Recent technological advances have allowed an exponential expansion of biological sequence data, and the extraction of meaningful information through Machine Learning (ML) algorithms. This knowledge improved the understanding of the mechanisms related to several fatal diseases, e.g., Cancer and COVID-19, helping to develop innovative solutions, such as CRISPR-based gene editing, coronavirus vaccine, and precision medicine. These advances benefit our society and economy, directly impacting people’s lives in various areas, such as health care, drug discovery, forensic analysis, and food analysis. Nevertheless, ML approaches to biological data require representative, quantitative, and informative features. Necessarily, as many ML algorithms can handle only numerical data, sequences need to be translated into a feature vector. This process, known as feature extraction, is a fundamental step for elaborating high-quality ML-based models in bioinformatics, by allowing the feature engineering stage, with the design and selection of suitable features. Feature engineering, ML algorithm selection, and hyperparameter tuning are often time-consuming processes that require extensive domain knowledge and are performed by a human expert. To deal with this problem, we developed a new package, BioAutoML, which automatically runs an end-to-end ML pipeline. BioAutoML extracts numerical and informative features from biological sequence databases, automating feature selection, recommendation of ML algorithm(s), and tuning of hyperparameters, using Automated ML (AutoML). Our experimental results demonstrate the robustness of our proposal across various domains, such as SARS-CoV-2, anticancer peptides, HIV sequences, and non-coding RNAs. BioAutoML has a high potential to significantly reduce the expertise required to use ML pipelines, aiding researchers in combating diseases, particularly in low- and middle-income countries. This initiative can provide biologists, physicians, epidemiologists, and other stakeholders with an opportunity for widespread use of these techniques to enhance the health and well-being of their communities.

[Link][Conference] 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][Conference] 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][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][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.

BioAutoML: Documentation
MathFeature: Documentation
BioDeepFuse: Documentation