Anderson Paulo Avila Santos


Anderson Paulo Avila Santos
PhD student

Working Group Microbial Data Science

Department of Environmental Microbiology
Helmholtz Centre for Environmental Research - UFZ
Permoserstr. 15, 04318 Leipzig, Germany

Phone +55 43 99151 4186

Anderson Paulo Avila Santos

Scientific Career

02/2020 present

PhD student - Computer Science at Institute of Mathematical and Computer Sciences – ICMC, University of Sao Paulo – USP, Sao Carlos, Brazil.

03/2015 07/2017

MSc Degree - Computer Science at State University of Londrina - USP - Londrina, Parana, Brazil.

02/2007 12/2010

BSC Degree - BSc Degree in Information System at Pontifical Catholic University of Parana, Londrina, Parana, Brazil

Research interests

I am currently pursuing my Ph.D. in Computer Science and Computational Mathematics at the esteemed Institute of Mathematical and Computer Sciences (ICMC) of the University of São Paulo (USP) in Brazil. Additionally, I am honored to serve as a visiting researcher at Germany's renowned Helmholtz-Zentrum für Umweltforschung.

My primary research emphasis is on leveraging artificial intelligence (AI) to address sophisticated biological challenges. I'm dedicated to making AI accessible to all, adhering strictly to the FAIR principles (Findable, Accessible, Interoperable, and Reusable).

My academic foundation was laid at the Pontifical Catholic University of Paraná (PUC/PR) where I studied Information Systems. Later, I specialized in Software Engineering and Databases at the State University of Londrina (UEL) and further honed my skills in AI applied to Industry at SENAI Londrina. My dedication to the field was cemented with a Master's in Computer Science from UEL.

Since 2018, I've been imparting my knowledge in higher education, instructing in Software Engineering and specialized AI programs. This journey has fortified my expertise in AI, its application in biological contexts, and the nuances of complex data analysis.


  • Avila Santos, A. P., Kabiru Nata’ala, M., Kasmanas, J. C., Bartholomäus, A., Keller-Costa, T., Jurburg, S. D., ... & Rocha, U. (2023). The AnimalAssociatedMetagenomeDB reveals a bias towards livestock and developed countries and blind spots in functional-potential studies of animal-associated microbiomes. Animal Microbiome, 5(1), 1-13.
  • Bonidia, R. P., Santos, A. P. A., de Almeida, B. L., Stadler, P. F., da Rocha, U. N., Sanches, D. S., & de Carvalho, A. C. (2022). BioAutoML: automated feature engineering and metalearning to predict noncoding RNAs in bacteria. Briefings in Bioinformatics, 23(4), bbac218.
  • Bonidia, R. P., Avila Santos, A. P., de Almeida, B. L., Stadler, P. F., Nunes da Rocha, U., Sanches, D. S., & De Carvalho, A. C. (2022). Information theory for biological sequence classification: A novel feature extraction technique based on Tsallis entropy. Entropy, 24(10), 1398.
  • Nata’ala, M. K., Avila Santos, A. P., Coelho Kasmanas, J., Bartholomäus, A., Saraiva, J. P., Godinho Silva, S., ... & Nunes da Rocha, U. (2022). MarineMetagenomeDB: a public repository for curated and standardized metadata for marine metagenomes. Environmental Microbiome, 17(1), 57.
  • de Almeida, B. L. S., Queiroz, A. P., Santos, A. P. A., Bonidia, R. P., da Rocha, U. N., Sanches, D. S., & de Carvalho, A. C. P. D. L. F. (2021). Feature importance analysis of non-coding dna/rna sequences based on machine learning approaches. In Advances in Bioinformatics and Computational Biology: 14th Brazilian Symposium on Bioinformatics, BSB 2021, Virtual Event, November 22–26, 2021, Proceedings 14 (pp. 81-92). Springer International Publishing.