Research 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 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
Emilio Fenoy
Alejandro Edera
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.
gamma-AM: A novel method for inferring biological function for a set of genes with previously unknown function.
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

Transfer learning: The key to functionally annotate the protein universe
L. A. Bugnon, E. Fenoy, A. A. Edera, J. Raad, G. Stegmayer, D. H. Milone
Patterns – 2023

Anc2vec: embedding gene ontology terms by preserving ancestors relationships
A. A. Edera, D. H. Milone, G. Stegmayer
Briefings in Bioinformatics – 2022

exp2GO: improving prediction of functions in the Gene Ontology with expression data
L. E. Di Persia, T. López, A. A. Arce, D. H. Milone, G. Stegmayer
IEEE/ACM Transactions on Computational Biology and Bioinformatics – 2022

Hierarchical deep learning for predicting GO annotations by integrating protein knowledge
Merino, R. Saidi, D. H. Milone, G. Stegmayer, M. Martín
Bioinformatics – 2022

Secondary structure prediction of long noncoding RNA: review and experimental comparison of existing approaches
L. A. Bugnon, A. Edera, S. Prochetto, M. Gerard, J. Raad, E. Fenoy, M. Rubiolo, U. Chorostecki, G. Gabaldon, F. Ariel, L. Di Persia, D. H. Milone, G. Stegmayer
Briefing in Bioinformatics – 2022

Transfer learning in proteins: evaluating novel protein learned representations for bioinformatics tasks
E. Fenoy, A. Edera, G. Stegmayer
Briefings in Bioinformatics – 2022

Deep Learning for the discovery of new pre-miRNAs: helping the fight against COVID-19
A. Bugnon, J. Raad, G. Merino, C. Yones, F. Ariel, D. H. Milone, G. Stegmayer
Machine Learning with Applications – 2021

High precision in microRNA prediction: a novel genome-wide approach with convolutional deep residual networks
Yones, J. Raad, L. A. Bugnon, D. H. Milone, G. Stegmayer
Computers in Biology and Medicine – 2021

miRe2e: a full end-to-end deep model based on transformers for prediction of pre-miRNAs
J. Raad, L. A. Bugnon, D. H. Milone, G. Stegmayer
Bioinformatics – 2021

Novel SARS-CoV-2 encoded small RNAs in the passage to humans
Merino, J. Raad, L. A. Bugnon, C. Yones, L. Kamenetzky, J. Claus, F. Ariel, D. H. Milone, G. Stegmayer
Bioinformatics – 2020

Complexity measures of the mature miRNA for improving pre-miRNAs prediction
J. Raad, G. Stegmayer, D. H. Milone
Bioinformatics – 2020

DL4papers: a deep learning approach for the automatic interpretation of scientific articles
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

Deep neural architectures for highly imbalanced data in bioinformatics 
L. A. Bugnon, C. Yones, D. H. Milone, G. Stegmayer
IEEE Transactions on Neural Networks and Learning Systems – 2019
Genome-wide hairpins datasets of animals and plants for novel miRNA prediction 
L. A. Bugnon, C. Yones, J. Raad, D. H. Milone, G. Stegmayer
Data in Brief – 2019

Clustermatch: discovering hidden relations in highly diverse kinds of qualitative and quantitative data without standardization 
M. Pividori, A. Cernadas, L. A. Haro, F. Carrari, G. Stegmayer, D. H. Milone
Bioinformatics – 2018

Extreme learning machines for reverse engineering of gene regulatory networks from expression time series 
M. Rubiolo, D. H. Milone, G. Stegmayer
Bioinformatics – 2018

Inferring unknown biological function byintegration of GO annotations and geneexpression data. 
G. Leale, A Bayá, D. H. Milone, P. Granitto, G. Stegmayer
IEEE/ACM Trans. on Comp. Biology and Bioinformatics – 2018

Metabolic pathways synthesis based on ant colony optimization 
M. Gerard, G. Stegmayer, D. H. Milone
Scientific Reports – 2018

Predicting novel microRNA: a comprehensive comparison of machine learning approaches 
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

Computational prediction of novel miRNAs from genome-wide data 
G. Stegmayer, C. Yones, L. Kamenetzky, N. Macchiaroli, D. H. Milone
Springer New York – 2017

Genome-wide pre-miRNA discovery from few labeled examples 
C. Yones, G. Stegmayer, D. H. Milone
Bioinformatics – 2017

High class-imbalance in pre-miRNA prediction: a novel approach based on deepSOM
G. Stegmayer, C. Yones, L. Kamenetzky, D. H. Milone
IEEE/ACM Trans. on Comp. Biology and Bioinformatics – 2017