BioPrediction Framework addresses the challenge of extracting insights from growing biological sequence databases across sectors like health and agriculture. Given the complexity of biological sequences, this end-to-end automated Machine Learning (ML) framework detects implicit molecular interactions without needing researchers to master ML. With three primary objectives - accurate molecular interaction prediction, user-friendly ML tool development for biologists, and enhancing ML interpretability - the project aims to revolutionise research in protein interactions, metabolic mapping, and pharmacology, promising significant societal impacts.
Natan is a brazilian software developer and undergraduate researcher, pursuing a B. Sc. degree in Computer Science at University of São Paulo. He’s more experienced in the fields of Computer Vision and Robotics, where he works as R&D Software Engineer, also with strong participation in Bioinformatics and Data Science research, his main area as undergraduate researcher. As a researcher, Natan aims to provide better solutions to the problems he’s most interested in. He also gets along with everything that involves Mathematics and Computer Science, loving to teach and talk about his favorite topics. more
I am a Brazilian bioinformatics researcher, pursuing a degree in physical and biomolecular sciences at the University of São Paulo. My background includes a strong foundation in mathematics, physics, and specific knowledge in molecular biology, coupled with a deep interest in data science. My research has a dual focus. Firstly, I aim to create machine learning tools that benefit biological science researchers. Additionally, I seek to merge traditional computational molecular biology approaches with machine learning tools. This integration aims to strengthen the field of research by harmonizing these two knowledge domains.more
I'm a Ph.D. candidate in Computer Science and Computational Mathematics at the University of São Paulo, Brazil, with expertise in AI, bioinformatics, and data science. I aim to create AI solutions that benefit society, particularly in low- and middle-income countries. My work on democratizing AI for biology has received awards, including the Google Latin America Research Awards in 2021 and recognition in global competitions. My projects also focus on AI education and combating fake news.morePortfolio
Full Professor since 2006 and currently Director since 2022 of the Institute of Mathematical and Computer Sciences at the University of São Paulo (ICMC-USP), São Carlos Campus, Research Productivity Fellow 1A of CNPq. He coordinates the IARA network, Artificial Intelligence Recreating Environments. He leads the WG12.2 Working Group on Machine Learning and Data Mining of the International Federation for Information Processing (IFIP). He served as Vice-President of the Brazilian Computer Society (SBC) from 2019 to 2023, member of the CNPq Computer Science Advisory Committee (CA-CC) from 2018 to 2021 (coordinator from 2019 to 2020). From 2013 to 2017 he was a member of the council of the International Association for Statistical Computing (IASC), of the International Statistical Institute (ISI). His main research interests are Machine Learning, Data Mining, and Data Science, with applications in various fields. He has published several papers in these areas, some of which have been awarded at conferences organized by ACM, IEEE, and SBC. He has written several books, including "Artificial Intelligence: A Machine Learning Approach", published by GrupoGen in 2011 and winner of the Jabuti Award 2012, and "A General Introduction to Data Analytics", published by Wiley in 2018. morePortfolio LinkedIn
AI tool for rapid segmentation in microscopic image analysis