News
About Us
Our main objective is to develop an integrated and user-friendly platform, called AutoAI-Pandemics, democratizing access to data science and machine learning techniques by allowing non-experts to use them well, e.g., biologists, physicians, and epidemiologists.
AutoAI-Pandemics seeks to provide the following solutions:
- Automated epidemiologic analysis to detect possible epidemic scenarios and corresponding optimal intervention policies;
- Automated bioinformatics analysis, e.g., drug discovery or pathogen genome mining;
- Fighting misinformation/disinformation to assist in the search for reliable sources.
Our Solutions
Some studies and solutions are available.
BioAutoML
Democratizing Machine Learning in Life Sciences - Automated Feature Engineering and Metalearning for Classification of Biological Sequences.
Read more Access solutionBioAutoML-FAST
Empowering Breakthroughs in Life Sciences with End-to-End Machine Learning - Launching soon!
Read more Access solutionMathFeature
Feature Extraction Package for Biological Sequences Based on Mathematical Descriptors.
Read more Access solutionBioPrediction
Democratizing Machine Learning in the Study of Molecular Interactions
Read more Access solutionBioPrediction-RPI
Democratizing the Prediction of Interaction Between Non-Coding RNA and Protein with End-to-End Machine Learning.
Read more Access solutionITT- Is That True?
Dominique: Your Information Integrity Assistant - Experimental Phase - Portuguese or English.
Read more Access solution
Total number of people directly and indirectly impacted by our projects
Accesses have been achieved by our solutions and articles
Book Pages accessed on the MinhaBiblioteca platform
People Educated with our book to democratize AI knowledge
Citations in academic papers have been earned by our studies
News published in national and international media
Awards have been earned by our projects
Our Projects
Projects with the Community
Awards
Recognitions and Awards
Young Bioinformatics Award 2024
Our BioAutoML project received an honorable mention from the Young Bioinformatics Award 2024, being chosen among the best theses in Bioinformatics and Computational Biology in Brazil.
See more: [Link-1]
Editor's Choice Article, Entropy
Our paper: "Information Theory for Biological Sequence Classification: A Novel Feature Extraction Technique Based on Tsallis Entropy." Recognized by the journal's Academic Editor as an exceptional contribution to the field, 2024.
See more: [Link-1]
Conic-Semesp
ÁGUEDA Project (Artificial Intelligence for Early Detection of Breast Cancer), recognized as the best ongoing research project in the field of Exact and Earth Sciences in Brazil by Conic-Semesp, among more than 1,200 registered projects, 2023.
Artificial Intelligence Prize
Project to empower researchers in life sciences wins J.F. Marar Artificial Intelligence Prize for Undergraduate Studies
See more: [Link-1]
Falling Walls Lab Brasil 2023
Two projects connected to AutoAI-Pandemias are among the 15 finalists of Falling Walls Lab Brasil 2023.
See more: [Link-1]
Our Team
André de Carvalho
Robson Bonidia
Claudio Struchiner
Ulisses da Rocha
Guilherme Goedert
Peter Stadler
Danilo Sanches
Maria Emília Walter
Troy Day
Marcia Castro
John Edmunds
Anderson Santos
Breno de Almeida
Bruno Florentino
Natan Sanches
Denis Tavares
João Lucas Rodrigues
Recent News Posts
See More
About Our Projects (Click on the image to watch the video)
![[Link-3]](https://ai4pep.org/wp-content/uploads/2023/07/Projects-2048x1707.png){kind=link}
Frequently Asked Questions
Infectious diseases, transmitted directly or indirectly, are among the main causes of epidemics, or even pandemics. Despite recent achievements, there are several open challenges for predicting possible epidemics, detecting variants, contact tracing, discovering new drugs, and fighting misinformation. Artificial Intelligence (AI) can provide tools to deal with these scenarios, having shown effective results in fighting infectious diseases, e.g., the COVID-19 pandemic. Although AI creates new opportunities, its proper use requires advanced knowledge of computing, statistics, and mathematics, limiting their use by non-experts, e.g., biologists, physicians, and epidemiologists. Our objective is to develop an integrated and user-friendly platform that can be effectively employed by non-experts working with infectious diseases. This platform, named AutoAI-Pandemics, will provide robust solutions using Automated Machine Learning for (T1) epidemiologic analysis to detect possible epidemic scenarios and corresponding interventions to safely suppress disease spread with minimal social impact; (T2) bioinformatics analysis, helping life scientists with pathogen genome mining, and (T3) fighting misinformation to assist the search for reliable information sources. We will provide a platform to work on various critical stages of an epidemic/pandemic. AutoAI-Pandemics can be used by policymakers and other stakeholders, healthcare professionals, pharmaceutical industries, genomic surveillance organizations, and for combating disinformation. AutoAI-Pandemics will comply with what is expected by responsible AI solutions, which include fairness, privacy protection, sustainability, and no form of prejudice. Finally, to deal with the complex aspects of this project, we assembled an interdisciplinary team of researchers with expertise in computer science, AI, bioinformatics, and infectious diseases epidemiology.
(1) Early detection of emerging and re-emerging infectious diseases;
(2) Early warning systems for emerging and re-emerging infectious diseases;
(3) Early response to emerging and re-emerging infectious diseases;
(4) Mitigation and control of developing epidemics/pandemics.
(T1) Automated epidemiologic analysis to detect possible epidemic scenarios and corresponding optimal intervention policies;
(T2) Automated bioinformatics analysis, e.g., drug discovery or pathogen genome mining;
(T3) Fighting misinformation/disinformation to assist in the search for reliable sources.
(1) Assist non-specialist researchers in using ML for analysis, study, and control of epidemics and pandemics;
(2) Assist in challenging problems such as drug resistance, treatment of infectious diseases, epidemiologic analysis, bioinformatics analysis, and combat misinformation;
(3) To develop public APIs/packages for the scientific community in each proposed topic;
(4) To develop a user-friendly platform that can be effectively applied by non-experts working with infectious diseases.
(1) Data dashboard and Portals;
(2) Web-Based Application;
(3) Peer-Reviewed Articles;
(4) Online Searchable Repository;
(5) Computational Tools.
(1) Researchers and healthcare workers;
(2) Pharmaceutical industry and genomic organizations;
(3) Policymakers and other stakeholders;
(4) International organizations, e.g., WHO and PAHO;
(5) Ministry of health, state health departments.
The direct impact was calculated based on access, books sold, events and others. We calculated the indirect impact by estimating that each of our awards and media features reached an average of 2,000 people each (see https://www.similarweb.com/).
[Link][Award] BONIDIA, Robson Parmezan; CARVALHO, André Carlos Ponce de Leon Ferreira de. BioAutoML: Democratizing Machine Learning in Life Sciences. In: PRÊMIO ARTUR ZIVIANI - CONCURSO DE TESES E DISSERTAÇÕES (DOUTORADO) - SIMPÓSIO BRASILEIRO DE COMPUTAÇÃO APLICADA À SAÚDE (SBCAS), 24. , 2024, Goiânia/GO. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2024 . p. 85-90. ISSN 2763-8987.
[Link][Award] FLORENTINO, Bruno R.; BONIDIA, Robson P.; CARVALHO, André C. P. L. F. de. Breaking Barriers: Democratizing Machine Learning for RNA-Protein Interaction Prediction in Life Sciences. In: CONCURSO DE TRABALHOS DE INICIAÇÃO CIENTÍFICA - SIMPÓSIO BRASILEIRO DE COMPUTAÇÃO APLICADA À SAÚDE (SBCAS), 24. , 2024, Goiânia/GO. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2024 . p. 7-12. ISSN 2763-8987.
[Link][IF 2022: 6.000] Florentino, B. R., Bonidia, R. P., Sanches, N. H., da Rocha, U. N., & de Carvalho, A. C. BioPrediction-RPI: Democratizing the Prediction of Interaction Between Non-Coding RNA and Protein with End-to-End Machine Learning. Computational and Structural Biotechnology Journal, 2024.
[Link][IF 2022: 4.100] AVILA SANTOS, ANDERSON P.; DE ALMEIDA, BRENO L. S.; BONIDIA, ROBSON P.; STADLER, PETER F.; STEFANIC, POLONCA; MANDIC-MULEC, INES; ROCHA, ULISSES; SANCHES, DANILO S.; DE CARVALHO, ANDRÉ C.P.L.F. BioDeepfuse: a hybrid deep learning approach with integrated feature extraction techniques for enhanced non-coding RNA classification. Rna Biology, v. 21, p. 1-12, 2024.
[Link][IF 2022: 7.700] ROCHA, Ulisses et al. MuDoGeR: Multi‐Domain Genome recovery from metagenomes made easy. Molecular Ecology Resources, v. 24, n. 2, p. e13904, 2024.. Rna Biology, v. 21, p. 1-12, 2024.
[Link][Conference] BONIDIA, R. P.; SANTOS, A. P. A.; ALMEIDA, B. L. S.; STADLER, P.; ROCHA, U. N.; SANCHES, D. S.; CARVALHO, A. C. P. L. F. BioAutoML: End-to-End Machine Learning Package for Life Sciences. In: 10th FEMS Congress of European Microbiologists, 2023, Hamburg - Germany. 10th FEMS Congress of European Microbiologists, 2023.
[Link][Conference] FLORENTINO, B. R.; SANCHES, N. H.; BONIDIA, R. P.; DE CARVALHO, ANDRÉ C. P. L. F. BioPrediction: Democratizing Machine Learning in the Study of Molecular Interactions. In: XX Encontro Nacional de Inteligência Artificial e Computacional, 2023, Belo Horizonte - MG. Anais do XX Encontro Nacional de Inteligência Artificial e Computacional, 2023. p. 525-539.
[Link][IF 2021: 13.994] BONIDIA, ROBSON P; SANTOS, ANDERSON P AVILA; DE ALMEIDA, BRENO L S; STADLER, PETER F; DA ROCHA, ULISSES N; SANCHES, DANILO S; DE CARVALHO, ANDRÉ C P L F. BioAutoML: automated feature engineering and metalearning to predict noncoding RNAs in bacteria. Briefings in Bioinformatics, v. 1, p. 1-13, 2022.
[Link][IF 2021: 2.738] BONIDIA, ROBSON P; SANTOS, ANDERSON P AVILA; DE ALMEIDA, BRENO L S; STADLER, PETER F; DA ROCHA, ULISSES N; SANCHES, DANILO S; DE CARVALHO, ANDRÉ C P L F. Information Theory for Biological Sequence Classification: A Novel Feature Extraction Technique Based on Tsallis Entropy. Entropy, v. 24, p. 1398, 2022.
[Link][IF 2021: 13.994] BONIDIA, ROBSON P; DOMINGUES, DOUGLAS S; SANCHES, DANILO S; DE CARVALHO, ANDRÉ C P L F. MathFeature: feature extraction package for DNA, RNA and protein sequences based on mathematical descriptors. Briefings in Bioinformatics, v. 1, p. 1-10, 2022.
[Link][IF 2020: 11.622] ALKHNBASHI, OMER S; MITROFANOV, ALEXANDER; BONIDIA, ROBSON; RADEN, MARTIN; TRAN, VAN DINH; EGGENHOFER, FLORIAN; SHAH, SHIRAZ A; ÖZTÜRK, EKREM; PADILHA, VICTOR A; SANCHES, DANILO S; DE CARVALHO, ANDRÉ C P L F; BACKOFEN, ROLF. CRISPRloci:comprehensive and accurate annotation of CRISPR-Cas systems. NUCLEIC ACIDS RESEARCH, v. 1, p. gkab456, 2021.
[Link][IF 2020: 11.622] BONIDIA, ROBSON P; SAMPAIO, LUCAS D H; DOMINGUES, DOUGLAS S; PASCHOAL, ALEXANDRE R; LOPES, FABRÍCIO M; DE CARVALHO, ANDRÉ C P L F; SANCHES, DANILO S. Feature extraction approaches for biological sequences: a comparative study of mathematical features. Briefings in Bioinformatics, v. 00, p. 1-20, 2021.
[Link][IF 2019: 3.745] BONIDIA, ROBSON P.; MACHIDA, JAQUELINE SAYURI; NEGRI, TATIANNE C.; ALVES, WONDER A. L.; KASHIWABARA, ANDRE Y.; DOMINGUES, DOUGLAS S.; DE CARVALHO, ANDRE C.P.L.F.; PASCHOAL, ALEXANDRE R.; SANCHES, DANILO S. A Novel Decomposing Model with Evolutionary Algorithms for Feature Selection in Long Non-Coding RNAs. IEEE Access, v. 1, p. 1-15, 2020.
Contact
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