AutoAI-Pandemics

Democratizing Machine Learning for Analysis, Study, and Control of Epidemics and Pandemics



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.

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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.


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BioPrediction

Democratizing Machine Learning in the Study of Molecular Interactions



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MathFeature

Feature Extraction Package for Biological Sequences Based on Mathematical Descriptors.



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ITT- Is That True?

Fake News Detector (Google Chrome) - Portuguese or English: We consider it extremely important to emphasize that this version is not yet ready; it is only in the experimental phase.



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BioDeepFuse

Empowering Researchers in Life Sciences with Deep Learning.



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BioPrediction-RPI

Democratizing the Prediction of Interaction Between Non-Coding RNA and Protein with End-to-End Machine Learning.



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ChemAutoML

Democratizing Cheminformatics - Launching soon!



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Accesses have been achieved by our solutions and articles

Stars on GitHub have been awarded to our solutions

Citations in academic papers have been earned by our studies

Data science books sold to democratize the use of AI tools

News published in national and international media

Awards have been earned by our projects

Our Projects

Projects with the Community

InteliGente (Former BioFatecou)

Building Paths of Equality with Artificial Intelligence.


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Data Science: Fundamentals and Applications

First book aimed at democratizing data science for non-experts


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I Advanced Summer School on Responsible AutoML

April 18th to 19th, 2024


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Awards

Recognitions and Awards

Transformer Educator Award

InteliGente was named the third most transformative educational project in the State of São Paulo (Brazil) by the Transformative Educator Award, 2024.


See more: [Link-1] [Link-2]

AI4PEP 2023

AutoAI-Pandemics was selected as one of the most promising proposals (a total of 221 proposals from 47 countries following a rigorous review process) in a global competition - CAN$362,500.


See more: [Link-1] [Link-2] [Link-3] [Link-4]

Prototypes for Humanity

BioPrediction — Project selected to participate in Prototypes for Humanity 2023, during COP28-Dubai, chosen from 3000 entries, from more than 100 countries, standing out among the 100 best of the world.


See more: [Link-1] [Link-2] [Link-3]

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.


See more: [Link-1] [Link-2] [Link-3] [Link-4]

Falling Walls Lab Brazil 2023

Falling Walls Lab Brazil awards our platform that uses machine learning to combat fake news


See more: [Link-1] [Link-2] [Link-3] [Link-4]

Transformer Educator Award

Finalist in the Higher Education Category (Among the 10 finalists in 2897 subscribers - BioFatecou Project), which aims to select the most transformative projects in Brazil, 2023.


See more: [Link-1] [Link-2]

Top Educational Award

Honorable mention to the InteliGente (Former BioFatecou), 2023 - Elected among the 5 most transformative projects in Brazil.


See more: [Link-1] [Link-2] [Link-3]

Artificial Intelligence Prize

Project to empower researchers in life sciences wins J.F. Marar Artificial Intelligence Prize for Undergraduate Studies


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Falling Walls Lab Brasil 2023

Two projects connected to AutoAI-Pandemias are among the 15 finalists of Falling Walls Lab Brasil 2023.


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Google Latin America Research Awards

BioAutoML was elected by LARA-Google among the 24 most promising ideas in Latin America (24 awarded projects, from a base of 700 submissions).


See more: [Link-1] [Link-2] [Link-3] [Link-4]

Our Team

André de Carvalho

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Robson Bonidia

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Claudio Struchiner

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Ulisses da Rocha

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Guilherme Goedert

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Peter Stadler

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Danilo Sanches

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Maria Emília Walter

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Troy Day

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Marcia Castro

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John Edmunds

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Anderson Santos

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Breno de Almeida

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Bruno Florentino

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Natan Sanches

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Denis Tavares

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João Lucas Rodrigues

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Recent News Posts

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.

[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] 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

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

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