Search

Your search keyword '"Vellido, Alfredo"' showing total 477 results
Start Over Author "Vellido, Alfredo"

Refine your results

477 results on '"Vellido, Alfredo"'

4. Exploring Data Distributions in Machine Learning Models with SOMs

Author

König, Caroline, Vellido, Alfredo, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Villmann, Thomas, editor, Kaden, Marika, editor, Geweniger, Tina, editor, and Schleif, Frank-Michael, editor

Published
2024
Full Text
View/download PDF

5. Long Short-Term Memory to predict 3D Amino acids Positions in GPCR Molecular Dynamics

Author

López-Correa, Juan Manuel, König, Caroline, and Vellido, Alfredo

Subjects
Quantitative Biology - Biomolecules, Computer Science - Artificial Intelligence, Computer Science - Machine Learning
Abstract

G-Protein Coupled Receptors (GPCRs) are a big family of eukaryotic cell transmembrane proteins, responsible for numerous biological processes. From a practical viewpoint around 34\% of the drugs approved by the US Food and Drug Administration target these receptors. They can be analyzed from their simulated molecular dynamics, including the prediction of their behavior in the presence of drugs. In this paper, the capability of Long Short-Term Memory Networks (LSTMs) are evaluated to learn and predict the molecular dynamic trajectories of a receptor. Several models were trained with the 3D position of the amino acids of the receptor considering different transformations on the position of the amino acid, such as their centers of mass, the geometric centers and the position of the $\alpha$--carbon for each amino acid. The error of the prediction of the position was evaluated by the mean average error (MAE) and root-mean-square deviation (RMSD). The LSTM models show a robust performance, with results comparable to the state-of-the-art in non-dynamic 3D predictions. The best MAE and RMSD values were found for the mass center of the amino acids with 0.078 {\AA} and 0.156 {\AA} respectively. This work shows the potential of LSTM to predict the molecular dynamics of GPRCs., Comment: G-Protein Coupled Receptors, LSTM, Molecular Dynamics

Published
2022

7. Recognition of Conformational States of a G Protein-Coupled Receptor from Molecular Dynamic Simulations Using Sampling Techniques

Author

Gutiérrez-Mondragón, Mario Alberto, König, Caroline, Vellido, Alfredo, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Rojas, Ignacio, editor, Valenzuela, Olga, editor, Rojas Ruiz, Fernando, editor, Herrera, Luis Javier, editor, and Ortuño, Francisco, editor

Published
2023
Full Text
View/download PDF

8. Radiomics-Based Assessment of OCT Angiography Images for Diabetic Retinopathy Diagnosis

Author

Carrera-Escalé, Laura, Benali, Anass, Rathert, Ann-Christin, Martín-Pinardel, Ruben, Bernal-Morales, Carolina, Alé-Chilet, Anibal, Barraso, Marina, Marín-Martinez, Sara, Feu-Basilio, Silvia, Rosinés-Fonoll, Josep, Hernandez, Teresa, Vilá, Irene, Castro-Dominguez, Rafael, Oliva, Cristian, Vinagre, Irene, Ortega, Emilio, Gimenez, Marga, Vellido, Alfredo, Romero, Enrique, and Zarranz-Ventura, Javier

Published
2023
Full Text
View/download PDF

9. Contributors

Author

Banerjee, Indranil, primary, Basu, Bikramjit, additional, Bowman, Anthony D., additional, Buddhiraju, Hima Sree, additional, Carbonell-Caballero, José, additional, Chandy, Thomas, additional, Clark, Ashley, additional, Das, Bodhisatwa, additional, Eranki, Avinash, additional, Gaikwad, Hanmant K., additional, Goswami, Rohan M., additional, Gowtham, N.H., additional, Gozzi, Noemi, additional, Hickerson, Darren H.M., additional, Hueso, Miguel, additional, Hunsberger, Joshua, additional, Jaswandkar, Sharad V., additional, Jololian, Leon, additional, Kanagaraj, Subramani, additional, Katti, Dinesh R., additional, Katti, Kalpana S., additional, Kommeri, Remya, additional, Kumar, Ravinder, additional, Mech, Dhruba Jyoti, additional, Montagud, Arnau, additional, Morrison, Nancy, additional, Mukherjee, Anwesha, additional, Murugappan, Sivasubramanian, additional, Pal, Kunal, additional, Paul, Willi, additional, Pemmada, Rakesh, additional, Pradhan, Bikash K., additional, Ramasubramanian, Krishna Vyas, additional, Ramasubramanian, Krishnamurthy, additional, Rengan, Aravind Kumar, additional, Rizvi, Mohd Suhail, additional, Saha, Sahely, additional, Sahani, Ashish, additional, Sarker, Sumit, additional, Sarmah, Arnab, additional, Sharma, Chandra P., additional, Srinivasan, Mirra, additional, Thangaraj, Padma Pradha, additional, Thangaraj, Santhosh Raja, additional, Thomas, Vinoy, additional, Álvarez, Rafael, additional, Unal, Aynur, additional, Valencia, Alfonso, additional, Valle, Giacomo, additional, Vellido, Alfredo, additional, and Xia, Yiyun, additional

Published
2023
Full Text
View/download PDF

12. Visual Insights from the Latent Space of Generative Models for Molecular Design

Author

Cavallaro, Salvatore, Vellido, Alfredo, König, Caroline, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Faigl, Jan, editor, Olteanu, Madalina, editor, and Drchal, Jan, editor

Published
2022
Full Text
View/download PDF

13. Integrated Science 2050: Multidisciplinarity and Interdisciplinarity in Health

Author

Rezaei, Nima, Saghazadeh, Amene, Ghani, Abdul Rahman Izaini, Vedadhir, AbouAli, Vahed, Aida, Vellido, Alfredo, Afshar, Alireza, Zali, Alireza, Kushniruk, Andre, Blacutt, Andrée-Anne, Pennisi, Antonino, Condino-Neto, Antonio, Khojasteh, Arash, Soto-Rojas, Armando E., Brown, Brian, Velasques, Bruna, Lucchiari, Claudio, Atilano-Barbosa, Daniel, Aprígio, Danielle, Kirsch, Donald R., Chiricò, Donata, Rayzan, Elham, Karakoc-Aydiner, Elif, Borycki, Elizabeth, Palmerini, Emilio Maria, Balogh, Esther A., Minutoli, Fabio, Ghobadinezhad, Farbod, Farrokhi, Farid, Reza, Faruque, Young, Gerald, Sierpiński, Grzegorz, Sajadi, Haniye Sadat, Ochs, Hans D., Murtomaa, Heikki, Monkman, Helen, Mojtabavi, Helia, Tonelli, Hélio A., Ziaei, Heliya, Sakly, Houneida, Jho, Hunkoog, Celiński, Ireneusz, Abdullah, Jafri Malin, Šrol, Jakub, Seekins, Jayne, Ravetz, Joe, Periñán, Juan José Garrido, Bittencourt, Juliana, Sarkar, Kaushik, Saleki, Kiarash, de Siqueira Rotenberg, Luisa, Jamee, Mahnaz, Keshavarz-Fathi, Mahsa, Gongora, Mariana, Cagy, Mauricio, Akhlaghdoust, Meisam, Lotfi, Melika, Baziar, Milad, Rafiaei, Milad, Amin Khazeei Tabari, Mohammad, Khami, Mohammad R., Golabchi, Mohammad Rasoul, Fadavipour, Mohammadreza, Tagina, Moncef, Lakhanpaul, Monica, Folayan, Morenike Oluwatoyin, Shamsizadeh, Morteza, Said, Mourad, Rambod Rad, Niloofar, Yazdanpanah, Niloufar, Samieefar, Noosha, Ribeiro, Pedro, Phantumvanit, Prathip, Parikh, Priti, van de Pas, Remco, Majdzadeh, Reza, Laudicella, Riccardo, Stein, Richard A., Mercadillo, Roberto E., Kelishadi, Roya, Behrouzieh, Sadra, Afkham, Saina Adiban, Momtazmanesh, Sara, Hosseini, Sayedeh Azimeh, Baldari, Sergio, Teixeira, Silmar, Seyedpour, Simin, Roche, Stéphane, Kekeghe, Stephen E., Feldman, Steven R., Fernandes, Thayaná, Ulrichs, Timo, Dorigo, Tommaso, Roudenok, Vasili, Iyer, Veeraraghavan J., Emmerich, Veronica K., Marinho, Victor, Čavojová, Vladimíra, Al-Herz, Waleed, Pirkoohi, Zahra Rahimi, Zakaria, Zaitun, Idris, Zamzuri, Izadi, Zhila, and Rezaei, Nima, Editor-in-Chief

Published
2022
Full Text
View/download PDF

15. A Deep Learning-Based Method for Uncovering GPCR Ligand-Induced Conformational States Using Interpretability Techniques

Author

Gutiérrez-Mondragón, Mario A., König, Caroline, Vellido, Alfredo, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Rojas, Ignacio, editor, Valenzuela, Olga, editor, Rojas, Fernando, editor, Herrera, Luis Javier, editor, and Ortuño, Francisco, editor

Published
2022
Full Text
View/download PDF

16. NMF for Quality Control of Multi-modal Retinal Images for Diagnosis of Diabetes Mellitus and Diabetic Retinopathy

Author

Benali, Anass, Carrera, Laura, Christin, Ann, Martín, Ruben, Alé, Anibal, Barraso, Marina, Bernal, Carolina, Marín, Sara, Feu, Silvia, Rosinés, Josep, Hernandez, Teresa, Vilá, Irene, Oliva, Cristian, Vinagre, Irene, Ortega, Emilio, Gimenez, Marga, Esmatjes, Enric, Zarranz-Ventura, Javier, Romero, Enrique, Vellido, Alfredo, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Rojas, Ignacio, editor, Valenzuela, Olga, editor, Rojas, Fernando, editor, Herrera, Luis Javier, editor, and Ortuño, Francisco, editor

Published
2022
Full Text
View/download PDF

17. Visual Mining of Industrial Gas Turbines Sensor Data as an Industry 4.0 Application

Author

Astudillo Aguilar, Angel X., Rosso, Stefano, Gibert, Karina, Vellido, Alfredo, Kacprzyk, Janusz, Series Editor, Pal, Nikhil R., Advisory Editor, Bello Perez, Rafael, Advisory Editor, Corchado, Emilio S., Advisory Editor, Hagras, Hani, Advisory Editor, Kóczy, László T., Advisory Editor, Kreinovich, Vladik, Advisory Editor, Lin, Chin-Teng, Advisory Editor, Lu, Jie, Advisory Editor, Melin, Patricia, Advisory Editor, Nedjah, Nadia, Advisory Editor, Nguyen, Ngoc Thanh, Advisory Editor, Wang, Jun, Advisory Editor, Sanjurjo González, Hugo, editor, Pastor López, Iker, editor, García Bringas, Pablo, editor, Quintián, Héctor, editor, and Corchado, Emilio, editor

Published
2022
Full Text
View/download PDF

19. Bioinformatics and Medicine in the Era of Deep Learning

Author

Bacciu, Davide, Lisboa, Paulo J. G., Martín, José D., Stoean, Ruxandra, and Vellido, Alfredo

Subjects
Computer Science - Learning, Quantitative Biology - Quantitative Methods, Statistics - Machine Learning
Abstract

Many of the current scientific advances in the life sciences have their origin in the intensive use of data for knowledge discovery. In no area this is so clear as in bioinformatics, led by technological breakthroughs in data acquisition technologies. It has been argued that bioinformatics could quickly become the field of research generating the largest data repositories, beating other data-intensive areas such as high-energy physics or astroinformatics. Over the last decade, deep learning has become a disruptive advance in machine learning, giving new live to the long-standing connectionist paradigm in artificial intelligence. Deep learning methods are ideally suited to large-scale data and, therefore, they should be ideally suited to knowledge discovery in bioinformatics and biomedicine at large. In this brief paper, we review key aspects of the application of deep learning in bioinformatics and medicine, drawing from the themes covered by the contributions to an ESANN 2018 special session devoted to this topic.

Published
2018

21. Network Community Cluster-Based Analysis for the Identification of Potential Leukemia Drug Targets

Author

Bazaga, Adrián, Vellido, Alfredo, Kacprzyk, Janusz, Series Editor, Pal, Nikhil R., Advisory Editor, Bello Perez, Rafael, Advisory Editor, Corchado, Emilio S., Advisory Editor, Hagras, Hani, Advisory Editor, Kóczy, László T., Advisory Editor, Kreinovich, Vladik, Advisory Editor, Lin, Chin-Teng, Advisory Editor, Lu, Jie, Advisory Editor, Melin, Patricia, Advisory Editor, Nedjah, Nadia, Advisory Editor, Nguyen, Ngoc Thanh, Advisory Editor, Wang, Jun, Advisory Editor, Vellido, Alfredo, editor, Gibert, Karina, editor, Angulo, Cecilio, editor, and Martín Guerrero, José David, editor

Published
2020
Full Text
View/download PDF

24. Introduction

Author

Bacciu, Davide, primary, Lisboa, Paulo J. G., additional, and Vellido, Alfredo, additional

Published
2022
Full Text
View/download PDF

28. NMF for Quality Control of Multi-modal Retinal Images for Diagnosis of Diabetes Mellitus and Diabetic Retinopathy

Author

Benali, Anass, primary, Carrera, Laura, additional, Christin, Ann, additional, Martín, Ruben, additional, Alé, Anibal, additional, Barraso, Marina, additional, Bernal, Carolina, additional, Marín, Sara, additional, Feu, Silvia, additional, Rosinés, Josep, additional, Hernandez, Teresa, additional, Vilá, Irene, additional, Oliva, Cristian, additional, Vinagre, Irene, additional, Ortega, Emilio, additional, Gimenez, Marga, additional, Esmatjes, Enric, additional, Zarranz-Ventura, Javier, additional, Romero, Enrique, additional, and Vellido, Alfredo, additional

Published
2022
Full Text
View/download PDF

31. A Study on the Robustness and Stability of Explainable Deep Learning in an Imbalanced Setting: The Exploration of the Conformational Space of G Protein-Coupled Receptors.

Author

Gutiérrez-Mondragón, Mario A., Vellido, Alfredo, and König, Caroline

Subjects
*SPACE exploration, *DEEP learning, *MEMBRANE proteins, *MACHINE learning, *MOLECULAR dynamics
Abstract

G-protein coupled receptors (GPCRs) are transmembrane proteins that transmit signals from the extracellular environment to the inside of the cells. Their ability to adopt various conformational states, which influence their function, makes them crucial in pharmacoproteomic studies. While many drugs target specific GPCR states to exert their effects—thereby regulating the protein's activity—unraveling the activation pathway remains challenging due to the multitude of intermediate transformations occurring throughout this process, and intrinsically influencing the dynamics of the receptors. In this context, computational modeling, particularly molecular dynamics (MD) simulations, may offer valuable insights into the dynamics and energetics of GPCR transformations, especially when combined with machine learning (ML) methods and techniques for achieving model interpretability for knowledge generation. The current study builds upon previous work in which the layer relevance propagation (LRP) technique was employed to interpret the predictions in a multi-class classification problem concerning the conformational states of the β 2 -adrenergic ( β 2 AR) receptor from MD simulations. Here, we address the challenges posed by class imbalance and extend previous analyses by evaluating the robustness and stability of deep learning (DL)-based predictions under different imbalance mitigation techniques. By meticulously evaluating explainability and imbalance strategies, we aim to produce reliable and robust insights. [ABSTRACT FROM AUTHOR]

Published
2024
Full Text
View/download PDF

33. Metrics for Probabilistic Geometries

Author

Tosi, Alessandra, Hauberg, Søren, Vellido, Alfredo, and Lawrence, Neil D.

Subjects
Statistics - Machine Learning, Computer Science - Learning
Abstract

We investigate the geometrical structure of probabilistic generative dimensionality reduction models using the tools of Riemannian geometry. We explicitly define a distribution over the natural metric given by the models. We provide the necessary algorithms to compute expected metric tensors where the distribution over mappings is given by a Gaussian process. We treat the corresponding latent variable model as a Riemannian manifold and we use the expectation of the metric under the Gaussian process prior to define interpolating paths and measure distance between latent points. We show how distances that respect the expected metric lead to more appropriate generation of new data., Comment: UAI 2014

Published
2014

37. Early pseudoprogression and progression lesions in glioblastoma patients are both metabolically heterogeneous.

Author

Ungan, Gülnur, Pons‐Escoda, Albert, Ulinic, Daniel, Arús, Carles, Ortega‐Martorell, Sandra, Olier, Ivan, Vellido, Alfredo, Majós, Carles, and Julià‐Sapé, Margarida

Subjects
GLIOBLASTOMA multiforme, ADJUVANT chemotherapy, TEMOZOLOMIDE, MATRIX decomposition, NONNEGATIVE matrices
Abstract

The standard treatment in glioblastoma includes maximal safe resection followed by concomitant radiotherapy plus chemotherapy and adjuvant temozolomide. The first follow‐up study to evaluate treatment response is performed 1 month after concomitant treatment, when contrast‐enhancing regions may appear that can correspond to true progression or pseudoprogression. We retrospectively evaluated 31 consecutive patients at the first follow‐up after concomitant treatment to check whether the metabolic pattern assessed with multivoxel MRS was predictive of treatment response 2 months later. We extracted the underlying metabolic patterns of the contrast‐enhancing regions with a blind‐source separation method and mapped them over the reference images. Pattern heterogeneity was calculated using entropy, and association between patterns and outcomes was measured with Cramér's V. We identified three distinct metabolic patterns—proliferative, necrotic, and responsive, which were associated with status 2 months later. Individually, 70% of the patients showed metabolically heterogeneous patterns in the contrast‐enhancing regions. Metabolic heterogeneity was not related to the regions' size and only stable patients were less heterogeneous than the rest. Contrast‐enhancing regions are also metabolically heterogeneous 1 month after concomitant treatment. This could explain the reported difficulty in finding robust pseudoprogression biomarkers. [ABSTRACT FROM AUTHOR]

Published
2024
Full Text
View/download PDF

38. Topological Sequence Segments Discriminate Between Class C GPCR Subtypes

Author

König, Caroline, Alquézar, René, Vellido, Alfredo, Giraldo, Jesús, Kacprzyk, Janusz, Series editor, Pal, Nikhil R., Advisory editor, Bello Perez, Rafael, Advisory editor, Corchado, Emilio S., Advisory editor, Hagras, Hani, Advisory editor, Kóczy, László T., Advisory editor, Kreinovich, Vladik, Advisory editor, Lin, Chin-Teng, Advisory editor, Lu, Jie, Advisory editor, Melin, Patricia, Advisory editor, Nedjah, Nadia, Advisory editor, Nguyen, Ngoc Thanh, Advisory editor, Wang, Jun, Advisory editor, Fdez-Riverola, Florentino, editor, Mohamad, Mohd Saberi, editor, Rocha, Miguel, editor, De Paz, Juan F., editor, and Pinto, Tiago, editor

Published
2017
Full Text
View/download PDF

39. Electricity Rate Planning for the Current Consumer Market Scenario Through Segmentation of Consumption Time Series

Author

Vellido, Alfredo, García, David L., Hutchison, David, Editorial Board Member, Kanade, Takeo, Editorial Board Member, Kittler, Josef, Editorial Board Member, Kleinberg, Jon M., Editorial Board Member, Mattern, Friedemann, Editorial Board Member, Mitchell, John C., Editorial Board Member, Naor, Moni, Editorial Board Member, Pandu Rangan, C., Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Terzopoulos, Demetri, Editorial Board Member, Tygar, Doug, Editorial Board Member, Weikum, Gerhard, Series Editor, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Oliveira, Eugénio, editor, Gama, João, editor, Vale, Zita, editor, and Lopes Cardoso, Henrique, editor

Published
2017
Full Text
View/download PDF

40. Machine Learning for Critical Care: An Overview and a Sepsis Case Study

Author

Vellido, Alfredo, Ribas, Vicent, Morales, Carles, Ruiz Sanmartín, Adolfo, Ruiz-Rodríguez, Juan Carlos, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Rojas, Ignacio, editor, and Ortuño, Francisco, editor

Published
2017
Full Text
View/download PDF

45. Automated Quality Control for Proton Magnetic Resonance Spectroscopy Data Using Convex Non-negative Matrix Factorization

Author

Mocioiu, Victor, Kyathanahally, Sreenath P., Arús, Carles, Vellido, Alfredo, Julià-Sapé, Margarida, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Ortuño, Francisco, editor, and Rojas, Ignacio, editor

Published
2016
Full Text
View/download PDF

46. Random Forests for Quality Control in G-Protein Coupled Receptor Databases

Author

Shkurin, Aleksei, Vellido, Alfredo, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Ortuño, Francisco, editor, and Rojas, Ignacio, editor

Published
2016
Full Text
View/download PDF

48. Advances in the Use of Deep Learning for the Analysis of Magnetic Resonance Image in Neuro-Oncology.

Author

Pitarch, Carla, Ungan, Gulnur, Julià-Sapé, Margarida, and Vellido, Alfredo

Subjects
DEEP learning, DIGITAL image processing, RESEARCH evaluation, MAGNETIC resonance imaging, MACHINE learning, ARTIFICIAL intelligence, BRAIN tumors, QUALITY control, RESEARCH funding, DATA analysis, NEURORADIOLOGY, ONCOLOGY, DIFFUSION of innovations
Abstract

Simple Summary: Within the rapidly evolving landscape of Machine Learning in the medical field, this paper focuses on the forefront advancements in neuro-oncological radiology. More specifically, it aims to provide the reader with an in-depth exploration of the latest advancements in employing Deep Learning methodologies for the classification of brain tumor radiological images. This review meticulously scrutinizes papers published from 2018 to 2023, unveiling ongoing topics of research while underscoring the main remaining challenges and potential avenues for future research identified by those studies. Beyond the review itself, the paper also underscores the importance of placing the image data modelling provided by Deep Learning techniques within the framework of analytical pipeline research. This means that data quality control and pre-processing should be correctly coupled with modelling itself, in a way that emphasizes the importance of responsible data utilization, as well as the critical need for transparency in data disclosure to ensure trustworthiness and reproducibility of findings. 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. This paper reviews in detail some of the most recent advances in the use of Deep Learning in this field, from the broader topic of the development of Machine-Learning-based analytical pipelines to specific instantiations of the use of Deep Learning in neuro-oncology; the latter including its use in the groundbreaking field of ultra-low field magnetic resonance imaging. [ABSTRACT FROM AUTHOR]

Published
2024
Full Text
View/download PDF

50. A methodology for the characterization of business-to-consumer E-commerce

Author

Vellido, Alfredo

Subjects
519.5, HF5001 Business, HF Commerce
Abstract

This thesis concerns the field of business-to-consumer electronic commerce. Research on Internet consumer behaviour is still in its infancy, and a quantitative framework to characterize user profiles for e-commerce is not yet established. This study proposes a quantitative framework that uses latent variable analysis to identify the underlying traits of Internet users' opinions. Predictive models are then built to select the factors that are most predictive of the propensity to buy on-line and classify Internet users according to that propensity. This is followed by a segmentation of the online market based on that selection of factors and the deployment of segment-specific graphical models to map the interactions between factors and between these and the propensity to buy online. The novel aspects of this work can be summarised as follows: the definition of a fully quantitative methodology for the segmentation and analysis of large data sets; the description of the latent dimensions underlying consumers' opinions using quantitative methods; the definition of a principled method of marginalisation to the empirical prior, for Bayesian neural networks, to deal with the use of class-unbalanced data sets; a study of the Generative Topographic Mapping (GTM) as a principled method for market segmentation, including some developments of the model, namely: a) an entropy-based measure to compare the class-discriminatory capabilities of maps of equal dimensions; b) a Cumulative Responsibility measure to provide information on the mapping distortion and define data clusters; c) Selective Smoothing as an extended model for the regularization of the GTM training.

Published
2000
Full Text
View/download PDF

Catalog

Books, media, physical & digital resources
See catalog results