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Gonçalves RL, Pagano AS, Reis ZSN, Brackstone K, Lopes TCP, Cordeiro SA, Nunes JM, Afagbedzi SK, Head M, Meira W, Batchelor J, Ribeiro ALP. Usability of Telehealth Systems for Noncommunicable Diseases in Primary Care From the COVID-19 Pandemic Onward: Systematic Review. J Med Internet Res 2023; 25:e44209. [PMID: 36787223 PMCID: PMC10022651 DOI: 10.2196/44209] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2022] [Revised: 01/25/2023] [Accepted: 02/06/2023] [Indexed: 02/08/2023] Open
Abstract
BACKGROUND During the COVID-19 pandemic, telehealth was expanded without the opportunity to extensively evaluate the adopted technology's usability. OBJECTIVE We aimed to synthesize evidence on health professionals' perceptions regarding the usability of telehealth systems in the primary care of individuals with noncommunicable diseases (NCDs; hypertension and diabetes) from the COVID-19 pandemic onward. METHODS A systematic review was performed of clinical trials, prospective cohort studies, retrospective observational studies, and studies that used qualitative data collection and analysis methods published in English, Spanish, and Portuguese from March 2020 onward. The databases queried were MEDLINE, Embase, BIREME, IEEE Xplore, BVS, Google Scholar, and grey literature. Studies involving health professionals who used telehealth systems in primary care and managed patients with NCDs from the COVID-19 pandemic onward were considered eligible. Titles, abstracts, and full texts were reviewed. Data were extracted to provide a narrative qualitative evidence synthesis of the included articles. The risk of bias and methodological quality of the included studies were analyzed. The primary outcome was the usability of telehealth systems, while the secondary outcomes were satisfaction and the contexts in which the telehealth system was used. RESULTS We included 11 of 417 retrieved studies, which had data from 248 health care professionals. These health care professionals were mostly doctors and nurses with prior experience in telehealth in high- and middle-income countries. Overall, 9 studies (82%) were qualitative studies and 2 (18%) were quasiexperimental or multisite trial studies. Moreover, 7 studies (64%) addressed diabetes, 1 (9%) addressed diabetes and hypertension, and 3 (27%) addressed chronic diseases. Most studies used a survey to assess usability. With a moderate confidence level, we concluded that health professionals considered the usability of telehealth systems to be good and felt comfortable and satisfied. Patients felt satisfied using telehealth. The most important predictor for using digital health technologies was ease of use. The main barriers were technological challenges, connectivity issues, low computer literacy, inability to perform complete physical examination, and lack of training. Although the usability of telehealth systems was considered good, there is a need for research that investigates factors that may influence the perceptions of telehealth usability, such as differences between private and public services; differences in the level of experience of professionals, including professional experience and experience with digital tools; and differences in gender, age groups, occupations, and settings. CONCLUSIONS The COVID-19 pandemic has generated incredible demand for virtual care. Professionals' favorable perceptions of the usability of telehealth indicate that it can facilitate access to quality care. Although there are still challenges to telehealth, more than infrastructure challenges, the most reported challenges were related to empowering people for digital health. TRIAL REGISTRATION PROSPERO International Prospective Register of Systematic Reviews CRD42021296887; https://www.crd.york.ac.uk/PROSPERO/display_record.php?RecordID=296887. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID) RR2-10.21801/ppcrj.2022.82.6.
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Affiliation(s)
- Roberta Lins Gonçalves
- Hospital das Clínicas da Universidade Federal de Minas Gerais, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
- Universidade Federal do Amazonas, Faculdade de Educação Física e Fisioterapia, Manaus, Brazil
| | | | | | | | | | - Sarah Almeida Cordeiro
- Universidade Federal do Amazonas, Faculdade de Educação Física e Fisioterapia, Manaus, Brazil
| | - Julia Macedo Nunes
- Department of Linguistics, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
| | | | - Michael Head
- University of Southampton, Southampton, United Kingdom
| | - Wagner Meira
- Department of Linguistics, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
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Moreira E, Meira W, Gonçalves MA, Laender AHF. The rise of hyperprolific authors in computer science: characterization and implications. Scientometrics 2023. [DOI: 10.1007/s11192-023-04676-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/17/2023]
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3
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Viegas F, Pereira A, Cecílio P, Tuler E, Meira W, Gonçalves M, Rocha L. Semantic Academic Profiler (SAP): a framework for researcher assessment based on semantic topic modeling. Scientometrics 2022; 127:5005-5026. [PMID: 35844248 PMCID: PMC9273922 DOI: 10.1007/s11192-022-04449-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2022] [Accepted: 06/20/2022] [Indexed: 11/24/2022]
Abstract
Recent efforts have focused on identifying multidisciplinary teams and detecting co-Authorship Networks based on exploring topic modeling to identify researchers’ expertise. Though promising, none of these efforts perform a real-life evaluation of the quality of the built topics. This paper proposes a Semantic Academic Profiler (SAP) framework that allows summarizing articles written by researchers to automatically build research profiles and perform online evaluations regarding these built profiles. SAP exploits and extends state-of-the-art Topic Modeling strategies based on Cluwords considering n-grams and introduces a new visual interface able to highlight the main topics related to articles, researchers and institutions. To evaluate SAP’s capability of summarizing the profile of such entities as well as its usefulness for supporting online assessments of the topics’ quality, we perform and contrast two types of evaluation, considering an extensive repository of Brazilian curricula vitae: (1) an offline evaluation, in which we exploit a traditional metric (NPMI) to measure the quality of several data representations strategies including (i) TFIDF, (ii) TFIDF with Bi-grams, (iii) Cluwords, and (iv) CluWords with Bi-grams; and (2) an online evaluation through an A/B test where researchers evaluate their own built profiles. We also perform an online assessment of SAP user interface through a usability test following the SUS methodology. Our experiments indicate that the CluWords with Bi-grams is the best solution and the SAP interface is very useful. We also observed essential differences in the online and offline assessments, indicating that using both together is very important for a comprehensive quality evaluation. Such type of study is scarce in the literature and our findings open space for new lines of investigation in the Topic Modeling area.
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Affiliation(s)
- Felipe Viegas
- Department of Computer Science, Federal University of Minas Gerais, Belo Horizonte, Minas Gerais Brazil
| | - Antônio Pereira
- Department of Computer Science, Federal University of São João Del Rey, São João Del Rey, Minas Gerais Brazil
| | - Pablo Cecílio
- Department of Computer Science, Federal University of São João Del Rey, São João Del Rey, Minas Gerais Brazil
| | - Elisa Tuler
- Department of Computer Science, Federal University of São João Del Rey, São João Del Rey, Minas Gerais Brazil
| | - Wagner Meira
- Department of Computer Science, Federal University of Minas Gerais, Belo Horizonte, Minas Gerais Brazil
| | - Marcos Gonçalves
- Department of Computer Science, Federal University of Minas Gerais, Belo Horizonte, Minas Gerais Brazil
| | - Leonardo Rocha
- Department of Computer Science, Federal University of São João Del Rey, São João Del Rey, Minas Gerais Brazil
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Urbanin G, Meira W, Serpa A, Costa DDS, Baldaçara L, da Silva AP, Guatimosim R, Lacerda AM, Oliveira EA, Braule A, Romano-Silva MA, da Silva AG, Malloy-Diniz L, Pappa G, Miranda DM. Social determinants in self-protective behavior: association rule mining study (Preprint). JMIR Public Health Surveill 2021; 8:e34020. [PMID: 35704360 PMCID: PMC9202654 DOI: 10.2196/34020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2021] [Revised: 03/14/2022] [Accepted: 04/26/2022] [Indexed: 11/13/2022] Open
Abstract
Background Human behavior is crucial in health outcomes. Particularly, individual behavior is a determinant of the success of measures to overcome critical conditions, such as a pandemic. In addition to intrinsic public health challenges associated with COVID-19, in many countries, some individuals decided not to get vaccinated, streets were crowded, parties were happening, and businesses struggling to survive were partially open, despite lockdown or stay-at-home instructions. These behaviors contrast with the instructions for potential benefits associated with social distancing, use of masks, and vaccination to manage collective and individual risks. Objective Considering that human behavior is a result of individuals' social and economic conditions, we investigated the social and working characteristics associated with reports of appropriate protective behavior in Brazil. Methods We analyzed data from a large web survey of individuals reporting their behavior during the pandemic. We selected 3 common self-care measures: use of protective masks, distancing by at least 1 m when out of the house, and handwashing or use of alcohol, combined with assessment of the social context of respondents. We measured the frequency of the use of these self-protective measures. Using a frequent pattern–mining perspective, we generated association rules from a set of answers to questions that co-occur with at least a given frequency, identifying the pattern of characteristics of the groups divided according to protective behavior reports. Results The rationale was to identify a pool of working and social characteristics that might have better adhesion to behaviors and self-care measures, showing these are more socially determined than previously thought. We identified common patterns of socioeconomic and working determinants of compliance with protective self-care measures. Data mining showed that social determinants might be important to shape behavior in different stages of the pandemic. Conclusions Identification of context determinants might be helpful to identify unexpected facilitators and constraints to fully follow public policies. The context of diseases contributes to psychological and physical health outcomes, and context understanding might change the approach to a disease. Hidden social determinants might change protective behavior, and social determinants of protective behavior related to COVID-19 are related to work and economic conditions. Trial Registration Not applicable.
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Affiliation(s)
- Gabriel Urbanin
- Departamento de Ciência da Computação, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
| | - Wagner Meira
- Departamento de Ciência da Computação, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
| | - Alexandre Serpa
- Instituto de Saúde Mental Baseada em Evidências, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
| | - Danielle de Souza Costa
- Instituto de Saúde Mental Baseada em Evidências, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
| | - Leonardo Baldaçara
- Instituto de Saúde Mental Baseada em Evidências, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
- Associação Brasileira de Psiquiatria, Brasilia, Brazil
| | - Ana Paula da Silva
- Departamento de Ciência da Computação, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
| | - Rafaela Guatimosim
- Instituto de Saúde Mental Baseada em Evidências, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
| | - Anísio Mendes Lacerda
- Departamento de Ciência da Computação, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
| | | | - Andre Braule
- Instituto de Saúde Mental Baseada em Evidências, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
| | - Marco Aurélio Romano-Silva
- Centro de Tecnologia em Medicina Molecular, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
| | - Antônio Geraldo da Silva
- Instituto de Saúde Mental Baseada em Evidências, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
- Associação Brasileira de Psiquiatria, Brasilia, Brazil
- Faculdade de Medicina da Universidade do Porto, Porto, Portugal
| | - Leandro Malloy-Diniz
- Instituto de Saúde Mental Baseada em Evidências, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
- Associação Brasileira de Psiquiatria, Brasilia, Brazil
- Centro de Tecnologia em Medicina Molecular, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
| | - Gisele Pappa
- Departamento de Ciência da Computação, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
| | - Débora Marques Miranda
- Instituto de Saúde Mental Baseada em Evidências, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
- Departamento de Pediatria, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
- Centro de Tecnologia em Medicina Molecular, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
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Lima EM, Ribeiro AH, Paixão GMM, Ribeiro MH, Pinto-Filho MM, Gomes PR, Oliveira DM, Sabino EC, Duncan BB, Giatti L, Barreto SM, Meira W, Schön TB, Ribeiro ALP. Deep neural network-estimated electrocardiographic age as a mortality predictor. Nat Commun 2021; 12:5117. [PMID: 34433816 PMCID: PMC8387361 DOI: 10.1038/s41467-021-25351-7] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2021] [Accepted: 07/14/2021] [Indexed: 02/07/2023] Open
Abstract
The electrocardiogram (ECG) is the most commonly used exam for the evaluation of cardiovascular diseases. Here we propose that the age predicted by artificial intelligence (AI) from the raw ECG (ECG-age) can be a measure of cardiovascular health. A deep neural network is trained to predict a patient's age from the 12-lead ECG in the CODE study cohort (n = 1,558,415 patients). On a 15% hold-out split, patients with ECG-age more than 8 years greater than the chronological age have a higher mortality rate (hazard ratio (HR) 1.79, p < 0.001), whereas those with ECG-age more than 8 years smaller, have a lower mortality rate (HR 0.78, p < 0.001). Similar results are obtained in the external cohorts ELSA-Brasil (n = 14,236) and SaMi-Trop (n = 1,631). Moreover, even for apparent normal ECGs, the predicted ECG-age gap from the chronological age remains a statistically significant risk predictor. These results show that the AI-enabled analysis of the ECG can add prognostic information.
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Affiliation(s)
- Emilly M Lima
- Telehealth Center, Hospital das Clínicas, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil.,Faculdade de Medicina, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
| | - Antônio H Ribeiro
- Departamento de Ciência da Computação, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil.,Department of Information Technology, Uppsala University, Uppsala, Sweden
| | - Gabriela M M Paixão
- Telehealth Center, Hospital das Clínicas, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil.,Faculdade de Medicina, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
| | | | - Marcelo M Pinto-Filho
- Telehealth Center, Hospital das Clínicas, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil.,Faculdade de Medicina, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
| | - Paulo R Gomes
- Telehealth Center, Hospital das Clínicas, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil.,Faculdade de Medicina, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
| | - Derick M Oliveira
- Departamento de Ciência da Computação, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
| | - Ester C Sabino
- Instituto de Medicina Tropical da Faculdade de Medicina da Universidade de São Paulo, São Paulo, Brazil
| | - Bruce B Duncan
- Programa de Pós-Graduação em Epidemiologia and Hospital de Clínicas de Porto Alegre, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil
| | - Luana Giatti
- Faculdade de Medicina, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
| | - Sandhi M Barreto
- Faculdade de Medicina, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
| | - Wagner Meira
- Departamento de Ciência da Computação, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
| | - Thomas B Schön
- Department of Information Technology, Uppsala University, Uppsala, Sweden.
| | - Antonio Luiz P Ribeiro
- Telehealth Center, Hospital das Clínicas, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil. .,Faculdade de Medicina, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil.
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Martins JFBS, Nascimento ER, Nascimento BR, Sable CA, Beaton AZ, Ribeiro AL, Meira W, Pappa GL. Towards automatic diagnosis of rheumatic heart disease on echocardiographic exams through video-based deep learning. J Am Med Inform Assoc 2021; 28:1834-1842. [PMID: 34279636 DOI: 10.1093/jamia/ocab061] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2020] [Revised: 03/09/2021] [Accepted: 03/19/2021] [Indexed: 01/17/2023] Open
Abstract
OBJECTIVE Rheumatic heart disease (RHD) affects an estimated 39 million people worldwide and is the most common acquired heart disease in children and young adults. Echocardiograms are the gold standard for diagnosis of RHD, but there is a shortage of skilled experts to allow widespread screenings for early detection and prevention of the disease progress. We propose an automated RHD diagnosis system that can help bridge this gap. MATERIALS AND METHODS Experiments were conducted on a dataset with 11 646 echocardiography videos from 912 exams, obtained during screenings in underdeveloped areas of Brazil and Uganda. We address the challenges of RHD identification with a 3D convolutional neural network (C3D), comparing its performance with a 2D convolutional neural network (VGG16) that is commonly used in the echocardiogram literature. We also propose a supervised aggregation technique to combine video predictions into a single exam diagnosis. RESULTS The proposed approach obtained an accuracy of 72.77% for exam diagnosis. The results for the C3D were significantly better than the ones obtained by the VGG16 network for videos, showing the importance of considering the temporal information during the diagnostic. The proposed aggregation model showed significantly better accuracy than the majority voting strategy and also appears to be capable of capturing underlying biases in the neural network output distribution, balancing them for a more correct diagnosis. CONCLUSION Automatic diagnosis of echo-detected RHD is feasible and, with further research, has the potential to reduce the workload of experts, enabling the implementation of more widespread screening programs worldwide.
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Affiliation(s)
- João Francisco B S Martins
- Department of Computer Science, Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais, Brazil
| | - Erickson R Nascimento
- Department of Computer Science, Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais, Brazil
| | - Bruno R Nascimento
- Department of Medicine, Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais, Brazil
| | - Craig A Sable
- Children's National Medical Center, Washington, DC, USA
| | - Andrea Z Beaton
- Cincinnati Children's Hospital Medical Center, The Heart Institute, Cincinnati, Ohio, USA
| | - Antônio L Ribeiro
- Department of Medicine, Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais, Brazil
| | - Wagner Meira
- Department of Computer Science, Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais, Brazil
| | - Gisele L Pappa
- Department of Computer Science, Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais, Brazil
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Henrique JR, Pereira RG, Ferreira RS, Keller H, de Van der Schueren M, Gonzalez MC, Meira W, Correia MITD. Pilot study GLIM criteria for categorization of a malnutrition diagnosis of patients undergoing elective gastrointestinal operations: A pilot study of applicability and validation. Nutrition 2020; 79-80:110961. [DOI: 10.1016/j.nut.2020.110961] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2020] [Revised: 06/25/2020] [Accepted: 07/06/2020] [Indexed: 01/07/2023]
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Souza RCSNP, Assunção RM, Oliveira DM, Neill DB, Meira W. Where did I get dengue? Detecting spatial clusters of infection risk with social network data. Spat Spatiotemporal Epidemiol 2018; 29:163-175. [PMID: 31128626 DOI: 10.1016/j.sste.2018.11.005] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/07/2017] [Revised: 06/13/2018] [Accepted: 11/14/2018] [Indexed: 11/25/2022]
Abstract
Typical spatial disease surveillance systems associate a single address to each disease case reported, usually the residence address. Social network data offers a unique opportunity to obtain information on the spatial movements of individuals as well as their disease status as cases or controls. This provides information to identify visit locations with high risk of infection, even in regions where no one lives such as parks and entertainment zones. We develop two probability models to characterize the high-risk regions. We use a large Twitter dataset from Brazilian users to search for spatial clusters through analysis of the tweets' locations and textual content. We apply our models to both real-world and simulated data, demonstrating the advantage of our models as compared to the usual spatial scan statistic for this type of data.
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Affiliation(s)
- Roberto C S N P Souza
- Department of Computer Science, Universidade Federal de Minas Gerais, Belo Horizonte, MG, Brazil.
| | - Renato M Assunção
- Department of Computer Science, Universidade Federal de Minas Gerais, Belo Horizonte, MG, Brazil.
| | - Derick M Oliveira
- Department of Computer Science, Universidade Federal de Minas Gerais, Belo Horizonte, MG, Brazil.
| | - Daniel B Neill
- Center for Urban Science and Progress, New York University, New York, NY, United States.
| | - Wagner Meira
- Department of Computer Science, Universidade Federal de Minas Gerais, Belo Horizonte, MG, Brazil.
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Sachetto Oliveira R, Martins Rocha B, Burgarelli D, Meira W, Constantinides C, Weber Dos Santos R. Performance evaluation of GPU parallelization, space-time adaptive algorithms, and their combination for simulating cardiac electrophysiology. Int J Numer Method Biomed Eng 2018; 34:e2913. [PMID: 28636811 DOI: 10.1002/cnm.2913] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/10/2016] [Revised: 06/09/2017] [Accepted: 06/16/2017] [Indexed: 05/23/2023]
Abstract
The use of computer models as a tool for the study and understanding of the complex phenomena of cardiac electrophysiology has attained increased importance nowadays. At the same time, the increased complexity of the biophysical processes translates into complex computational and mathematical models. To speed up cardiac simulations and to allow more precise and realistic uses, 2 different techniques have been traditionally exploited: parallel computing and sophisticated numerical methods. In this work, we combine a modern parallel computing technique based on multicore and graphics processing units (GPUs) and a sophisticated numerical method based on a new space-time adaptive algorithm. We evaluate each technique alone and in different combinations: multicore and GPU, multicore and GPU and space adaptivity, multicore and GPU and space adaptivity and time adaptivity. All the techniques and combinations were evaluated under different scenarios: 3D simulations on slabs, 3D simulations on a ventricular mouse mesh, ie, complex geometry, sinus-rhythm, and arrhythmic conditions. Our results suggest that multicore and GPU accelerate the simulations by an approximate factor of 33×, whereas the speedups attained by the space-time adaptive algorithms were approximately 48. Nevertheless, by combining all the techniques, we obtained speedups that ranged between 165 and 498. The tested methods were able to reduce the execution time of a simulation by more than 498× for a complex cellular model in a slab geometry and by 165× in a realistic heart geometry simulating spiral waves. The proposed methods will allow faster and more realistic simulations in a feasible time with no significant loss of accuracy.
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Affiliation(s)
- Rafael Sachetto Oliveira
- Departamento de Ciência da Computação, Universidade Federal de São João de Rei, São João del-rei MG, Brazil
| | - Bernardo Martins Rocha
- Departamento de Ciência da Computação e Programa em Modelagem Computacional, Universidade Federal de Juiz de Fora, Juiz de Fora, MG, Brazil
| | - Denise Burgarelli
- Departamento de Matemática, Universidade Federal de Minas Gerais, Belo Horizonte, MG, Brazil
| | - Wagner Meira
- Departamento de Ciência da Computação, Universidade Federal de Minas Gerais, Belo Horizonte, MG, Brazil
| | | | - Rodrigo Weber Dos Santos
- Departamento de Ciência da Computação e Programa em Modelagem Computacional, Universidade Federal de Juiz de Fora, Juiz de Fora, MG, Brazil
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Salles T, Rocha L, Mourão F, Gonçalves M, Viegas F, Meira W. A Two-Stage Machine learning approach for temporally-robust text classification. INFORM SYST 2017. [DOI: 10.1016/j.is.2017.04.004] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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Marques-Toledo CDA, Degener CM, Vinhal L, Coelho G, Meira W, Codeço CT, Teixeira MM. Dengue prediction by the web: Tweets are a useful tool for estimating and forecasting Dengue at country and city level. PLoS Negl Trop Dis 2017; 11:e0005729. [PMID: 28719659 PMCID: PMC5533462 DOI: 10.1371/journal.pntd.0005729] [Citation(s) in RCA: 44] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2016] [Revised: 07/28/2017] [Accepted: 06/20/2017] [Indexed: 01/12/2023] Open
Abstract
BACKGROUND Infectious diseases are a leading threat to public health. Accurate and timely monitoring of disease risk and progress can reduce their impact. Mentioning a disease in social networks is correlated with physician visits by patients, and can be used to estimate disease activity. Dengue is the fastest growing mosquito-borne viral disease, with an estimated annual incidence of 390 million infections, of which 96 million manifest clinically. Dengue burden is likely to increase in the future owing to trends toward increased urbanization, scarce water supplies and, possibly, environmental change. The epidemiological dynamic of Dengue is complex and difficult to predict, partly due to costly and slow surveillance systems. METHODOLOGY / PRINCIPAL FINDINGS In this study, we aimed to quantitatively assess the usefulness of data acquired by Twitter for the early detection and monitoring of Dengue epidemics, both at country and city level at a weekly basis. Here, we evaluated and demonstrated the potential of tweets modeling for Dengue estimation and forecast, in comparison with other available web-based data, Google Trends and Wikipedia access logs. Also, we studied the factors that might influence the goodness-of-fit of the model. We built a simple model based on tweets that was able to 'nowcast', i.e. estimate disease numbers in the same week, but also 'forecast' disease in future weeks. At the country level, tweets are strongly associated with Dengue cases, and can estimate present and future Dengue cases until 8 weeks in advance. At city level, tweets are also useful for estimating Dengue activity. Our model can be applied successfully to small and less developed cities, suggesting a robust construction, even though it may be influenced by the incidence of the disease, the activity of Twitter locally, and social factors, including human development index and internet access. CONCLUSIONS Tweets association with Dengue cases is valuable to assist traditional Dengue surveillance at real-time and low-cost. Tweets are able to successfully nowcast, i.e. estimate Dengue in the present week, but also forecast, i.e. predict Dengue at until 8 weeks in the future, both at country and city level with high estimation capacity.
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Affiliation(s)
- Cecilia de Almeida Marques-Toledo
- Departamento de Bioquimica e Imunologia do Instituto de Ciencias Biologicas, Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais, Brazil
- Consultoria Tecnica, Ecovec LTDA, Belo Horizonte, Minas Gerais, Brazil
| | - Carolin Marlen Degener
- Programa de Computacao Cientifica, Fundacao Oswaldo Cruz, Rio de Janeiro, Rio de Janeiro, Brazil
| | - Livia Vinhal
- Secretaria de Vigilancia em Saude, Ministerio da Saude, Brasilia, Brazil
| | - Giovanini Coelho
- Secretaria de Vigilancia em Saude, Ministerio da Saude, Brasilia, Brazil
| | - Wagner Meira
- Departamento de Ciencia da Computacao do Instituto de Ciencias Exatas, Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais, Brazil
| | - Claudia Torres Codeço
- Programa de Computacao Cientifica, Fundacao Oswaldo Cruz, Rio de Janeiro, Rio de Janeiro, Brazil
| | - Mauro Martins Teixeira
- Departamento de Bioquimica e Imunologia do Instituto de Ciencias Biologicas, Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais, Brazil
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Pereira R, Dias L, Ávila J, Santos N, Gurgel EI, Cherchiglia ML, AcÚrcio F, Reis A, Meira W, Guerra AA. Unified health database creation: 125 million brazilian cohort from information systems of hospital, outpatient, births, notifications and mortalities. Int J Popul Data Sci 2017. [PMCID: PMC8480838 DOI: 10.23889/ijpds.v1i1.225] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
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13
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Pappa GL, Cunha TO, Bicalho PV, Ribeiro A, Couto Silva AP, Meira W, Beleigoli AMR. Factors Associated With Weight Change in Online Weight Management Communities: A Case Study in the LoseIt Reddit Community. J Med Internet Res 2017; 19:e17. [PMID: 28093378 PMCID: PMC5282451 DOI: 10.2196/jmir.5816] [Citation(s) in RCA: 54] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2016] [Revised: 09/05/2016] [Accepted: 10/22/2016] [Indexed: 11/23/2022] Open
Abstract
Background Recent research has shown that of the 72% of American Internet users who have looked for health information online, 22% have searched for help to lose or control weight. This demand for information has given rise to many online weight management communities, where users support one another throughout their weight loss process. Whether and how user engagement in online communities relates to weight change is not totally understood. Objective We investigated the activity behavior and analyze the semantic content of the messages of active users in LoseIt (r/loseit), a weight management community of the online social network Reddit. We then explored whether these features are associated with weight loss in this online social network. Methods A data collection tool was used to collect English posts, comments, and other public metadata of active users (ie, users with at least one post or comment) on LoseIt from August 2010 to November 2014. Analyses of frequency and intensity of user interaction in the community were performed together with a semantic analysis of the messages, done by a latent Dirichlet allocation method. The association between weight loss and online user activity patterns, the semantics of the messages, and real-world variables was found by a linear regression model using 30-day weight change as the dependent variable. Results We collected posts and comments of 107,886 unique users. Among these, 101,003 (93.62%) wrote at least one comment and 38,981 (36.13%) wrote at least one post. Median percentage of days online was 3.81 (IQR 9.51). The 10 most-discussed semantic topics on posts were related to healthy food, clothing, calorie counting, workouts, looks, habits, support, and unhealthy food. In the subset of 754 users who had gender, age, and 30-day weight change data available, women were predominant and 92.9% (701/754) lost weight. Female gender, body mass index (BMI) at baseline, high levels of online activity, the number of upvotes received per post, and topics discussed within the community were independently associated with weight change. Conclusions Our findings suggest that among active users of a weight management community, self-declaration of higher BMI levels (which may represent greater dissatisfaction with excess weight), high online activity, and engagement in discussions that might provide social support are associated with greater weight loss. These findings have the potential to aid health professionals to assist patients in online interventions by focusing efforts on increasing engagement and/or starting discussions on topics of higher impact on weight change.
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Affiliation(s)
- Gisele Lobo Pappa
- Computer Science Department, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
| | - Tiago Oliveira Cunha
- Computer Science Department, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
| | - Paulo Viana Bicalho
- Computer Science Department, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
| | - Antonio Ribeiro
- Internal Medicine Department, Faculdade de Medicina, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil.,Centro de Telessaúde do Hospital das Clínicas, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
| | - Ana Paula Couto Silva
- Computer Science Department, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
| | - Wagner Meira
- Computer Science Department, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
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Boari de Lima E, Meira W, de Melo-Minardi RC. Isofunctional Protein Subfamily Detection Using Data Integration and Spectral Clustering. PLoS Comput Biol 2016; 12:e1005001. [PMID: 27348631 PMCID: PMC4922564 DOI: 10.1371/journal.pcbi.1005001] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2015] [Accepted: 05/22/2016] [Indexed: 01/14/2023] Open
Abstract
As increasingly more genomes are sequenced, the vast majority of proteins may only be annotated computationally, given experimental investigation is extremely costly. This highlights the need for computational methods to determine protein functions quickly and reliably. We believe dividing a protein family into subtypes which share specific functions uncommon to the whole family reduces the function annotation problem's complexity. Hence, this work's purpose is to detect isofunctional subfamilies inside a family of unknown function, while identifying differentiating residues. Similarity between protein pairs according to various properties is interpreted as functional similarity evidence. Data are integrated using genetic programming and provided to a spectral clustering algorithm, which creates clusters of similar proteins. The proposed framework was applied to well-known protein families and to a family of unknown function, then compared to ASMC. Results showed our fully automated technique obtained better clusters than ASMC for two families, besides equivalent results for other two, including one whose clusters were manually defined. Clusters produced by our framework showed great correspondence with the known subfamilies, besides being more contrasting than those produced by ASMC. Additionally, for the families whose specificity determining positions are known, such residues were among those our technique considered most important to differentiate a given group. When run with the crotonase and enolase SFLD superfamilies, the results showed great agreement with this gold-standard. Best results consistently involved multiple data types, thus confirming our hypothesis that similarities according to different knowledge domains may be used as functional similarity evidence. Our main contributions are the proposed strategy for selecting and integrating data types, along with the ability to work with noisy and incomplete data; domain knowledge usage for detecting subfamilies in a family with different specificities, thus reducing the complexity of the experimental function characterization problem; and the identification of residues responsible for specificity.
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Affiliation(s)
- Elisa Boari de Lima
- Department of Biochemistry and Immunology, Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais, Brazil
- Department of Computer Science, Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais, Brazil
| | - Wagner Meira
- Department of Computer Science, Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais, Brazil
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Salles T, Rocha L, Gonçalves MA, Almeida JM, Mourão F, Meira W, Viegas F. A quantitative analysis of the temporal effects on automatic text classification. J Assoc Inf Sci Technol 2015. [DOI: 10.1002/asi.23452] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Affiliation(s)
- Thiago Salles
- Department of Computer Sience; Universidade Federal de Minas Gerais; Brazil
| | | | | | - Jussara M. Almeida
- Department of Computer Sience; Universidade Federal de Minas Gerais; Brazil
| | - Fernando Mourão
- Department of Computer Sience; Universidade Federal de Minas Gerais; Brazil
| | - Wagner Meira
- Department of Computer Sience; Universidade Federal de Minas Gerais; Brazil
| | - Felipe Viegas
- Department of Computer Sience; Universidade Federal de Minas Gerais; Brazil
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Gonçalves WRS, Gonçalves-Almeida VM, Arruda AL, Meira W, da Silveira CH, Pires DEV, de Melo-Minardi RC. PDBest: a user-friendly platform for manipulating and enhancing protein structures. Bioinformatics 2015; 31:2894-6. [PMID: 25910698 DOI: 10.1093/bioinformatics/btv223] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2015] [Accepted: 04/19/2015] [Indexed: 11/14/2022] Open
Abstract
UNLABELLED PDBest (PDB Enhanced Structures Toolkit) is a user-friendly, freely available platform for acquiring, manipulating and normalizing protein structures in a high-throughput and seamless fashion. With an intuitive graphical interface it allows users with no programming background to download and manipulate their files. The platform also exports protocols, enabling users to easily share PDB searching and filtering criteria, enhancing analysis reproducibility. AVAILABILITY AND IMPLEMENTATION PDBest installation packages are freely available for several platforms at http://www.pdbest.dcc.ufmg.br CONTACT wellisson@dcc.ufmg.br, dpires@dcc.ufmg.br, raquelcm@dcc.ufmg.br SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
| | | | - Aleksander L Arruda
- Department of Computer Science, Universidade Federal de Minas Gerais, Brazil
| | - Wagner Meira
- Department of Computer Science, Universidade Federal de Minas Gerais, Brazil
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Silveira SA, Fassio AV, Gonçalves-Almeida VM, de Lima EB, Barcelos YT, Aburjaile FF, Rodrigues LM, Meira W, de Melo-Minardi RC. VERMONT: Visualizing mutations and their effects on protein physicochemical and topological property conservation. BMC Proc 2014; 8:S4. [PMID: 25237391 PMCID: PMC4155615 DOI: 10.1186/1753-6561-8-s2-s4] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022] Open
Abstract
In this paper, we propose an interactive visualization called VERMONT which tackles the problem of visualizing mutations and infers their possible effects on the conservation of physicochemical and topological properties in protein families. More specifically, we visualize a set of structure-based sequence alignments and integrate several structural parameters that should aid biologists in gaining insight into possible consequences of mutations. VERMONT allowed us to identify patterns of position-specific properties as well as exceptions that may help predict whether specific mutations could damage protein function.
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Affiliation(s)
- Sabrina A Silveira
- Department of Computer Science, Universidade Federal de Minas Gerais, Av. Antônio Carlos 6.627, 31270-901, Belo Horizonte, Brazil
| | - Alexandre V Fassio
- Department of Computer Science, Universidade Federal de Minas Gerais, Av. Antônio Carlos 6.627, 31270-901, Belo Horizonte, Brazil.,Department of Biochemistry and Immunology, Universidade Federal de Minas Gerais, Av. Antônio Carlos 6.627, 31270-901, Belo Horizonte, Brazil
| | - Valdete M Gonçalves-Almeida
- Department of Computer Science, Universidade Federal de Minas Gerais, Av. Antônio Carlos 6.627, 31270-901, Belo Horizonte, Brazil
| | - Elisa B de Lima
- Department of Computer Science, Universidade Federal de Minas Gerais, Av. Antônio Carlos 6.627, 31270-901, Belo Horizonte, Brazil.,Department of Biochemistry and Immunology, Universidade Federal de Minas Gerais, Av. Antônio Carlos 6.627, 31270-901, Belo Horizonte, Brazil
| | - Yussif T Barcelos
- Department of Computer Science, Universidade Federal de Minas Gerais, Av. Antônio Carlos 6.627, 31270-901, Belo Horizonte, Brazil
| | - Flávia F Aburjaile
- Department of Biochemistry and Immunology, Universidade Federal de Minas Gerais, Av. Antônio Carlos 6.627, 31270-901, Belo Horizonte, Brazil
| | - Laerte M Rodrigues
- Department of Computer Science, Universidade Federal de Minas Gerais, Av. Antônio Carlos 6.627, 31270-901, Belo Horizonte, Brazil.,Department of Biochemistry and Immunology, Universidade Federal de Minas Gerais, Av. Antônio Carlos 6.627, 31270-901, Belo Horizonte, Brazil
| | - Wagner Meira
- Department of Computer Science, Universidade Federal de Minas Gerais, Av. Antônio Carlos 6.627, 31270-901, Belo Horizonte, Brazil
| | - Raquel C de Melo-Minardi
- Department of Computer Science, Universidade Federal de Minas Gerais, Av. Antônio Carlos 6.627, 31270-901, Belo Horizonte, Brazil
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Pires DEV, de Melo-Minardi RC, da Silveira CH, Campos FF, Meira W. aCSM: noise-free graph-based signatures to large-scale receptor-based ligand prediction. ACTA ACUST UNITED AC 2013; 29:855-61. [PMID: 23396119 DOI: 10.1093/bioinformatics/btt058] [Citation(s) in RCA: 47] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022]
Abstract
MOTIVATION Receptor-ligand interactions are a central phenomenon in most biological systems. They are characterized by molecular recognition, a complex process mainly driven by physicochemical and structural properties of both receptor and ligand. Understanding and predicting these interactions are major steps towards protein ligand prediction, target identification, lead discovery and drug design. RESULTS We propose a novel graph-based-binding pocket signature called aCSM, which proved to be efficient and effective in handling large-scale protein ligand prediction tasks. We compare our results with those described in the literature and demonstrate that our algorithm overcomes the competitor's techniques. Finally, we predict novel ligands for proteins from Trypanosoma cruzi, the parasite responsible for Chagas disease, and validate them in silico via a docking protocol, showing the applicability of the method in suggesting ligands for pockets in a real-world scenario. AVAILABILITY AND IMPLEMENTATION Datasets and the source code are available at http://www.dcc.ufmg.br/∼dpires/acsm. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Douglas E V Pires
- Department of Computer Science, Universidade Federal de Minas Gerais, Av. Antônio Carlos, 6627, Pampulha Belo Horizonte - MG, 31270-901, Brazil.
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Pires DEV, de Melo-Minardi RC, dos Santos MA, da Silveira CH, Santoro MM, Meira W. Cutoff Scanning Matrix (CSM): structural classification and function prediction by protein inter-residue distance patterns. BMC Genomics 2011; 12 Suppl 4:S12. [PMID: 22369665 PMCID: PMC3287581 DOI: 10.1186/1471-2164-12-s4-s12] [Citation(s) in RCA: 49] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022] Open
Abstract
Background The unforgiving pace of growth of available biological data has increased the demand for efficient and scalable paradigms, models and methodologies for automatic annotation. In this paper, we present a novel structure-based protein function prediction and structural classification method: Cutoff Scanning Matrix (CSM). CSM generates feature vectors that represent distance patterns between protein residues. These feature vectors are then used as evidence for classification. Singular value decomposition is used as a preprocessing step to reduce dimensionality and noise. The aspect of protein function considered in the present work is enzyme activity. A series of experiments was performed on datasets based on Enzyme Commission (EC) numbers and mechanistically different enzyme superfamilies as well as other datasets derived from SCOP release 1.75. Results CSM was able to achieve a precision of up to 99% after SVD preprocessing for a database derived from manually curated protein superfamilies and up to 95% for a dataset of the 950 most-populated EC numbers. Moreover, we conducted experiments to verify our ability to assign SCOP class, superfamily, family and fold to protein domains. An experiment using the whole set of domains found in last SCOP version yielded high levels of precision and recall (up to 95%). Finally, we compared our structural classification results with those in the literature to place this work into context. Our method was capable of significantly improving the recall of a previous study while preserving a compatible precision level. Conclusions We showed that the patterns derived from CSMs could effectively be used to predict protein function and thus help with automatic function annotation. We also demonstrated that our method is effective in structural classification tasks. These facts reinforce the idea that the pattern of inter-residue distances is an important component of family structural signatures. Furthermore, singular value decomposition provided a consistent increase in precision and recall, which makes it an important preprocessing step when dealing with noisy data.
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Affiliation(s)
- Douglas E V Pires
- Department of Biochemistry and Immunology, Universidade Federal de Minas Gerais, Belo Horizonte, 31270-901, Brazil.
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Gonçalves-Almeida VM, Pires DEV, de Melo-Minardi RC, da Silveira CH, Meira W, Santoro MM. HydroPaCe: understanding and predicting cross-inhibition in serine proteases through hydrophobic patch centroids. Bioinformatics 2011; 28:342-9. [DOI: 10.1093/bioinformatics/btr680] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
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23
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Veloso A, Zaki M, Meira W, Gonçalves M. Competence-conscious associative classification. Stat Anal Data Min 2009. [DOI: 10.1002/sam.10058] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
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Gomide J, Melo-Minardi R, Dos Santos MA, Neshich G, Meira W, Lopes JC, Santoro M. Using linear algebra for protein structural comparison and classification. Genet Mol Biol 2009; 32:645-51. [PMID: 21637532 PMCID: PMC3036040 DOI: 10.1590/s1415-47572009000300032] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2008] [Accepted: 05/25/2009] [Indexed: 11/23/2022] Open
Abstract
In this article, we describe a novel methodology to extract semantic characteristics from protein structures using linear algebra in order to compose structural signature vectors which may be used efficiently to compare and classify protein structures into fold families. These signatures are built from the pattern of hydrophobic intrachain interactions using Singular Value Decomposition (SVD) and Latent Semantic Indexing (LSI) techniques. Considering proteins as documents and contacts as terms, we have built a retrieval system which is able to find conserved contacts in samples of myoglobin fold family and to retrieve these proteins among proteins of varied folds with precision of up to 80%. The classifier is a web tool available at our laboratory website. Users can search for similar chains from a specific PDB, view and compare their contact maps and browse their structures using a JMol plug-in.
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Affiliation(s)
- Janaína Gomide
- Departamento de Ciência da Computação, Universidade Federal de Minas Gerais, Belo Horizonte, MG Brazil
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da Silveira CH, Pires DEV, Minardi RC, Ribeiro C, Veloso CJM, Lopes JCD, Meira W, Neshich G, Ramos CHI, Habesch R, Santoro MM. Protein cutoff scanning: A comparative analysis of cutoff dependent and cutoff free methods for prospecting contacts in proteins. Proteins 2009; 74:727-43. [PMID: 18704933 DOI: 10.1002/prot.22187] [Citation(s) in RCA: 67] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Affiliation(s)
- Carlos H da Silveira
- Department of Biochemistry and Immunology, Institute of Biological Sciences, Federal University of Minas Gerais, UFMG, Brazil.
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Teodoro G, Tavares T, Ferreira R, Kurc T, Meira W, Guedes D, Pan T, Saltz J. A Run-time System for Efficient Execution of Scientific Workflows on Distributed Environments. Int J Parallel Program 2008; 36:250-266. [PMID: 22582009 PMCID: PMC3348585 DOI: 10.1007/s10766-007-0068-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/31/2023]
Abstract
Scientific workflow systems have been introduced in response to the demand of researchers from several domains of science who need to process and analyze increasingly larger datasets. The design of these systems is largely based on the observation that data analysis applications can be composed as pipelines or networks of computations on data. In this work, we present a runtime support system that is designed to facilitate this type of computation in distributed computing environments. Our system is optimized for data-intensive workflows, in which efficient management and retrieval of data, coordination of data processing and data movement, and check-pointing of intermediate results are critical and challenging issues. Experimental evaluation of our system shows that linear speedups can be achieved for sophisticated applications, which are implemented as a network of multiple data processing components.
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Affiliation(s)
- George Teodoro
- Department of Computer Science, Universidade Federal de Minas Gerais, 31270-010 Belo Horizonte, MG - Brazil, tel +55(31)3499-5860 - fax +55(31)3499-5858
| | - Tulio Tavares
- Department of Computer Science, Universidade Federal de Minas Gerais, 31270-010 Belo Horizonte, MG - Brazil, tel +55(31)3499-5860 - fax +55(31)3499-5858
| | - Renato Ferreira
- Department of Computer Science, Universidade Federal de Minas Gerais, 31270-010 Belo Horizonte, MG - Brazil, tel +55(31)3499-5860 - fax +55(31)3499-5858
| | - Tahsin Kurc
- Department of Biomedical Informatics, The Ohio State University, Columbus, OH, 43210 - USA, tel +1(614)292-4778 - fax +1(614)688-6600
| | - Wagner Meira
- Department of Computer Science, Universidade Federal de Minas Gerais, 31270-010 Belo Horizonte, MG - Brazil, tel +55(31)3499-5860 - fax +55(31)3499-5858
| | - Dorgival Guedes
- Department of Computer Science, Universidade Federal de Minas Gerais, 31270-010 Belo Horizonte, MG - Brazil, tel +55(31)3499-5860 - fax +55(31)3499-5858
| | - Tony Pan
- Department of Biomedical Informatics, The Ohio State University, Columbus, OH, 43210 - USA, tel +1(614)292-4778 - fax +1(614)688-6600
| | - Joel Saltz
- Department of Biomedical Informatics, The Ohio State University, Columbus, OH, 43210 - USA, tel +1(614)292-4778 - fax +1(614)688-6600
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Melo RC, Ribeiro C, Murray CS, Veloso CJM, da Silveira CH, Neshich G, Meira W, Carceroni RL, Santoro MM. Finding protein-protein interaction patterns by contact map matching. Genet Mol Res 2007; 6:946-963. [PMID: 18058715] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/25/2023]
Abstract
We propose a novel method for defining patterns of contacts present in protein-protein complexes. A new use of the traditional contact maps (more frequently used for representation of the intra-chain contacts) is presented for analysis of inter-chain contacts. Using an algorithm based on image processing techniques, we can compare protein-protein interaction maps and also obtain a dissimilarity score between them. The same algorithm used to compare the maps can align the contacts of all the complexes and be helpful in the determination of a pattern of conserved interactions at the interfaces. We present an example for the application of this method by analyzing the pattern of interaction of bovine pancreatic trypsin inhibitors and trypsins, chymotrypsins, a thrombin, a matriptase, and a kallikrein - all classified as serine proteases. We found 20 contacts conserved in trypsins and chymotrypsins and 3 specific ones are present in all the serine protease complexes studied. The method was able to identify important contacts for the protein family studied and the results are in agreement with the literature.
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Affiliation(s)
- R C Melo
- Departamento de Bioquímica e Imunologia, Universidade Federal de Minas Gerais, Belo Horizonte, MG, Brasil.
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Veloso CJM, Silveira CH, Melo RC, Ribeiro C, Lopes JCD, Santoro MM, Meira W. On the characterization of energy networks of proteins. Genet Mol Res 2007; 6:799-820. [PMID: 18058705] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/25/2023]
Abstract
The construction of a realistic theoretical model of proteins is determinant for improving the computational simulations of their structural and functional aspects. Modeling proteins as a network of non-covalent connections between the atoms of amino acid residues has shown valuable insights into these macromolecules. The energy-related properties of protein structures are known to be very important in molecular dynamics. However, these same properties have been neglected when the protein structures are modeled as networks of atoms and amino acid residues. A new approach for the construction of protein models based on a network of atoms is presented. This method, based on interatomic interaction, takes into account the energy and geometric aspects of the protein structures that were not employed before, such as atomic occlusion inside the protein, the use of solvation, protein modeling and analysis, and the use of energy potentials to estimate the energies of interatomic non-covalent contacts. As a result, we achieved a more realistic network model of proteins. This model has the virtue of being more robust in face of different unknown variables that usually are arbitrarily estimated. We were able to determine the most connected residues of all the proteins studied, so that we are now in a better condition to study their structural role.
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Affiliation(s)
- C J M Veloso
- Departamento de Ciência da Computação, UFMG, Belo Horizonte, MG, Brasil.
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Abstract
The peak power consumption of hardware components affects their powersupply, packaging, and cooling requirements. When the peak power consumption is high, the hardware components or the systems that use them can become expensive and bulky. Given that components and systems rarely (if ever) actually require peak power, it is highly desirable to limit power consumption to a less-than-peak power budget, based on which power supply, packaging, and cooling infrastructure scan be more intelligently provisioned.
In this paper, we study dynamic approaches for limiting the powerconsumption of main memories. Specifically, we propose four techniques that limit consumption by adjusting the power states of thememory devices, as a function of the load on the memory subsystem. Our simulations of applications from three benchmarks demonstrate that our techniques can consistently limit power to a pre-established budget. Two of the techniques can limit power with very low performance degradation. Our results also show that, when using these superior techniques, limiting power is at least as effective an energy-conservation approach as state-of-the-art technique sexplicitly designed for performance-aware energy conservation. These latter results represent a departure from current energy management research and practice.
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Affiliation(s)
- Bruno Diniz
- Federal University of Minas Gerais, Belo Horizonte, Brazil
| | | | - Wagner Meira
- Federal University of Minas Gerais, Belo Horizonte, Brazil
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de Melo RC, Lopes CER, Fernandes FA, da Silveira CH, Santoro MM, Carceroni RL, Meira W, Araújo ADA. A contact map matching approach to protein structure similarity analysis. Genet Mol Res 2006; 5:284-308. [PMID: 16819709] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/10/2023]
Abstract
We modeled the problem of identifying how close two proteins are structurally by measuring the dissimilarity of their contact maps. These contact maps are colored images, in which the chromatic information encodes the chemical nature of the contacts. We studied two conceptually distinct image-processing algorithms to measure the dissimilarity between these contact maps; one was a content-based image retrieval method, and the other was based on image registration. In experiments with contact maps constructed from the protein data bank, our approach was able to identify, with greater than 80% precision, instances of monomers of apolipoproteins, globins, plastocyanins, retinol binding proteins and thioredoxins, among the monomers of Protein Data Bank Select. The image registration approach was only slightly more accurate than the content-based image retrieval approach.
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Affiliation(s)
- Raquel C de Melo
- Departamento de Ciência da Computação, UFMG, Belo Horizonte, MG, Brasil.
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Abstract
This work presents a new approach for ranking documents in the vector space model. The novelty lies in two fronts. First, patterns of term co-occurrence are taken into account and are processed efficiently. Second, term weights are generated using a data mining technique called association rules. This leads to a new ranking mechanism called the
set-based vector model
. The components of our model are no longer index terms but index termsets, where a termset is a set of index terms. Termsets capture the intuition that semantically related terms appear close to each other in a document. They can be efficiently obtained by limiting the computation to small passages of text. Once termsets have been computed, the ranking is calculated as a function of the termset frequency in the document and its scarcity in the document collection. Experimental results show that the set-based vector model improves average precision for all collections and query types evaluated, while keeping computational costs small. For the 2-gigabyte TREC-8 collection, the set-based vector model leads to a gain in average precision figures of 14.7% and 16.4% for disjunctive and conjunctive queries, respectively, with respect to the standard vector space model. These gains increase to 24.9% and 30.0%, respectively, when proximity information is taken into account. Query processing times are larger but, on average, still comparable to those obtained with the standard vector model (increases in processing time varied from 30% to 300%). Our results suggest that the set-based vector model provides a correlation-based ranking formula that is effective with general collections and computationally practical.
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Affiliation(s)
| | | | | | - Berthier Ribeiro-Neto
- Federal University of Minas Gerais, Brazil and Akwan Information Technologies, MG, Brazil
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Abstract
Traditional methods for data mining typically make the assumption that the data is centralized, memory-resident, and static. This assumption is no longer tenable. Such methods waste computational and input/output (I/O) resources when data is dynamic, and they impose excessive communication overhead when data is distributed. Efficient implementation of incremental data mining methods is, thus, becoming crucial for ensuring system scalability and facilitating knowledge discovery when data is dynamic and distributed. In this paper, we address this issue in the context of the important task of frequent itemset mining. We first present an efficient algorithm which dynamically maintains the required information even in the presence of data updates without examining the entire dataset. We then show how to parallelize this incremental algorithm. We also propose a distributed asynchronous algorithm, which imposes minimal communication overhead for mining distributed dynamic datasets. Our distributed approach is capable of generating local models (in which each site has a summary of its own database) as well as the global model of frequent itemsets (in which all sites have a summary of the entire database). This ability permits our approach not only to generate frequent itemsets, but also to generate high-contrast frequent itemsets, which allows one to examine how the data is skewed over different sites.
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Affiliation(s)
- Matthew Eric Otey
- Computer and Information Science Department, The Ohio State University, Columbus, OH 43210, USA.
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