News
About Us
Our main objective is to create AutoAI-Pandemics, a simple and integrated platform that allows non-experts, including biologists, physicians, epidemiologists and other stakeholders, to apply data science and machine learning techniques effectively.
This platform will deliver different solutions, including:
- Enabling the development of AI solutions for health data without requiring programming expertise;
- Monitoring and predicting diseases;
- Supporting bioinformatics analysis to assist health scientists;
- Combating misinformation by facilitating access to reliable information 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
Read more Access solutionBioPrediction-PPI
End-to-End Machine Learning to to Predict Protein-Protein Interactions.
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 v1: Your Information Integrity Assistant - Experimental Phase - Portuguese or English.
Read more Access solutionITT- Is That True?
Dominique v2: Your Information Integrity Assistant - Portuguese - Trained in 20,000 news.
Read more Access solutionDiz Aí, Verdade?
Empowering Portuguese Speakers With Digital Literacy and Real-Time Fact-Checking - Launching soon!
Read more Access solutionLive Map
Our Chat for Reliable Information on Brazilian Public Health - Launching soon!
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 materials 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
Conic-Semesp Award
ConsCiêncIA Project (Literacy in Artificial Intelligence), recognized as the best ongoing research project in the field of Humanities and Applied Social Sciences in Brazil for undergraduate scientific initiation by Conic-Semesp (the Largest Scientific Initiation Congress in Brazil), among more than 1,400 registered projects, receiving R$1,500.
[Link-1] [Link-2] [Link-3]Prototypes for Humanity 2025
For the third consecutive year, one of our students has been selected by Prototypes for Humanity — this time, ChemAutoML, a project for drug discovery. From over 3,300 entries across 100+ countries, it was chosen among the top 100 innovations in the world.
[Link-1] [Link-2]CAPES Thesis Award (2025)
Honorable Mention. Recognized as one of the best doctoral theses in Brazil in the field of Computer Science and Mathematics. CAPES is Brazil’s top national award for PhD theses (Honorable Mention indicates recognition among the best theses in the country).
[Link-1]HundrED Global Collection 2026
InteliGente has been selected for the HundrED Global Collection 2026, placing us among the most impactful and scalable innovations in education worldwide. InteliGente stood out among 800+ global innovations reviewed by 250 experts, being recognized as one of the world’s most impactful.
[Link-1]Brazilian Society of Endocrinology and Metabolism
We are co-authors of the scientific paper awarded first place at the 10th Paraíba Congress of Endocrinology and Metabolism — Machine Learning in Central Adrenal Insufficiency, organized by the Brazilian Society of Endocrinology and Metabolism – Paraíba Regional.
[Link-1]V Academic Recognition Award in Human Rights
We received the V Academic Recognition Award in Human Rights, granted by the University of Campinas (Unicamp) and the Vladimir Herzog Institute, in the category Exact Sciences, Engineering, and Technology – Doctorate. The award recognizes research that makes a significant contribution to the promotion of human rights through social impact, innovation, and scientific relevance.
[Link-1] [Link-2]Jabuti Academic Award 2025
Our book is a finalist (5 books) for the Jabuti Academic Award, recognized among the best in Brazil in the field of computer science in 2024. The Jabuti is the most prestigious literary prizes in Brazil, celebrating excellence in academic and literary production across the country.
[Link-1] [Link-2] [Link-3]USP Outstanding Thesis Award 2025
Dr. Bonidia and Dr. Carvalho are winners of the USP Outstanding Thesis Award 2025 (Prêmio Tese Destaque USP 2025), granted to the best doctoral thesis of the year in Exact and Earth Sciences by the University of São Paulo. This award recognizes research of excellence and high academic impact, and is one of the most prestigious distinctions from Brazil's leading public university and a reference in Latin America.
[Link-1]AI4PEP, IDRC, and the UK International Development
AutoAI-Pandemics was the only initiative from our country (Brazil) selected among the top 21 ideas in a prestigious global call promoted by AI4PEP, IDRC, and the UK International Development. The project received a grant of CAD 206,400 to advance its mission of using AI for pandemic preparedness and equity in the Global South.
[Link-1] [Link-2]AI for Global Health Innovation
Our Students Participated in the Global South AI4PEP "AI for Global Health Innovation" Challenge and Won Some Prizes - Third Place
[Link-1]AI for Global Health Innovation
Our Students Participated in the Global South AI4PEP "AI for Global Health Innovation" Challenge and Won Some Prizes - Honorable Mention
[Link-1]Global GESS Education Awards
Finalist at the GESS Education Awards 2024 (Dubai) – Innovation in Education Category. Selected as a global finalist in one of the world's most prestigious education ceremonies, standing out among more than 900 entries from 60 countries. This recognition validates the development and implementation of high-impact educational solutions, placing our work among the top international innovators.
[Link-1] [Link-2] [Link-3]Global Innovation Challenge 2024 (Social Shifters)
Global Finalist — Social Shifters Global Innovation Challenge 2024 (JPMorganChase) - Selected as finalist among 5,186 teams from 122 countries, in a global challenge that supported the development of 2,840 social impact solutions with the involvement of 2,180 volunteers worldwide.
[Link-1] [Link-2]LED Prize
InteliGente has been selected for the next phase of the LED Prize 2025 (chosen from a total of 2,041 entries), an initiative by Globo and the Roberto Marinho Foundation, which aims to recognize innovative initiatives that are driving transformations in Brazilian education.
[Link-1] [Link-2] [Link-3]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
Troy Day
Marcia Castro
John Edmunds
Anderson Santos
Breno de Almeida
Bruno Florentino
Natan Sanches
Denis Tavares
João Lucas Rodrigues
Recent News Posts
News
InteliGente, UTFPR, is the only public institution in Brazil to have a project selected by HundrED.
News
Our book is recommended as Six Must-Reads in Data Science and Artificial Intelligence for 2025.
News
Image Super-Resolution with Guarantees via Conformal Generative Models - Article available on arXiv
News
Dominique is featured in IDRC publication on Strengthening health systems through responsible AI.
News
AutoAI-Pandemics at the HSR Global Symposium on Health Systems Research - Dr Guilherme Goedert.
News
AutoAI-Pandemics at the HSR Global Symposium on Health Systems Research - Dr Guilherme Goedert.
News
AutoAI-Pandemics at the HSR Global Symposium on Health Systems Research - Dr Guilherme Goedert.
News
AutoAI-Pandemics - AI4PEP - AI research guided at ICMC/USP will represent Brazil at COP-28, in Dubai
![[Link-3]](https://ai4pep.org/wp-content/uploads/2023/07/Projects-2048x1707.png){kind=link}
News
AutoAI-Pandemics
Frequently Asked Questions
Health is one of the areas of highest scientific and social concern. Despite significant advances in diagnostics and treatments, important challenges persist in monitoring health, personalizing medical treatment, drug discovering, and combating misinformation. Artificial Intelligence (AI), specifically Machine Learning (ML), can provide tools to address these challenges, given their past contributions, e.g., during the COVID-19 pandemic. Despite these advances, the effective use of ML remains limited by technical barriers that restrict its broader adoption. Developing robust models requires skills in programming, feature engineering, and handling large-scale datasets, competencies that are not widely available among health professionals. This gap contributes to asymmetries in access to technology and constrains its integration in settings with limited technical capacity. Moreover, most AI tools are trained in Global North datasets, producing contextual biases and reduced precision in the Global South. To overcome this limitation, we created a software hub that brings together multiple solutions based on ML and can be effectively used by healthcare professionals who are not ML experts. This Hub, named AutoAI-Pandemics, provides end-to-end ML solutions for: (T1) monitoring and predicting diseases; (T2) enabling the development of AI solutions for health data without requiring programming expertise; (T3) supporting bioinformatics analysis to assist life scientists; and (T4) combating misinformation by facilitating access to reliable information sources. AutoAI-Pandemics have the potential to revolutionize health data analysis, making advanced AI tools more accessible to several communities worldwide and unlocking new opportunities for innovation across diverse subareas of health sciences.
(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) Monitoring and predicting diseases;
(T2) Enabling the development of AI solutions for health data without requiring programming expertise;
(T3) Supporting bioinformatics analysis with AI to assist life scientists;
(T4) Combating misinformation by facilitating access to reliable information 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 project is coordinated by Dr. André de Carvalho (PI), Universidade de São Paulo (USP); and Dr. Robson Bonidia (Co-PI), Universidade Tecnológica Federal do Paraná (UTFPR).
Over the years, our project has achieved large-scale visibility through structured programs, research, digital platforms, educational materials, awards, media coverage, and social media outreach. To ensure transparency and methodological rigor, our impact assessment follows an evidence-based framework that distinguishes between direct impact and indirect public reach, applying conservative conversion and overlap-correction factors.
-
Direct Impact:
refers to people who engaged with the project through formal participation, educational interaction, or use of our tool/resources.
This includes:- more than 2,200 people with confirmed individual records, including 782 children from the Tampinha Mágica initiative, 346 educators in national surveys, 100+ students formally involved in long-term projects, and over 1,000 unique participants in lectures, workshops, and training courses;
- digital engagement, including more than 50,000 article reads across scientific platforms and over 10,000 unique users in our pages;
- distribution and use of materials, including 1,633 printed books, 487 e-books, and more than 65,500 pages accessed through the MinhaBiblioteca academic platform;
- pedagogical replication through open educational resources, games, and project-based methodologies, enabling users to disseminate the materials and activities to new learners.
-
Indirect Reach (visibility and awareness):
refers to people exposed to the project through social media, institutional dissemination, academic circulation, awards, and media coverage.
This includes:- 1,079,419+ social media impressions across platforms, converted into estimated unique reach using conservative uniqueness factors;
- 50+ news articles published in national and international outlets such as Globo, Band, Olhar Digital, Veja, IDRC, UTFPR, USP, AI4PEP, among others;
- continuous amplification generated by 40+ national and international awards and recognitions.
- Methodological approach: This impact is based in data from Google Analytics, educational records, platform logs, social media reports, and public media coverage. Conservative conversion factors and overlap corrections are applied to prevent double counting and overestimation, following practices commonly used in open-science initiatives.
[Article][Preprint] FLORENTINO, Bruno R.; BONIDIA, Robson et al. BioPrediction-PPI: Simplifying the Prediction of Protein-Protein actions through Artificial Intelligence. bioRxiv, p. 2025.11. 16.688401, 2025.
[Article][Preprint] FLORENTINO, Bruno; Bonidia, Robson et al. Artificial Intelligence for All? Brazilian Teachers on Ethics, Equity, and the Everyday Challenges of AI in Education. arXiv preprint arXiv:2512.23834, 2025.
[Article][Conference] BRAZ, P. B.; BATISTA, J. M.; BONIDIA, ROBSON P. ConsCiêncIA: Um Caminho Lúdico para o Ensino Crítico e Ético de Inteligência Artificial nas Escolas Públicas. In: Conic-Semesp - 25 Congresso Nacional de Iniciação Científica, 2025, Online.
[Article][Conference] MATTOS, I. A.; BONIDIA, ROBSON P. Aletheia: Detecção de Fake News com Modelos de Linguagem que Raciocinam. In: Conic-Semesp - 25 Congresso Nacional de Iniciação Científica, 2025, Online.
[Article][Conference] SALES, J. P. M.; CARVALHO, A. C. P. L. F.; BONIDIA, ROBSON. SynapSpeech - Escutando os Primeiros Sinais do Alzheimer. In: Conic-Semesp - 25 Congresso Nacional de Iniciação Científica, 2025, Online.
[Article][Conference] MARTINS, M. V.; CASSIMIRO, J. P. S.; ADMERTIDES, Y. S.; OUTSUK, T.; BONIDIA, ROBSON. Cartas do Fado: Ética e Moral em Jogo. In: Conic-Semesp - 25 Congresso Nacional de Iniciação Científica, 2025, Online.
[Article][Conference] BATISTA, J. M.; BRAZ, P. B.; BONIDIA, ROBSON P. ConsCiêncIA: Um Caminho Lúdico para o Ensino Crítico e Ético de Inteligência Artificial nas Escolas Públicas. In: XV Seminário de Extensão e Inovação & XXX Seminário de Iniciação Científica e Tecnológica da UTFPR, 2025, Curitiba.
[Article][IF 2024: 5.200] ROCHA, ULISSES; BONIDIA, ROBSON; et al. Democratising Artificial Intelligence for Pandemic Preparedness and Global Governance in Latin American and Caribbean Countries.Microbial Biotechnology (Online), v. 18, p. 01, 2025.
[Article][IF 2024: 3.100] RECHIA BITENCOURT, MARIANA; COSTA ARAÚJO, FERNANDO; PEIXOTO BISCOTTO, ISABELA; DO NASCIMENTO, VICTOR H; CARVALHO, ANDRÉ; BONIDIA, ROBSON; SILVEIRA DE CARVALHO, LUCIANI RENATA. Diagnostic Utility Of Machine Learning In Central Adrenal Insufficiency Due To Pituitary Disorders. Journal Of The Endocrine Society, v. 9, p. 01, 2025.
[Report] Bonidia et al. Artificial Intelligence in Basic Education: Teachers' Perceptions and Challenges 2025.
[Link] KASMANAS, Jonas Coelho et al. Integrating comparative genomics and risk classification by assessing virulence, antimicrobial resistance, and plasmid spread in microbial communities with gSpreadComp. GigaScience, v. 14, p. giaf072, 2025.
[Poster] BITENCOURT, M. R.; ARAUJO, F. C.; BISCOTTO, I. P.; NASCIMENTO, V. H. ; CARVALHO, A. C. P. L. F.; BONIDIA, R. P.; CARVALHO, L. R. S. Diagnostic Utility of Machine Learning in Central Adrenal Insufficiency due to Congenital Hypopituitarism and Other Pituitary Disorders. In: ENDO 2025, 2025, San Francisco, CA. ENDO 2025, 2025.
[Poster] BATISTA FILHO, A. G.; VILARINHO, L.; BITENCOURT, M.; ARAUJO, F.; BISCOTTO, I.; NASCIMENTO, V.; CARVALHO, A. C. P. L. F.; BONIDIA, R. P.; CARVALHO, L. Utilidade Diagnóstica do Aprendizado de Máquina na Insuficiência Adrenal Central por Hipopituitarismo Congênito e Outras Doenças Hipofisárias. In: X Congresso Paraibano de Endocrinologia e Metabologia, 2025, Paraíba. X Congresso Paraibano de Endocrinologia e Metabologia, 2025.
[Poster] ALMEIDA, B. L. S.; KAMATH, S. ; SANTOS, A. A.; BONIDIA, R. P.; ESPINDOLA-HERNANDEZ, P.; STADLER, P.; OLIVEIRA, A.; CARVALHO, A.; ROCHA, U. N. Exploration of multiple protein language models and over 16 million open reading frames recovered from thousands of terrestrial metagenomes uncover protein domains and families in 13,486 previously unknown orthologs. In: 17th Symposium on Bacterial Genetics and Ecology, 2025, Graz, Austria. 17th Symposium on Bacterial Genetics and Ecology, 2025.
[Link] CERQUIARE, Felipe Alves; BONIDIA, Robson Parmezan; DE OLIVEIRA SESTITO, Camila Dias. The Era of AI in Education: exploring potentials and challenges. Dialogia, n. 54, p. e28410-e28410, 2025.
[Award] Bonidia et al. InteliGente: Building Paths of Equality with Artificial Intelligence in the Global South Article in LACNIC Blog, 2025.
[Think Tanks] Bonidia et al. InteliGente: Building Paths of Equality with Artificial Intelligence Article in Southern Voice Org. 2025.
[Report] DIGNUM, Virginia et al. Roadmap for AI Policy Research. 2025.
[Report] BENGIO, Yoshua et al. International AI Safety Report. arXiv preprint arXiv:2501.17805, 2025.
[Preprint] CSILLAG, Daniel; STRUCHINER, Claudio José; GOEDERT, Guilherme Tegoni. Prediction-Powered E-Values. arXiv preprint arXiv:2502.04294, 2025.
[Preprint] GENARI, Juliano; GOEDERT, Guilherme Tegoni. Mining Unstructured Medical Texts With Conformal Active Learning. arXiv preprint arXiv:2502.04372, 2025.
[Preprint] ADAME, Eduardo; CSILLAG, Daniel; GOEDERT, Guilherme Tegoni. Image Super-Resolution with Guarantees via Conformal Generative Models. arXiv preprint arXiv:2502.09664, 2025.
[Poster] Almeida; Bonidia et al., AutoAI-Pandemics: Democratizing Machine Learning for Analysis, Study, and Control of Epidemics and Pandemics, Poster presented at Health Systems Research 2024.
[Poster] R. Bonidia et al., AutoAI-Pandemics: Democratizing Machine Learning for Analysis, Study, and Control of Epidemics and Pandemics, Presented at Helmholtz AI, Germany, 2024.
[Preprint] CSILLAG, Daniel; STRUCHINER, Claudio José; GOEDERT, Guilherme Tegoni. Strategic Conformal Prediction. arXiv preprint arXiv:2411.01596, 2024.
[Preprint] DE CARVALHO, Andre et al; Bonidia, Robson et al; Democratising Artificial Intelligence for Pandemic Preparedness and Global Governance in Latin American and Caribbean Countries. arXiv preprint arXiv:2409.14181, 2024.
[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.
![[Poster]](figures/news/114.png){kind=link}
Contact
Location:
Universidade de São Paulo - Av. Trab. São Carlense, 400 - Centro, São Carlos - SP, 13566-590, Brazil
Call:
+55 16 3373-9700
Open Hours:
Mon-Fri: 08AM - 23PM