Our work:

Through the eyes of Computational Sciences
Through the eyes of Biological Sciences

Want to join the party? Master and PhD positions

Candidates should be highly motivated and have strong academic backgrounds in Computer Science or Bioinformatics and excellent writing skills including documentation skills to maintain software and support documentation. A high degree of energy, accuracy, and attention to detail, and a passion for science. The students will participate in multidisciplinary studies focusing on the design of novel methods and computational strategies for Bioinformatics problems. Interested students are encouraged to contact Prof. Dorn (mdorn@inf.ufrgs.br ) with a short introduction of her/his academic backgrounds, research interests, and career goal. Students wishing to complete a Masters or Ph.D. degree in Computer Science are enrolled in the Postgraduate Program in Computing (PPGC). Students who want to complete a Master or Ph.D. in Molecular and Cell Biology are registered in the Postgraduate Program in Molecular and Cell Biology (PPGBCM). The candidate may hold a scholarship or may receive financial support from some other source.

Lastest Publications

Perspectives and applications of machine learning for evolutionary developmental biology

Feltes, B.C.; Grisci, B.I.; Poloni, J.F.; Dorn, M. Perspectives and Applications of Machine Learning for Evolutionary Developmental Biology. Molecular BioSystems, v. 14, p. 289-306, 2018.

Evolutionary Developmental Biology (Evo-Devo) is an ever-expanding field that aims to understand how development was modulated by the evolutionary process. In this sense, “omic” studies emerged as a powerful ally to unravel the molecular mechanisms underlying development. In this scenario, bioinformatics tools become necessary to analyze the growing amount of information. Among computational approaches, machine learning stands out as a promising field to generate knowledge and trace new research perspectives for bioinformatics ... more

CuMiDa: An Extensively Curated Microarray Database for Benchmarking and Testing of Machine Learning Approaches in Cancer Research

Feltes, B.C.; Grisci, B.I.; Chandelier, E.B..; Dorn, M. CuMiDa: An Extensively Curated Microarray Database for Benchmarking and Testing of Machine Learning Approaches in Cancer Research. Journal of Computational Biology, Ahead of Print, 2019.

The employment of machine learning (ML) approaches to extract gene expression information from microarray studies has increased in the past years, specially on cancer-related works. However, despite this continuous interest in applying ML in cancer biomedical research, there are no curated repositories focused only on providing quality data sets exclusively for benchmarking and testing of such techniques for cancer research. Thus, in this work, we present the Curated Microarray Database (CuMiDa), a database composed of 78 handpicked microarray data sets for Homo sapiens that were carefully examined from more than 30,000 microarray experiments from the Gene Expression Omnibus using a rigorous filtering criteria ...more

Neuroevolution as a Tool for Microarray Gene Expression Pattern Identification in Cancer Research

Grisci, B.I.; Feltes, B.C.; Dorn, M. Neuroevolution as a Tool for Microarray Gene Expression Pattern Identification in Cancer Research. Journal of Biomedical Informatics, v. 89, p. 122-133, 2019.

Microarrays are still one of the major techniques employed to study cancer biology. However, the identification of expression patterns from microarray datasets is still a significant challenge to overcome. In this work, a new approach using Neuroevolution, a machine learning field that combines neural networks and evolutionary computation, provides aid in this challenge by simultaneously classifying microarray data and selecting the subset of more relevant genes. The main algorithm, FS-NEAT, was adapted by the addition of new structural operators designed for this high dimensional data ... more