Short Bio

I am an Associate Professor (Professor Adjunto) at the Institute of Informatics (INF) of Universidade Federal do Rio Grande do Sul (UFRGS), Brazil, and a Research Professor at Hospital de Clínicas de Porto Alegre (HCPA), where I coordinate the Bioinformatics Core (Núcleo de Bioinformática). I am a supervisor in the Postgraduate Program in Computing (PPGC)/UFRGS and a CNPq Research Productivity Fellow Level 2 (PQ-2).

My research areas are Machine Learning, Bioinformatics, and Computational Biology. My main scientific goal is to develop and apply state-of-the-art computational techniques (mainly machine learning algorithms) to solve Medicine- and Biology-related problems, particularly those appearing in the post-genomic era (you can read more about it here).

"Most exciting research is happening at the interface of the disciplines". My research is highly interdisciplinary. Thus, despite my goal in drawing new advances in Machine Learning, I have a strong focus in furthering its application in Medicine and Biology to solve challenging and significant problems related to Health and Genomics.

Interested in a collaboration, research supervision, or simply discussing some ideas? You are welcome to contact me.

Current research topics

Ongoing research projects are focusing, primarily, in the following topics:

  • Clinical machine learning
  • Computational strategies for integrative analysis of multi-omics data
  • Computational prediction and analysis of microRNAs' targets
  • Detection of gene expression patterns and regulatory networks associated to complex human diseases
  • Ensemble- and network-based methods to identify genomic biomarkers
  • Fair machine learning models for Health

Academic Trajectory

2014-2016, I was a Postdoctoral Researcher under the supervision of Profa. Dra. Andréia Biolo in the Experimental and Molecular Cardiovascular Laboratory, at the Experimental Research Center of Hospital de Clínicas de Porto Alegre (HCPA). I developed my research in the fields of Bioinformatics and Systems Biology. My primary focus was on investigating transcriptional and post-transcriptional regulatory networks and differential expression related to the development of cardiovascular diseases. During this period, I also collaborated in several other projects concerned with the study of mechanisms of gene expression regulation involved in the onset and progression of complex diseases, such as cancer and diabetes.

2012-2013, I was a visiting PhD student in Prof. Dr. Manolis Kellis’ Computational Biology Group, at the Computer Science and Artificial Intelligence Laboratory of Massachusetts Institute of Technology (MIT). I was involved in a project whose aim was to infer regulatory networks based on the integrative analysis of ENCODE/modENCODE data for human, fly and worm, and compare the structural and functional properties of the reconstructed regulatory networks across species.

2010-2014, I was a PhD candidate in Computer Science at the Institute of Informatics of UFRGS, under the advising of Profa. Dra. Ana Lúcia C. Bazzan and Prof. Dr. Adriano V. Werhli (co-advisor) in the Multiagent Systems Lab. My PhD Thesis was entitled "Exploring ensemble learning techniques to optimize the reverse engineering of gene regulatory networks". You can read it here.

2005-2010, I was an undergraduate student at the Center of Computational Sciences of Federal University of Rio Grande (FURG), from which I received my Bachelor degree in Computer Engineering. My Bachelor's thesis aimed at the reconstruction of gene regulatory networks based on a Bayesian hierarchical model and a parallel sampling algorithm, and was advised by Prof. Dr. Adriano Werhli.

Recent papers

A transcriptome meta-analysis of ethanol embryonic exposure: implications in neurodevelopment and neuroinflammatory genes

Lord V.O., Giudicelli G.C., Recamonde-Mendoza M., Vianna F.S.L., Kowalski T.W.,
Neuroscience Informatics, 2022

Transcriptome meta-analysis of valproic acid exposure in human embryonic stem cells

Kowalski T.W., Lord V.O., Sgarioni E., Gomes J.D.A., Mariath L.M., Recamonde-Mendoza M., Vianna F.S.L.
European Neuropsychopharmacology, 2022

Machine learning methods for prediction of cancer driver genes: a survey paper

Andrades R., Recamonde-Mendoza M.
Briefings in Bioinformatics, 2022

Identifying posttraumatic stress disorder staging from clinical and sociodemographic features: a proof-of-concept study using a machine learning approach

Ramos-Lima L.F., Waikamp V., Oliveira-Watanabe T.T., Teche S.P., Recamonde-Mendoza M., Mello M.F., Mello A.F., Freitas L.H.M.
Psychiatry Research, 2022

Gene Expression Analysis Platform (GEAP): A highly customizable, fast, versatile and ready-to-use microarray analysis platform

Nunes I.J.G., Recamonde-Mendoza M., Feltes B.
Genetics and Molecular Biology, 2022

Comparative Genomics Identifies Putative Interspecies Mechanisms Underlying Crbn-Sall4-Linked Thalidomide Embryopathy

Kowalski T.W., Caldas-Garcia G.B., Gomes J.A., Fraga L.R., Schuler-Faccini L., Recamonde-Mendoza M., Paixão-Cortês V.R., Vianna F.S.L.
Frontiers in Genetics, 2021

Patterns of high-risk drinking among medical students: A web-based survey with machine learning

Marcon G., Pereira F.A., Zimerman A., da Silva B.C., von Diemen L., Passos I.C., Recamonde-Mendoza M.
Computers in Biology and Medicine, 2021

EPGAT: Gene Essentiality Prediction With Graph Attention Networks

Schapke J., Tavares A., Recamonde-Mendoza M.
IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2021