Computing, 116, 108324\u2013108324, <strong>2022<\/strong>, \u00a0<a href=\"https:\/\/doi.org\/10.1016\/j.asoc.2021.108324\">https:\/\/doi.org\/10.1016\/j.asoc.2021.108324<\/a>.<\/li>\n<li>[<strong>1.13<\/strong>] Jos\u00e9 Sousa, Jo\u00e3o Barata, Tracking the wings of covid-19 by modelling adaptability with open mobility data<strong>, <\/strong>Applied Artificial Intelligence, https:\/\/doi.org\/10.1080\/08839514.2020.1840196, Applied Artificial Intelligence Journal, 2020;<\/li>\n<li><strong>[1.14<\/strong>] Bontempi, G. et al., \u201cThe CLAIRE COVID-19 initiative: approach, experiences and recommendations\u201d, Ethics and Information Technology, 2021, <a href=\"https:\/\/doi.org\/10.1007\/s10676-020-09567-7\">https:\/\/doi.org\/10.1007\/s10676-020-09567-7<\/a>.<\/li>\n<li>[<strong>1.15<\/strong>] AC Roddy, A Jurek, J Sousa, A Gilmore, PG O\u2019Reilly, A Stupnikov, D Gonzalez DeCastro, KM Prise, M Salto-Tellez, DG McArt, NUQA: Estimating cancer spatial and temporal heterogeneity and evolution through alignment-free methods, Molecular Biology and Evolution Journal, 2019;<\/li>\n<li>[<strong>1.16<\/strong>] Jos\u00e9 Sousa, Jo\u00e3o Barata, \u201cMining Sociotechnical Patterns of Enterprise Systems with Complex Networks: A Guiding Framework\u201d chapter in Pa\u0144kowska, M. (2021).\u00a0<em>Autopoiesis and Self-Sustaining Processes for Organizational Success<\/em>. IGI Global. <a href=\"http:\/\/doi:10.4018\/978-1-7998-6713-5\">http:\/\/doi:10.4018\/978-1-7998-6713-5<\/a>(<a href=\"https:\/\/www.igi-global.com\/book\/autopoiesis-self-sustaining-processes-organizational\/256641\">https:\/\/www.igi-global.com\/book\/autopoiesis-self-sustaining-processes-organizational\/256641<\/a>)<\/li>\n<\/ul>\n"}],"email":"","position_with_team":{"text_before_link":"Research Team Leader of","link_text":"Computational Intelligence\u200b","text_after_link":"","link":"https:\/\/sano.science\/research-teams\/computational-intelligence\/"},"publications":[{"ID":23099,"post_author":"8","post_date":"2025-04-16 11:09:42","post_date_gmt":"2025-04-16 09:09:42","post_content":"<!-- wp:heading {\"epAnimationGeneratedClass\":\"edplus_anim-jlHxqX\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<h2 class=\"wp-block-heading eplus-wrapper\" id=\"h-ricardo-felix-nbsp-morais-nbsp-jose-maria-sousa-nbsp-cemal-nbsp-koba-nbsp-leon-nbsp-andres-tiago-jesus-nbsp-ines-nbsp-baldeiras-nbsp-tiago-gil-nbsp-oliveira-nbsp-isabel-nbsp-santana\">Ricardo F\u00e9lix Morais, Jos\u00e9 Maria Sousa,  Cemal  Koba,  Leon  Andres, Tiago Jesus,  In\u00eas  Baldeiras, Tiago Gil  Oliveira,  Isabel  Santana<\/h2>\n<!-- \/wp:heading -->\n\n<!-- wp:spacer {\"height\":\"50px\",\"epAnimationGeneratedClass\":\"edplus_anim-GVGpom\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<div style=\"height:50px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n<!-- \/wp:spacer -->\n\n<!-- wp:paragraph {\"epAnimationGeneratedClass\":\"edplus_anim-52WlCB\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<p class=\" eplus-wrapper\"> In the publication <strong>Differential involvement of neurotransmitter pathways in AD, bvFTD and MCI: Whole-brain MRI analysis<\/strong>, the authors explore how neurodegenerative conditions such as Alzheimer\u2019s disease (AD), mild cognitive impairment (MCI), and behavioral variant frontotemporal dementia (bvFTD) affect neurotransmitter systems differently. As these disorders grow more prevalent, understanding their neurochemical underpinnings becomes increasingly important. While structural brain changes have been widely studied, the role of neurotransmitter pathways has received less attention.<\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:paragraph {\"epAnimationGeneratedClass\":\"edplus_anim-QjyljR\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<p class=\" eplus-wrapper\">This study analyzed MRI data from 214 participants (89 AD, 74 bvFTD, 51 MCI) using FreeSurfer segmentation and JuSpace neurotransmitter maps. Volumetric and correlation analyses identified disease-specific patterns: AD-related atrophy was linked to dopaminergic and GABAergic systems; bvFTD showed associations with mu-opioid receptor loss; MCI exhibited early serotonergic disruptions before structural changes were evident.<\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:paragraph {\"epAnimationGeneratedClass\":\"edplus_anim-52WlCB\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<p class=\" eplus-wrapper\">The findings reveal distinct neurochemical profiles for each condition, suggesting that neurotransmitter mapping may provide valuable insights for diagnosis and therapeutic strategies beyond conventional imaging approaches.<\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:spacer {\"height\":\"50px\",\"epAnimationGeneratedClass\":\"edplus_anim-GVGpom\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<div style=\"height:50px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n<!-- \/wp:spacer -->\n\n<!-- wp:paragraph {\"epAnimationGeneratedClass\":\"edplus_anim-30HfSq\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<p class=\" eplus-wrapper\"><strong><strong>Autors<\/strong>:<\/strong> Ricardo F\u00e9lix\u00a0Morais,\u00a0<a href=\"https:\/\/sano.science\/people\/jose-sousa\/\">Jos\u00e9 Maria Sousa<\/a>,\u00a0 <a href=\"https:\/\/sano.science\/people\/cemal-koba\/\">Cemal\u00a0 Koba<\/a>,\u00a0 Leon\u00a0 Andres, Tiago Jesus, In\u00eas\u00a0 Baldeiras,\u00a0Tiago Gil\u00a0 Oliveira,\u00a0 Isabel \u00a0Santana<\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:paragraph {\"epAnimationGeneratedClass\":\"edplus_anim-30HfSq\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<p class=\" eplus-wrapper\"><strong>DOI<\/strong>: 10.1016\/j.nbd.2025.106897<\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:paragraph {\"epAnimationGeneratedClass\":\"edplus_anim-u0oM2M\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<p class=\" eplus-wrapper\"><strong>Keywords<\/strong>: JuSpace, Brain volumetry, Receptor density, Neuroimaging analysis, Neurotransmitter mapping, Dementia biomarkers<\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:spacer {\"height\":\"50px\",\"epAnimationGeneratedClass\":\"edplus_anim-GVGpom\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<div style=\"height:50px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n<!-- \/wp:spacer -->\n\n<!-- wp:buttons {\"epAnimationGeneratedClass\":\"edplus_anim-nX30O1\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<div class=\"wp-block-buttons eplus-wrapper\"><!-- wp:button 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-->\n<div class=\"wp-block-button eplus-wrapper\"><a class=\"wp-block-button__link wp-element-button\"><\/a><\/div>\n<!-- \/wp:button --><\/div>\n<!-- \/wp:buttons -->\n\n<!-- wp:acf\/button {\"id\":\"block_67ff7295ff727\",\"name\":\"acf\/button\",\"data\":{\"title\":\"READ HERE\",\"_title\":\"field_61d40397c2f0a\",\"button_type\":\"link\",\"_button_type\":\"field_63bbde3b8f0d0\",\"url\":\"https:\/\/www.sciencedirect.com\/science\/article\/pii\/S0969996125001135?via%3Dihub#ks0005\",\"_url\":\"field_61d4039bc2f0b\",\"button_style\":\"primary\",\"_button_style\":\"field_63872d045d0f0\",\"target\":\"_blank\",\"_target\":\"field_63872c705d0ef\",\"button_extra_classes\":\"\",\"_button_extra_classes\":\"field_642beab6a97de\"},\"align\":\"\",\"mode\":\"edit\"} \/-->","post_title":"Differential involvement of neurotransmitter pathways in AD, bvFTD and MCI: Whole-brain MRI analysis","post_excerpt":"Article in journal: Neurobiology of Disease, 2025","post_status":"publish","comment_status":"closed","ping_status":"closed","post_password":"","post_name":"differential-involvement-of-neurotransmitter-pathways-in-ad-bvftd-and-mci-whole-brain-mri-analysis","to_ping":"","pinged":"","post_modified":"2025-05-08 11:23:08","post_modified_gmt":"2025-05-08 09:23:08","post_content_filtered":"","post_parent":0,"guid":"https:\/\/sano.science\/?post_type=research&p=23099","menu_order":0,"post_type":"research","post_mime_type":"","comment_count":"0","filter":"raw"},{"ID":14895,"post_author":"5","post_date":"2024-01-15 13:32:31","post_date_gmt":"2024-01-15 12:32:31","post_content":"<!-- wp:heading {\"epAnimationGeneratedClass\":\"edplus_anim-TfGSL8\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<h2 class=\"wp-block-heading eplus-wrapper\">Ana Teresa Pinto, Ana Beatriz Machado, Hugo Os\u00f3rio, Marta Laranjeiro Pinto, Rui Vitorino, Gon\u00e7alo Justino, C\u00e1tia Santa, Fl\u00e1via Castro, T\u00e2nia Cruz, Carla Rodrigues, Jorge Lima, Jos\u00e9 Lu\u00eds R Sousa, Ana Patr\u00edcia Cardoso, Rita Figueira, Armanda Monteiro, Margarida Marques, Bruno Manadas, Jarne Pauwels, Kris Gevaert, Marc Mareel, S\u00f3nia Rocha, Tiago Duarte, Maria Jos\u00e9 Oliveira<\/h2>\n<!-- \/wp:heading -->\n\n<!-- wp:spacer {\"height\":\"50px\",\"epAnimationGeneratedClass\":\"edplus_anim-xsETDf\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<div style=\"height:50px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n<!-- \/wp:spacer -->\n\n<!-- wp:paragraph {\"epAnimationGeneratedClass\":\"edplus_anim-Y7Xlvc\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<p class=\" eplus-wrapper\">To identify a molecular signature of macrophages exposed to clinically relevant ionizing radiation (IR) doses, mirroring radiotherapy sessions. Methods: Human monocyte-derived macrophages were exposed to 2 Gy\/ fraction\/ day for 5 days, mimicking one week of cancer patient\u2019s radiotherapy. Protein expression profile by proteomics was performed. Results: A gene ontology analysis revealed that radiation-induced protein changes are associated with metabolic alterations, which were further supported by a reduction of both cellular ATP levels and glucose uptake. Most of the radiation-induced deregulated targets exhibited a decreased expression, as was the case of cathepsin D, a lysosomal protease associated with cell death, which was validated by Western blot. We also found that irradiated macrophages exhibited an increased expression of the transferrin receptor 1 (TfR1), which is responsible for the uptake of transferrin-bound iron. TfR1 upregulation was also found in tumor-associated mouse macrophages upon tumor irradiation. In vitro irradiated macrophages also presented a trend for increased divalent metal transporter 1 (DMT1), which transports iron from the endosome to the cytosol, and a significant increase in iron release. Conclusions: Irradiated macrophages present lower ATP levels and glucose uptake, and exhibit decreased cathepsin D expression, while increasing TfR1 expression and altering iron metabolism.<\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:spacer {\"height\":\"50px\",\"epAnimationGeneratedClass\":\"edplus_anim-xsETDf\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<div style=\"height:50px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n<!-- \/wp:spacer -->\n\n<!-- wp:acf\/button {\"id\":\"block_65a525d09607f\",\"name\":\"acf\/button\",\"data\":{\"title\":\"READ HERE\",\"_title\":\"field_61d40397c2f0a\",\"button_type\":\"link\",\"_button_type\":\"field_63bbde3b8f0d0\",\"url\":\"https:\/\/www.mdpi.com\/2072-6694\/15\/1\/270\",\"_url\":\"field_61d4039bc2f0b\",\"button_style\":\"primary\",\"_button_style\":\"field_63872d045d0f0\",\"target\":\"_blank\",\"_target\":\"field_63872c705d0ef\",\"button_extra_classes\":\"\",\"_button_extra_classes\":\"field_642beab6a97de\"},\"align\":\"\",\"mode\":\"edit\"} \/-->","post_title":"Macrophage Resistance to Ionizing Radiation Exposure Is Accompanied by Decreased Cathepsin D and Increased Transferrin Receptor 1 Expression","post_excerpt":"In: Cancers, 2022.","post_status":"publish","comment_status":"closed","ping_status":"closed","post_password":"","post_name":"macrophage-resistance-to-ionizing-radiation-exposure-is-accompanied-by-decreased-cathepsin-d-and-increased-transferrin-receptor-1-expression","to_ping":"","pinged":"","post_modified":"2024-01-15 13:32:32","post_modified_gmt":"2024-01-15 12:32:32","post_content_filtered":"","post_parent":0,"guid":"https:\/\/sano.science\/?post_type=research&p=14895","menu_order":46,"post_type":"research","post_mime_type":"","comment_count":"0","filter":"raw"},{"ID":15023,"post_author":"5","post_date":"2024-01-18 10:12:47","post_date_gmt":"2024-01-18 09:12:47","post_content":"<!-- wp:heading {\"epAnimationGeneratedClass\":\"edplus_anim-ORcpXv\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<h2 class=\"wp-block-heading eplus-wrapper\">Donald M. Lyall, Andrey Kormilitzin, Claire Lancaster, Jose Sousa, Fanny Petermann-Rocha, Christopher Buckley, Eric L. Harshfield, Matthew H. Iveson, Christopher R. Madan, R\u00edona McArdle, Danielle Newby, Vasiliki Orgeta, Eugene Tang, Stefano Tamburin, Lokendra S. Thakur, Ilianna Lourida, The Deep Dementia Phenotyping (DEMON) Network, David J. Llewellyn, Janice M. Ranson<\/h2>\n<!-- \/wp:heading -->\n\n<!-- wp:spacer {\"height\":\"50px\",\"epAnimationGeneratedClass\":\"edplus_anim-a00u1U\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<div style=\"height:50px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n<!-- \/wp:spacer -->\n\n<!-- wp:paragraph {\"epAnimationGeneratedClass\":\"edplus_anim-0Y5Ppz\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<p class=\" eplus-wrapper\">The use of applied modeling in dementia risk prediction, diagnosis, and prognostics will have substantial public health benefits, particularly as \u201cdeep phenotyping\u201d cohorts with multi-omics health data become available.<\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:spacer {\"height\":\"50px\",\"epAnimationGeneratedClass\":\"edplus_anim-a00u1U\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<div style=\"height:50px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n<!-- \/wp:spacer -->\n\n<!-- wp:acf\/button {\"id\":\"block_65a8eb7afc850\",\"name\":\"acf\/button\",\"data\":{\"title\":\"READ HERE\",\"_title\":\"field_61d40397c2f0a\",\"button_type\":\"link\",\"_button_type\":\"field_63bbde3b8f0d0\",\"url\":\"https:\/\/alz-journals.onlinelibrary.wiley.com\/doi\/10.1002\/alz.13391\",\"_url\":\"field_61d4039bc2f0b\",\"button_style\":\"primary\",\"_button_style\":\"field_63872d045d0f0\",\"target\":\"_blank\",\"_target\":\"field_63872c705d0ef\",\"button_extra_classes\":\"\",\"_button_extra_classes\":\"field_642beab6a97de\"},\"align\":\"\",\"mode\":\"edit\"} \/-->","post_title":"Artificial intelligence for dementia \u2013 Applied models and digital health","post_excerpt":"In: Alzheimer's & Dementia: Journal of the Alzheimer's Association, 2023.","post_status":"publish","comment_status":"closed","ping_status":"closed","post_password":"","post_name":"artificial-intelligence-for-dementia-applied-models-and-digital-health-2","to_ping":"","pinged":"","post_modified":"2024-01-26 15:21:02","post_modified_gmt":"2024-01-26 14:21:02","post_content_filtered":"","post_parent":0,"guid":"https:\/\/sano.science\/?post_type=research&p=15023","menu_order":2,"post_type":"research","post_mime_type":"","comment_count":"0","filter":"raw"},{"ID":12905,"post_author":"5","post_date":"2023-07-25 22:09:27","post_date_gmt":"2023-07-25 20:09:27","post_content":"<!-- wp:heading {\"epAnimationGeneratedClass\":\"edplus_anim-IxLYNe\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<h2 class=\"wp-block-heading eplus-wrapper\"><strong>Alfredo Ibias, Varun Ravi Varma, Karol Capa\u0142a, Luca Gherardini, Jose Sousa<\/strong> <\/h2>\n<!-- \/wp:heading -->\n\n<!-- wp:spacer {\"height\":\"50px\",\"epAnimationGeneratedClass\":\"edplus_anim-G8C2CJ\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<div style=\"height:50px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n<!-- \/wp:spacer -->\n\n<!-- wp:paragraph {\"epAnimationGeneratedClass\":\"edplus_anim-D5g7jz\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<p class=\" eplus-wrapper\">In personalized health, small datasets with missing data are quite common. Current Machine Learning methods are unable to process such datasets in a meaningful way due to the huge data volume requirement. To address this problem, we propose a new Small and iNcomplete Dataset Analyser (SaNDA) to process such datasets in a meaningful way. Due to the characteristics of these datasets and the criticality of the domain, an explainable method is mandatory to facilitate decision-making interpretation. Thus, SaNDA prioritises explainability over efficiency by design. We evaluated our proposal against Random Forest as a baseline for explainable methods, and against gcForest as state-of-the-art for small datasets. We observed that our proposal outperforms Random Forest when there is more missing data and\/or a lower number of entries in the dataset, obtaining less favourable results over larger, well-curated datasets. It is also preferable than gcForest due to its explainability and privacy protection capabilities. Given the difficulties in obtaining complete, reliable data in the healthcare field, we consider that our proposal could be useful for practitioners.<\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:spacer {\"height\":\"50px\",\"epAnimationGeneratedClass\":\"edplus_anim-Zsu9XH\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<div style=\"height:50px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n<!-- \/wp:spacer -->\n\n<!-- wp:acf\/button {\"id\":\"block_64ca5efe15ebe\",\"name\":\"acf\/button\",\"data\":{\"title\":\"READ HERE\",\"_title\":\"field_61d40397c2f0a\",\"button_type\":\"link\",\"_button_type\":\"field_63bbde3b8f0d0\",\"url\":\"https:\/\/www.sciencedirect.com\/science\/article\/pii\/S0020025523006631\",\"_url\":\"field_61d4039bc2f0b\",\"button_style\":\"primary\",\"_button_style\":\"field_63872d045d0f0\",\"target\":\"_blank\",\"_target\":\"field_63872c705d0ef\",\"button_extra_classes\":\"\",\"_button_extra_classes\":\"field_642beab6a97de\"},\"align\":\"\",\"mode\":\"edit\"} \/-->","post_title":"SaNDA: A small and iNcomplete dataset analyser","post_excerpt":"In: Information Sciences, 2023","post_status":"publish","comment_status":"closed","ping_status":"closed","post_password":"","post_name":"sanda-a-small-and-incomplete-dataset-analyser","to_ping":"","pinged":"","post_modified":"2024-01-05 13:39:25","post_modified_gmt":"2024-01-05 12:39:25","post_content_filtered":"","post_parent":0,"guid":"https:\/\/sano.science\/?post_type=research&p=12905","menu_order":6,"post_type":"research","post_mime_type":"","comment_count":"0","filter":"raw"},{"ID":12525,"post_author":"5","post_date":"2023-07-12 12:16:53","post_date_gmt":"2023-07-12 10:16:53","post_content":"<!-- wp:heading {\"epAnimationGeneratedClass\":\"edplus_anim-kFdseI\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<h2 class=\"wp-block-heading eplus-wrapper\">Bacciu, Davide; Girardi, Emanuela; Maratea, Marco; Sousa, Jose<\/h2>\n<!-- \/wp:heading -->\n\n<!-- wp:spacer {\"height\":\"50px\",\"epAnimationGeneratedClass\":\"edplus_anim-OBLAyv\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<div style=\"height:50px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n<!-- \/wp:spacer -->\n\n<!-- wp:paragraph {\"epAnimationGeneratedClass\":\"edplus_anim-elzUtU\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<p class=\" eplus-wrapper\">The COVID-19 pandemic has influenced our lives significantly since March 2020, and a number of initiatives have been put forward in order to tackle its effects, including those focused on technological solutions. In this paper, we present one of such initiatives, i.e. the CLAIRE\u2019s taskforce on AI and COVID-19, in which Artificial Intelligence methodologies and tools are being developed to help the society contrasting the pandemic. We present the different lines of development within the taskforce, some fields in which they are used, and draw few recommendations.<\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:spacer {\"height\":\"50px\",\"epAnimationGeneratedClass\":\"edplus_anim-OBLAyv\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<div style=\"height:50px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n<!-- \/wp:spacer -->\n\n<!-- wp:acf\/button {\"id\":\"block_659809699623f\",\"name\":\"acf\/button\",\"data\":{\"title\":\"READ HERE\",\"_title\":\"field_61d40397c2f0a\",\"button_type\":\"link\",\"_button_type\":\"field_63bbde3b8f0d0\",\"url\":\"https:\/\/content.iospress.com\/download\/intelligenza-artificiale\/ia210121?id=intelligenza-artificiale%2Fia210121\",\"_url\":\"field_61d4039bc2f0b\",\"button_style\":\"primary\",\"_button_style\":\"field_63872d045d0f0\",\"target\":\"_blank\",\"_target\":\"field_63872c705d0ef\",\"button_extra_classes\":\"\",\"_button_extra_classes\":\"field_642beab6a97de\"},\"align\":\"\",\"mode\":\"edit\"} \/-->","post_title":"AI & COVID-19\u00a0","post_excerpt":"In: Intelligenza Artificiale, 2022.","post_status":"publish","comment_status":"closed","ping_status":"closed","post_password":"","post_name":"ai-covid-19","to_ping":"","pinged":"","post_modified":"2024-01-05 14:52:23","post_modified_gmt":"2024-01-05 13:52:23","post_content_filtered":"","post_parent":0,"guid":"https:\/\/new.sano.science\/?post_type=research&p=12525","menu_order":42,"post_type":"research","post_mime_type":"","comment_count":"0","filter":"raw"},{"ID":12522,"post_author":"5","post_date":"2023-07-12 12:14:54","post_date_gmt":"2023-07-12 10:14:54","post_content":"<!-- wp:heading {\"epAnimationGeneratedClass\":\"edplus_anim-wk3dOG\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<h2 class=\"wp-block-heading eplus-wrapper\">Sousa, Jos\u00e9; Jo\u00e3oBarata; van Woerdend, Hugo C; Kee, Frank<\/h2>\n<!-- \/wp:heading -->\n\n<!-- wp:spacer {\"height\":\"50px\",\"epAnimationGeneratedClass\":\"edplus_anim-hXJWQZ\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<div style=\"height:50px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n<!-- \/wp:spacer -->\n\n<!-- wp:paragraph {\"epAnimationGeneratedClass\":\"edplus_anim-Tczseu\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<p class=\" eplus-wrapper\">Mobile health (mHealth) technologies, such as symptom tracking apps, are crucial for coping with the global pandemic crisis by providing near real-time, in situ information for the medical and governmental response. However, in such a dynamic and diverse environment, methods are still needed to support public health decision-making. This paper uses the lens of strong structuration theory to investigate networks of COVID-19 symptoms in the Belfast metropolitan area. A self-supervised machine learning method measuring information entropy was applied to the Northern Ireland COVIDCare app. The findings reveal: (1) relevant stratifications of disease symptoms, (2) particularities in health-wealth networks, and (3) the predictive potential of artificial intelligence to extract entangled knowledge from data in COVID-related apps. The proposed method proved to be effective for near real-time in-situ analysis of COVID-19 progression and to focus and complement public health decisions. Our contribution is relevant to an understanding of SARS-COV-2 symptom entanglements in localised environments. It can assist decision-makers in designing both reactive and proactive health measures that should be personalised to the heterogeneous needs of different populations. Moreover, near real-time assessment of pandemic symptoms using digital technologies will be critical to create early warning systems of emerging SARS-CoV-2 strains and predict the need for healthcare resources.<\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:spacer {\"height\":\"50px\",\"epAnimationGeneratedClass\":\"edplus_anim-2jQu15\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<div style=\"height:50px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n<!-- \/wp:spacer -->\n\n<!-- wp:acf\/button {\"id\":\"block_64ae7cf6ec99c\",\"name\":\"acf\/button\",\"data\":{\"title\":\"READ HERE\",\"_title\":\"field_61d40397c2f0a\",\"button_type\":\"link\",\"_button_type\":\"field_63bbde3b8f0d0\",\"url\":\"https:\/\/www.sciencedirect.com\/science\/article\/pii\/S1568494621011169?via%3Dihub\",\"_url\":\"field_61d4039bc2f0b\",\"button_style\":\"primary\",\"_button_style\":\"field_63872d045d0f0\",\"target\":\"_blank\",\"_target\":\"field_63872c705d0ef\",\"button_extra_classes\":\"\",\"_button_extra_classes\":\"field_642beab6a97de\"},\"align\":\"\",\"mode\":\"edit\"} \/-->","post_title":"COVID-19 Symptoms App Analysis to Foresee Healthcare Impacts: Evidence from Northern Ireland\u00a0","post_excerpt":"In: Applied Soft Computing, 2022.","post_status":"publish","comment_status":"closed","ping_status":"closed","post_password":"","post_name":"covid-19-symptoms-app-analysis-to-foresee-healthcare-impacts-evidence-from-northern-ireland","to_ping":"","pinged":"","post_modified":"2024-01-05 14:50:15","post_modified_gmt":"2024-01-05 13:50:15","post_content_filtered":"","post_parent":0,"guid":"https:\/\/new.sano.science\/?post_type=research&p=12522","menu_order":43,"post_type":"research","post_mime_type":"","comment_count":"0","filter":"raw"}]},"_links":{"self":[{"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/people\/545","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/people"}],"about":[{"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/types\/people"}],"version-history":[{"count":12,"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/people\/545\/revisions"}],"predecessor-version":[{"id":25092,"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/people\/545\/revisions\/25092"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/media\/18409"}],"wp:attachment":[{"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/media?parent=545"}],"wp:term":[{"taxonomy":"people_teams","embeddable":true,"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/people_teams?post=545"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}