Diego Galeano, Ph.D.

I am a Machine Learning Researcher at the Faculty of Engineering, National University of Asunción, Paraguay. I am also a consultant for the biotech/pharma industry in the U.S.

I develop machine learning models for applications in drug discovery and medicine.

2023

Core mitochondrial genes are down-regulated during SARS-CoV-2 infection of rodent and human hosts
Joseph W Guarnieri, Joseph M Dybas, Hossein Fazelinia, Man S Kim, Justin Frere, Yuanchao Zhang, Yentli Soto Albrecht, Deborah G Murdock, Alessia Angelin, Larry N Singh, Scott L Weiss, Sonja M Best, Marie T Lott, Shiping Zhang, Henry Cope, Victoria Zaksas, Amanda Saravia-Butler, Cem Meydan, Jonathan Foox, Christopher Mozsary, Yaron Bram, Yared Kidane, Waldemar Priebe, Mark R Emmett, Robert Meller, Sam Demharter, Valdemar Stentoft-Hansen, Marco Salvatore, Diego Galeano, Francisco J Enguita, Peter Grabham, Nidia S Trovao, Urminder Singh, Jeffrey Haltom, Mark T Heise, Nathaniel J Moorman, Victoria K Baxter, Emily A Madden, Sharon A Taft-Benz, Elizabeth J Anderson, Wes A Sanders, Rebekah J Dickmander, Stephen B Baylin, Eve Syrkin Wurtele, Pedro M Moraes-Vieira, Deanne Taylor, Christopher E Mason, Jonathan C Schisler, Robert E Schwartz, Afshin Beheshti, Douglas C Wallace
Science Translational Medicine 2023.

2022

Machine learning prediction of side effects for drugs in clinical trials
Diego Galeano, Alberto Paccanaro
Cell Reports Methods 2022

The interplay between lncRNAs, RNA-binding proteins and viral genome during SARS-CoV-2 infection reveals strong connections with regulatory events involved in RNA metabolism and immune response
Francisco J Enguita, Ana Lúcia Leitão, J Tyson McDonald, Viktorija Zaksas, Saswati Das, Diego Galeano, Deanne Taylor, Eve Syrkin Wurtele, Amanda Saravia-Butler, Stephen B Baylin, Robert Meller, D Marshall Porterfield, Douglas C Wallace, Jonathan C Schisler, Christopher E Mason, Afshin Beheshti
Theranostics 2022.

Machine learning and network medicine approaches for drug repositioning for COVID-19
Suzana de Siqueira Santos, Mateo Torres, Diego Galeano, María del Mar Sánchez, Luca Cernuzzi, Alberto Paccanaro
Cell Patterns 2022.

2021

A Recommender System Approach for Predicting Effective Antivirals
Rafael Adorno, Diego Galeano, DH Stalder, Luca Cernuzzi, Alberto Paccanaro
2021 XLVII Latin American Computing Conference (CLEI), IEEE 2021.

Role of miR-2392 in driving SARS-CoV-2 infection
JT McDonald, FJ Enguita, ..., Diego Galeano, Alberto Paccanaro, ... and Afshin Beheshti, UNC COVID-19 Pathobiology Consortium
Cell Reports 2021

The Great Deceiver: miR-2392’s Hidden Role in Driving SARS-CoV-2 Infection
JT McDonald, FJ Enguita, ..., Diego Galeano, Alberto Paccanaro, ... and Afshin Beheshti, UNC COVID-19 Pathobiology Consortium
bioRxiv 2021.

Interpretable Drug Target Predictions using Self-Expressiveness
Diego Galeano, Santiago Noto, Ruben Jimenez and Alberto Paccanaro
bioRxiv 2021.

2020

Predicting the Frequencies of Drug Side Effects
Diego Galeano, Shantao Li, Mark Gerstein and Alberto Paccanaro
Nature Communications 2020.

2019

Predicting the Frequencies of Drug Side Effects
Diego Galeano, and Alberto Paccanaro
Preprint 2019.
(bioarxiv)

Learning Interpretable Disease Self-Representations for Drug Repositioning
(co-first author*) Fabrizio Frasca*, Diego Galeano, Guadalupe Gonzalez, Ivan Laponogov, Kirill Veselkov, Alberto Paccanaro, Michael M Bronstein
NeurIPS Graph Representation Learning Workshop. 2019.
pdf (arxiv)

The Geometric Sparse Matrix Completion Model for Predicting Drug Side effects
Diego Galeano, Alberto Paccanaro
Preprint. 2019.
(bioarxiv)

2018

A recommender system approach for predicting drug side effects
Diego Galeano, and Alberto Paccanaro
IJCNN. 2018.
(IEEE Xplore)

2017

Mining the Biomedical Literature to predict shared drug targets in drugbank
(co-first author*) Horacio Caniza*, <Diego Galeano, and Alberto Paccanaro
IEEE-CLEI. 2017.
(IEEE Xplore)

2016

Drug targets prediction using chemical similarity
Diego Galeano, and Alberto Paccanaro
IEEE-CLEI. 2016.

2015

Posturography Platform and Balance Control Training and Research System Based on FES and Muscle Synergies
Diego Galeano, Fernando Brunetti, Diego Torricelli, S. Piazza, JLP Rovira
Springer Series in Computational Neuroscience (book). 2015.

2014

A tool for balance control training using muscle synergies and multimodal interfaces
Diego Galeano, F. Brunetti, D. Torricelli, S. Piazza, JLP Rovira
BioMed research international (Hindawi) 2014.

2013

A low cost platform based on FES and muscle synergies for postural control research and rehabilitation
Diego Galeano, F. Brunetti, S. Piazza, D. Torricelli
NEUROTECHNIX 2013.

1 - Machine Learning for Space Biology

Dr Afshin Behesti have identified key circulating miRNA spaceflight signature that regulates vascular damage (Malkani et al., Cell Reports, 2020). Our idea is to develop machine learning models to mitigate the adverse effect caused by space radiation and microgravity in astronauts. Our project seeks to develop novel models that predicts small molecules that can be used as treatments.

2 - Predicting the frequencies of drug side effects

A central issue in drug risk-benefit assessment is the identification of the frequencies of side effects in humans. Currently, these frequencies are experimentally determined in randomised controlled clinical trials. We developed a novel machine learning framework for computationally predicting the frequencies of drug side effects. Our matrix decomposition algorithm learns latent signatures of drugs and side effects that are both reproducible and biologically interpretable. We show the usefulness of our approach on 759 structurally and therapeutically diverse drugs and 994 side effects from all human physiological systems. Our approach can be applied to any drug or compound for which a few side effect frequencies have been identified, in order to predict the frequencies of further, yet unidentified, side effects. We also found that our model is informative of the biology underlying drug activity: individual components of the drug signatures are related to the distinct anatomical categories of the drugs and to the specific drug routes of administration.

2 - Extending the druggable genome

The identification of unknown targets of drugs is critical for the development of novel medicines and for the repositioning of old drugs. Yet, the molecular characterisation of molecules remains a daunting task. Recent estimates indicates that the number of targets known for approved drugs is only 4.5 (DrugBank 5.1). The goal of this project is to develop a machine learning model that not only allow to predict novel drug targets but also, it can help us to extend the known druggable genome.

References

Hopkins, A. L. & Groom, C. R. The druggable genome. Nature Reviews Drug Discovery 1, 727–730 (2002).

Finan, C. et al. The druggable genome and support for target identification and validation in drug development. Science Translational Medicine 9, (2017).

Russ, A. P. & Lampel, S. The druggable genome: an update. Drug Discovery Today 10, 1607–1610 (2005).

Rask-Andersen, M., Masuram, S. & Schiöth, H. B. The Druggable Genome: Evaluation of Drug Targets in Clinical Trials Suggests Major Shifts in Molecular Class and Indication. Annu. Rev. Pharmacol. Toxicol. 54, 9–26 (2014).

Diego Galeano is a Machine Learning Researcher at the Facultad de Ingenieria, Universidad Nacional de Asuncion (FIUNA), in Paraguay. Diego completed his Ph.D. at Royal Holloway, University of London in 2019, working under the supervision of Prof. Alberto Paccanaro. Diego also was Postdoctoral Researcher in Data Science at Fundação Getulio Vargas (FGV) Rio de Janeiro, Brasil in 2020-2021. Diego received a full-ride ITAIPU scholarship for undergraduate studies in Paraguay, and in 2015, the BECAL scholarship for his doctoral studies in London. Diego was a research fellow of GersteinLab at Yale University in 2017, where he worked in model interpretability and enhancer prediction. This research visit was thanks to the Royal Holloway Travel Award, the Santander Travel Award and the BECAL Travel Award. He also received the best poster and presentation awards for three years at the Annual Computer Science Colloquium at Royal Holloway. His interests lie in applications of AI and machine learning in healthcare, biology and medicine. He is also interested in the social impact of AI.

ML Researcher
Facultad de Ingenieria, FIUNA
Universidad Nacional de Asuncion, Paraguay

dgaleano@ing.una.py

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