Search Results - "Barros, Rodrigo C."

Craving for a Robust Methodology: A Systematic Review of Machine Learning Algorithms on Substance-Use Disorders Treatment Outcomes.

Authors: de Mattos, Bernardo Paim Mattjie, Christian Ravazio, Rafaela et al.

Source: International Journal of Mental Health & Addiction. Apr2026, Vol. 24 Issue 2, p1090-1117. 28p.

Machine learning and big data analytics in bipolar disorder: A position paper from the International Society for Bipolar Disorders Big Data Task Force.

Authors: Passos, Ives C. Ballester, Pedro L. Barros, Rodrigo C. et al.

Source: Bipolar Disorders. Nov2019, Vol. 21 Issue 7, p582-594. 13p. 1 Diagram, 2 Charts.

Reduction strategies for hierarchical multi-label classification in protein function prediction.

Authors: Cerri, Ricardo Barros, Rodrigo C. de Carvalho, Andre C. P. L. F. et al.

Source: BMC Bioinformatics. 9/15/2016, Vol. 17, p1-24. 24p. 8 Diagrams, 13 Charts, 1 Graph.

CrowdEst: a method for estimating (and not simulating) crowd evacuation parameters in generic environments.

Authors: Testa, Estêvão Barros, Rodrigo C. Musse, Soraia Raupp

Source: Visual Computer; Jun2019, Vol. 35 Issue 6-8, p1119-1130, 12p

Inducing Hierarchical Multi-label Classification rules with Genetic Algorithms.

Authors: Cerri, Ricardo Basgalupp, Márcio P. Barros, Rodrigo C. et al.

Source: Applied Soft Computing; Apr2019, Vol. 77, p584-604, 21p

Fairness in Deep Learning: A Survey on Vision and Language Research.

Authors: Parraga, Otavio More, Martin D. Oliveira, Christian M. et al.

Source: ACM Computing Surveys. Jun2025, Vol. 57 Issue 6, p1-40. 40p.

Automatic Design of Decision-Tree Algorithms with Evolutionary Algorithms.

Authors: Barros, Rodrigo C. Basgalupp, Márcio P. deCarvalho, André C. P. L. F. et al.

Source: Evolutionary Computation; Winter2013, Vol. 21 Issue 4, p559-684, 26p

Automatic design of decision-tree induction algorithms tailored to flexible-receptor docking data.

Authors: Barros, Rodrigo C. Winck, Ana T. Machado, Karina S. et al.

Source: BMC Bioinformatics. 2012, Vol. 13 Issue 1, p310-323. 14p. 4 Diagrams, 3 Charts.

Stratification of Modifiable and Non‐Modifiable Risk Factors for Conversion to MCI in a Cognitively Unimpaired Cohort.

Authors: Arnold, Daniel Ferrari‐Souza, João Pedro Barros, Rodrigo C. et al.

Source: Alzheimer's & dementia: the journal of the Alzheimer's Association Dec2025 Supplement; Vol. 21 (S6).

Journal Info: Publisher: Wiley-Blackwell ISSN: 1552-5260

A Stratified Machine Learning Evaluation of Risk Factors of Dementia Conversion.

Authors: Arnold, Daniel Ferrari‐Souza, João Pedro Barros, Rodrigo C. et al.

Source: Alzheimer's & dementia: the journal of the Alzheimer's Association Dec2025 Supplement; Vol. 21 (S6).

Journal Info: Publisher: Wiley-Blackwell ISSN: 1552-5260

A decision tree to improve identification of pathogenic mutations in clinical practice.

Authors: do Nascimento, Priscilla Machado Medeiros, Inácio Gomes Falcão, Raul Maia et al.

Source: BMC Medical Informatics & Decision Making. 3/10/2020, Vol. 20 Issue 1, p1-11. 11p. 1 Diagram, 2 Charts.

Data Stream Clustering: A Survey.

Authors: SILVA, JONATHAN A. FARIA, ELAINE R. BARROS, RODRIGO C. et al.

Source: ACM Computing Surveys. Oct2013, Vol. 46 Issue 1, p13-13:31. 31p. 1 Color Photograph, 8 Diagrams, 5 Charts.

A machine learning approach to predict amyloid and tau positivity using clinical features.

Authors: Arnold, Daniel Borelli, Wyllians Vendramini Machado, Luiza Santos et al.

Source: Alzheimer's & dementia: the journal of the Alzheimer's Association Dec2024 Supplement; Vol. 20 (S10).

Journal Info: Publisher: Wiley-Blackwell ISSN: 1552-5260

A machine learning approach to predict tau positivity using clinical features in amyloid‐positive individuals.

Authors: Arnold, Daniel Borelli, Wyllians Vendramini Machado, Luiza Santos et al.

Source: Alzheimer's & dementia: the journal of the Alzheimer's Association Dec2024 Supplement; Vol. 20 (S10).

Journal Info: Publisher: Wiley-Blackwell ISSN: 1552-5260

Clustering Molecular Dynamics Trajectories for Optimizing Docking Experiments.

Authors: De Paris, Renata Quevedo, Christian V. Ruiz, Duncan D. et al.

Source: Computational Intelligence & Neuroscience. 3/22/2015, Vol. 2015, p1-9. 9p.

Machine learning for discovering missing or wrong protein function annotations: A comparison using updated benchmark datasets.

Authors: Nakano, Felipe Kenji Lietaert, Mathias Vens, Celine

Source: BMC Bioinformatics. 9/23/2019, Vol. 20 Issue 1, pN.PAG-N.PAG. 1p. 10 Diagrams, 13 Charts, 5 Graphs.