Bioinformatics & Machine Learning

Bioinformatics and computational biology are interdisciplinary fields for developing original algorithms to analyse large collections of biological data, to make new predictions and discover new knowledge. The main research topics are: unsupervised and semi-supervised deep learning, high-class imbalance, heterogeneous data fusion, graphs inference and synthesis, semantic inference. Our main goal is to address scientific challenges in machine learning and to develop machine learning challenges and to develop novel methods for biologically relevant issues. The research line also involves the development of novel and high quality bioinformatics tools and cutting-edge machine learning methods.

Team members

Georgina Stegmayer
Diego Milone
Leandro Di Persia
Mariano Rubiolo
Matías Gerard
Leandro Bugnon
Cristian Yones
Gabriela Merino
Jonathan Raad

Web demos

mirDNN: A convolutional deep residual neural network for prediction of pre-miRNAs in genome-wide data.
DL4papers: A deep learning approach for the automatic interpretation of scientific articles.
PhDSeeker: PhDSeeker is a bio-inspired algorithm for synthesizing linear and branched metabolic pathways.
Clustermatch: Clustermatch is an efficient clustering method able to process highly diverse datasest.
ELM-GRNNminer: Extreme learning machines for reverse engineering of gene regulatory networks from expression time series.
miRNAss: Semi-supervised method for microRNA prediction with few labelled samples.
BioDataFusion: A novel approach for highly-diverse multi-omics data fusion.
deepSOM: A novel and effective way of approaching high class-imbalance in pre-miRNA prediction.
miRNA: supervised vs unsupervised Compares a supervised vs an unsupervised approach for pre-miRNA prediction in model genomes.
gamma-AM: A novel method for inferring biological function for a set of genes with previously unknown function.
divControl: A novel method to smoothly control the diversity of a cluster ensemble.
miRNA-SOM: miRNA-SOM is a tool for the discovery of pre-miRNA in the E. multilocularis genome.
EvoMS: An evolutionary tool for finding novel metabolic pathways linking compounds through feasible reactions.
miRNAfe: A comprehensive tool providing almost all state-of-the-art RNA feature extraction methods used today.
miRNAfe: full miRNAfe full is an advanced tool to extract features from RNA sequences, providing almost all state-of-the-art feature extraction methods published today.
omeSOM full: *omeSOM is a tool designed to give support to the data mining task of metabolic and transcriptional datasets derived from different databases.
bSOM lite: Simple demo of bSOM, an algorithm for improve clustering with information from metabolic pathways.

Selected publications

J. Raad, G. Stegmayer, D. H. Milone
Bioinformatics – 2020
L. A. Bugnon, C. Yones, J. Raad, M. Gerard, M. Rubiolo, G. Merino, M. Pividori, L. Di Persia, D. H. Milone, G. Stegmayer
Oxford Bioinformatics – 2020
Delfina A. Ré, Patricia L.M. Lang, C. Yones, Agustín Arce, G. Stegmayer, D. H. Milone, Pablo A. Manavella
Development – 2019
L. A. Bugnon, C. Yones, D. H. Milone, G. Stegmayer
IEEE Transactions on Neural Networks and Learning Systems – 2019
L. A. Bugnon, C. Yones, J. Raad, D. H. Milone, G. Stegmayer
Data in Brief (in press) – 2019
M. Pividori, A. Cernadas, L. A. Haro, F. Carrari, G. Stegmayer, D. H. Milone
Bioinformatics – 2018
M. Rubiolo, D. H. Milone, G. Stegmayer
Bioinformatics, Volume 34, Number 7, page 1253–1260 – Apr 2018
G. Leale, A Bayá, D. H. Milone, P. Granitto, G. Stegmayer
IEEE/ACM Trans. on Comp. Biology and Bioinformatics, Volume 15, Number 1, page 168–180 – 2018
M. Gerard, G. Stegmayer, D. H. Milone
Scientific Reports, Volume 8, Number 1, page 16398 – 2018
G. Stegmayer, L. Di Persia, M. Rubiolo, M. Gerard, M. Pividori, C. Yones, L. A. Bugnon, T. Rodriguez, J. Raad, D. H. Milone
Briefings in Bioinformatics – 2018
G. Stegmayer, C. Yones, L. Kamenetzky, N. Macchiaroli, D. H. Milone
Springer New York, page 29–37 – 2017
C. Yones, G. Stegmayer, D. H. Milone
Bioinformatics – 2017
G. Stegmayer, C. Yones, L. Kamenetzky, D. H. Milone
IEEE/ACM Trans. on Comp. Biology and Bioinformatics, Number 6, page 1316–1326 – 2017