1.
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16 p, 1.2 MB |
The problem of institutional fit : uncovering patterns with boosted decision trees
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Epstein, Graham (University of Waterloo. School of Environment, Resources and Sustainability) ;
Apetrei, Cristina I. (Leuphana University, Faculty of Sustainability) ;
Baggio, Jacopo (University of Central Florida. School of Politics, Security and International Affairs) ;
Chawla, Sivee (James Cook University. ARC Centre of Excellence for Coral Reef Studies) ;
Cumming, Graeme S. (James Cook University. ARC Centre of Excellence for Coral Reef Studies) ;
Gurney, Georgina (James Cook University. ARC Centre of Excellence for Coral Reef Studies) ;
Morrison, Tiffany (James Cook University. ARC Centre of Excellence for Coral Reef Studies) ;
Unnikrishnan, Hita (Sheffield University. Urban Institute) ;
Villamayor Tomás, Sergio (Universitat Autònoma de Barcelona. Institut de Ciència i Tecnologia Ambientals)
Complex social-ecological contexts play an important role in shaping the types of institutions that groups use to manage resources, and the effectiveness of those institutions in achieving social and environmental objectives. [...]
2024 - 10.5334/ijc.1226
International Journal of the Commons, Vol. 18, issue 1 (2024) , p. 1-16
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2.
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Local interpretation of machine learning models in remote sensing with SHAP : the case of global climate constraints on photosynthesis phenology
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Descals, Adrià (Centre de Recerca Ecològica i d'Aplicacions Forestals) ;
Verger, Aleixandre (Centre de Recerca Ecològica i d'Aplicacions Forestals) ;
Yin, Gaofei (Southwest Jiaotong University. Faculty of Geosciences and Environmental Engineering) ;
Filella, Iolanda (Centre de Recerca Ecològica i d'Aplicacions Forestals) ;
Peñuelas, Josep (Centre de Recerca Ecològica i d'Aplicacions Forestals)
Data-driven models using machine learning have been widely used in remote-sensing applications such as the retrieval of biophysical variables and land cover classification. However, these models behave as a 'black box', meaning that the relationships between the input and predicted variables are hard to interpret. [...]
2023 - 10.1080/01431161.2023.2217982
International Journal of Remote Sensing, Vol. 44, issue 10 (2023) , p. 3160-3173
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3.
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55 p, 707.1 KB |
Advances in the Use of Deep Learning for the Analysis of Magnetic Resonance Image in Neuro-Oncology
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Pitarch, Carla (Universitat Politècnica de Catalunya. Departament d'Informàtica) ;
Ungan, Gulnur Semahat (Universitat Autònoma de Barcelona. Departament de Bioquímica i de Biologia Molecular) ;
Julià Sapé, Ma. Margarita (Universitat Autònoma de Barcelona. Institut de Biotecnologia i de Biomedicina "Vicent Villar Palasí") ;
Vellido, Alfredo (Universitat Politècnica de Catalunya. Departament d'Informàtica)
Machine Learning is entering a phase of maturity, but its medical applications still lag behind in terms of practical use. The field of oncological radiology (and neuro-oncology in particular) is at the forefront of these developments, now boosted by the success of Deep-Learning methods for the analysis of medical images. [...]
2024 - 10.3390/cancers16020300
Cancers, Vol. 16, Issue 2 (January 2024) , art. 300
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4.
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6 p, 731.5 KB |
A metabolite-based machine learning approach to diagnose Alzheimer-type dementia in blood : Results from the European Medical Information Framework for Alzheimer disease biomarker discovery cohort
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Stamate, Daniel (University of London) ;
Kim, Min (Steno Diabetes Center Copenhagen) ;
Proitsi, Petroula (King's College London) ;
Westwood, Sarah (University of Oxford) ;
Baird, Alison (University of Oxford) ;
Nevado-Holgado, Alejo (University of Oxford) ;
Hye, Abdul (King's College London) ;
Bos, Isabelle (Amsterdam UMC) ;
Vos, Stephanie J.B. (Maastricht University) ;
Vandenberghe, Rik (Amsterdam UMC) ;
Teunissen, Charlotte E. (Amsterdam UMC. University Medical Center) ;
Kate, Mara Ten (Amsterdam UMC. University Medical Center) ;
Scheltens, Philip (Vrije Universiteit Amsterdam) ;
Gabel, Silvy (Laboratory for Cognitive Neurology) ;
Meersmans, Karen (Laboratory for Cognitive Neurology) ;
Blin, Olivier (Aix-Marseille Université) ;
Richardson, Jill (GlaxoSmithKline R&D) ;
De Roeck, Ellen (University of Antwerp) ;
Engelborghs, Sebastiaan (Vrije Universiteit Brussel (VUB)) ;
Sleegers, Kristel (Center for Molecular Neurology. VIB) ;
Bordet, Régis (University of Lille) ;
Ramit, Lorena (Hospital Clínic i Provincial de Barcelona) ;
Kettunen, Petronella (Sahlgrenska Academy at University of Gothenburg) ;
Tsolaki, Magda (AHEPA University Hospital (Grècia)) ;
Verhey, Frans (Maastricht University) ;
Alcolea, Daniel (Institut d'Investigació Biomèdica Sant Pau) ;
Lleó, Alberto (Institut d'Investigació Biomèdica Sant Pau) ;
Peyratout, Gwendoline (Lausanne University Hospital) ;
Tainta, Mikel (Fundacion CITA-alzheimer Fundazioa) ;
Johannsen, Peter (Copenhagen University Hospital) ;
Freund-Levi, Yvonne (Karolinska Institutet (Estocolm, Suècia)) ;
Frölich, Lutz (University of Heidelberg) ;
Dobricic, Valerija (University of Lübeck) ;
Frisoni, Giovanni B. (IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli) ;
Molinuevo, José L. (Universitat Pompeu Fabra) ;
Wallin, Anders (Sahlgrenska Academy at the University of Gothenburg) ;
Popp, Julius (Geneva University Hospitals) ;
Martinez-Lage, Pablo (Fundación CITA-Alzhéimer Fundazioa) ;
Bertram, Lars (University of Oslo) ;
Blennow, Kaj (Sahlgrenska University Hospital (Suècia)) ;
Zetterberg, Henrik (UCL Institute of Neurology (Regne Unit)) ;
Streffer, Johannes (University of Antwerp) ;
Visser, Pieter J. (Amsterdam UMC) ;
Lovestone, Simon (Janssen-Cilag UK Ltd) ;
Legido-Quigley, Cristina (King's College London) ;
Universitat Autònoma de Barcelona
Machine learning (ML) may harbor the potential to capture the metabolic complexity in Alzheimer Disease (AD). Here we set out to test the performance of metabolites in blood to categorize AD when compared to CSF biomarkers. [...]
2019 - 10.1016/j.trci.2019.11.001
Alzheimer's & dementia, Vol. 5 (2019) , p. 933-938
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5.
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6 p, 420.6 KB |
Characteristics associated with the perception of high-impact disease (PsAID ≥4) in patients with recent-onset psoriatic arthritis. Machine learning-based model
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Queiro, Ruben (Universidad de Oviedo) ;
Seoane-Mato, Daniel (Sociedad Española de Reumatologia) ;
Laiz, Ana (Institut d'Investigació Biomèdica Sant Pau) ;
Agirregoikoa, E.G. (Hospital Universitario Basurto) ;
Montilla, C. (Hospital Universitario de Salamanca) ;
Park, Hye-Sang (Hospital de la Santa Creu i Sant Pau (Barcelona, Catalunya)) ;
Pinto-Tasende, J.A. (Complexo Hospitalario Universitario de A Coruña) ;
Bethencourt Baute, Juan José (Hospital Universitario de Canarias (La Laguna)) ;
Ibáñez, B.J. (Hospital Universitario 12 de Octubre) ;
Toniolo, E. (Hospital Universitari Son Llàtzer) ;
Ramírez, Julio (Hospital Clínic i Provincial de Barcelona) ;
García, A.S. (Universidad Autónoma de Madrid) ;
Cañete, Juan D. (Hospital Clínic i Provincial de Barcelona) ;
Juanola, Xavier (Hospital Universitari de Bellvitge) ;
Gratacós, Jordi (Parc Taulí Hospital Universitari. Institut d'Investigació i Innovació Parc Taulí (I3PT)) ;
Rodriguez Moreno, Jesús (Hospital Universitari de Bellvitge) ;
Notario, Jaime (Hospital Universitari de Bellvitge) ;
López-Ferrer, Anna (Hospital de la Santa Creu i Sant Pau (Barcelona, Catalunya)) ;
Cuervo, Andrea (Hospital Clínic i Provincial de Barcelona) ;
Alsina Gibert, Mercè (Hospital Clínic i Provincial de Barcelona) ;
Universitat Autònoma de Barcelona
To evaluate which patient and disease characteristics are associated with the perception of high-impact disease (PsAID ≥4) in recent-onset psoriatic arthritis. We performed a multicenter observational prospective study (2-year follow-up, regular annual visits). [...]
2022 - 10.1016/j.semarthrit.2022.152097
Seminars in Arthritis and Rheumatism, Vol. 57 (december 2022) , p. 152097
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6.
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22 p, 2.1 MB |
A semi-agnostic ansatz with variable structure for variational quantum algorithms
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Bilkis, Matias (Universitat Autònoma de Barcelona. Departament de Física) ;
Cerezo, Marco (Los Alamos National Laboratory. Center for Nonlinear Studies) ;
Verdon, Guillaume (University of Waterloo. Institute for Quantum Computing) ;
Coles, Patrick (Los Alamos National Laboratory. Theoretical Division) ;
Cincio, Lukasz (Los Alamos National Laboratory. Theoretical Division)
Quantum machine learning-and specifically Variational Quantum Algorithms (VQAs)-offers a powerful, flexible paradigm for programming near-term quantum computers, with applications in chemistry, metrology, materials science, data science, and mathematics. [...]
2023 - 10.1007/s42484-023-00132-1
Quantum machine intelligence, Vol. 5, Issue 2 (December 2023) , art. 43
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7.
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12 p, 2.5 MB |
Toward machine learning for microscopic mechanisms : A formula search for crystal structure stability based on atomic properties
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Gajera, Udaykumar (University of Turin. Chemistry Department) ;
Storchi, Loriano (Università Degli Studi G. d'Annunzio. Dipartimento di Farmacia) ;
Amoroso, Danila (Université de Liège. NanoMat/Q-mat/CESAM) ;
Delodovici, Francesco (Université Paris-Saclay. CentraleSupélec) ;
Picozzi, Silvia (Consiglio Nazionale Delle Ricerche)
Machine-learning techniques are revolutionizing the way to perform efficient materials modeling. We here propose a combinatorial machine-learning approach to obtain physical formulas based on simple and easily accessible ingredients, such as atomic properties. [...]
2022 - 10.1063/5.0088177
Journal of applied physics, Vol. 131, Issue 21 (June 2022) , art. 215703
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8.
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9 p, 1.5 MB |
Deep machine learning for meteor monitoring : Advances with transfer learning and gradient-weighted class activation mapping
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Peña-Asensio, Eloy (Universitat Autònoma de Barcelona. Departament de Química) ;
Trigo Rodríguez, Josep Maria (Institut de Ciències de l'Espai) ;
Grèbol-Tomàs, Pau (Institut de Ciències de l'Espai) ;
Regordosa-Avellana, David (Spanish Meteor Network) ;
Rimola, Albert (Universitat Autònoma de Barcelona. Departament de Química)
In recent decades, the use of optical detection systems for meteor studies has increased dramatically, resulting in huge amounts of data being analyzed. Automated meteor detection tools are essential for studying the continuous meteoroid incoming flux, recovering fresh meteorites, and achieving a better understanding of our Solar System. [...]
2023 - 10.1016/j.pss.2023.105802
Planetary and Space Science, Vol. 238 (November 2023) , art. 105802
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9.
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22 p, 4.1 MB |
Understanding and Predicting Cognitive Improvement of Young Adults in Ischemic Stroke Rehabilitation Therapy
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Martinez, Helard Becerra (University of College Dublin) ;
Cisek, Katryna (Technological University Dublin) ;
Garcia-Rudolph, Alejandro (Institut Germans Trias i Pujol. Fundació Lluita Contra les Infeccions) ;
Kelleher, John D. (Technological University Dublin) ;
Hines, Andrew (University of College Dublin) ;
Universitat Autònoma de Barcelona
Accurate early predictions of a patient's likely cognitive improvement as a result of a stroke rehabilitation programme can assist clinicians in assembling more effective therapeutic programs. In addition, sufficient levels of explainability, which can justify these predictions, are a crucial requirement, as reported by clinicians. [...]
2022 - 10.3389/fneur.2022.886477
Frontiers in neurology, Vol. 13 (july 2022)
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10.
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26 p, 520.5 KB |
Combining Molecular, Imaging, and Clinical Data Analysis for Predicting Cancer Prognosis
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Lobato-Delgado, Barbara (Institut d'Investigació Biomèdica Sant Pau) ;
Priego-Torres, Blanca (Instituto de Investigación e Innovación Biomédica de Cádiz) ;
Sanchez-Morillo, Daniel (Instituto de Investigación e Innovación Biomédica de Cádiz)
Cancer is one of the most detrimental diseases globally. Accordingly, the prognosis prediction of cancer patients has become a field of interest. In this review, we have gathered 43 state-of-the-art scientific papers published in the last 6 years that built cancer prognosis predictive models using multimodal data. [...]
2022 - 10.3390/cancers14133215
Cancers, Vol. 14 Núm. 13 (July 2022) , p. 3215
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