Research

Diseases are caused by a complex interplay between different factors, such as genetics and the environment. A deeper understanding of the genetic risk factors of complex diseases can enable the development of more personalized treatments and prevention strategies.

Our research is focused on developing novel machine-learning methods to advance key computational aspects of precision medicine. We have a particular focus on the integration of genetic studies with relevant molecular patterns extracted from multi-omics data. For this, we aim to develop the next generation of methodologies that will consolidate large and heterogeneous sources of biomedical information to extract biological insight to ultimately improve human health.

Strongly committed to open source and open science, we use GitHub for the development of reproducible workflows and Manubot for the transparent authoring of modern and collaborative scholarly manuscripts.

Integration of genetic studies with gene co-expression patterns

Projecting genetic associations through gene expression patterns highlights disease etiology and drug mechanisms
Projecting genetic associations through gene expression patterns highlights disease etiology and drug mechanisms
Milton Pividori, Sumei Lu, Binglan Li, Chun Su, Matthew E. Johnson, …, Benjamin F. Voight, Carsten Skarke, Marylyn D. Ritchie, Struan F. A. Grant, Casey S. Greene
Nature Communications  ·  09 Sep 2023  ·  doi:10.1038/s41467-023-41057-4
Here we introduce the PhenoPLIER framework, an omnigenic approach that integrates genetic studies (GWAS/TWAS) with gene modules learned from expression data. PhenoPLIER provides 1) a regression framework that computes an association between gene modules and diseases, 2) a clustering approach that finds groups of diseases with shared transcriptomic properties, and 3) a gene module-based drug-repurposing method.
Manubot
Manuscript source

Extracting complex transcriptional signatures using not-only-linear correlation coefficients

An efficient not-only-linear correlation coefficient based on machine learning
An efficient not-only-linear correlation coefficient based on machine learning
Milton Pividori, Marylyn D. Ritchie, Diego H. Milone, Casey S. Greene
Cold Spring Harbor Laboratory  ·  17 Jun 2022  ·  doi:10.1101/2022.06.15.496326
This article introduces the Clustermatch Correlation Coefficient (CCC), an efficient, easy-to-use and not-only-linear coefficient based on machine learning models. CCC can detect biologically meaningful linear and nonlinear patterns missed by standard, linear-only correlation coefficients.
Manubot
Manuscript source

Automatic revision of scholarly text using AI and large language models

A publishing infrastructure for AI-assisted academic authoring
A publishing infrastructure for AI-assisted academic authoring
Milton Pividori, Casey S. Greene
Cold Spring Harbor Laboratory  ·  23 Jan 2023  ·  doi:10.1101/2023.01.21.525030
We developed the Manubot AI Editor, a plugin to the Manubot publishing infrastructure that uses manuscript section-specific prompts and OpenAI’s models (such as GPT-3) to automatically revise and improve the text.
Manubot
Manuscript source

All

2023

Projecting genetic associations through gene expression patterns highlights disease etiology and drug mechanisms
Projecting genetic associations through gene expression patterns highlights disease etiology and drug mechanisms
Milton Pividori, Sumei Lu, Binglan Li, Chun Su, Matthew E. Johnson, …, Benjamin F. Voight, Carsten Skarke, Marylyn D. Ritchie, Struan F. A. Grant, Casey S. Greene
Nature Communications  ·  09 Sep 2023  ·  doi:10.1038/s41467-023-41057-4
Here we introduce the PhenoPLIER framework, an omnigenic approach that integrates genetic studies (GWAS/TWAS) with gene modules learned from expression data. PhenoPLIER provides 1) a regression framework that computes an association between gene modules and diseases, 2) a clustering approach that finds groups of diseases with shared transcriptomic properties, and 3) a gene module-based drug-repurposing method.
Manubot
Manuscript source
The high-dimensional space of human diseases built from diagnosis records and mapped to genetic loci
The high-dimensional space of human diseases built from diagnosis records and mapped to genetic loci
Gengjie Jia, Yu Li, Xue Zhong, Kanix Wang, Milton Pividori, …, Michiaki Kubo, Nancy J. Cox, James Evans, Xin Gao, Andrey Rzhetsky
Nature Computational Science  ·  22 May 2023  ·  doi:10.1038/s43588-023-00453-y
A publishing infrastructure for AI-assisted academic authoring
A publishing infrastructure for AI-assisted academic authoring
Milton Pividori, Casey S. Greene
Cold Spring Harbor Laboratory  ·  23 Jan 2023  ·  doi:10.1101/2023.01.21.525030
We developed the Manubot AI Editor, a plugin to the Manubot publishing infrastructure that uses manuscript section-specific prompts and OpenAI’s models (such as GPT-3) to automatically revise and improve the text.
Manubot
Manuscript source

2022

Discerning asthma endotypes through comorbidity mapping
Discerning asthma endotypes through comorbidity mapping
Gengjie Jia, Xue Zhong, Hae Kyung Im, Nathan Schoettler, Milton Pividori, …, Michiaki Kubo, Nancy J. Cox, Carole Ober, Andrey Rzhetsky, Julian Solway
Nature Communications  ·  07 Nov 2022  ·  doi:10.1038/s41467-022-33628-8
An efficient not-only-linear correlation coefficient based on machine learning
An efficient not-only-linear correlation coefficient based on machine learning
Milton Pividori, Marylyn D. Ritchie, Diego H. Milone, Casey S. Greene
Cold Spring Harbor Laboratory  ·  17 Jun 2022  ·  doi:10.1101/2022.06.15.496326
This article introduces the Clustermatch Correlation Coefficient (CCC), an efficient, easy-to-use and not-only-linear coefficient based on machine learning models. CCC can detect biologically meaningful linear and nonlinear patterns missed by standard, linear-only correlation coefficients.
Manubot
Manuscript source

2020

Polygenic transcriptome risk scores improve portability of polygenic risk scores across ancestries
Polygenic transcriptome risk scores improve portability of polygenic risk scores across ancestries
Yanyu Liang, Milton Pividori, Ani Manichaikul, Abraham A. Palmer, Nancy J. Cox, Heather Wheeler, Hae Kyung Im
Cold Spring Harbor Laboratory  ·  13 Nov 2020  ·  doi:10.1101/2020.11.12.373647
FAIA: a framework for intelligent agents development
FAIA: a framework for intelligent agents development
La Tecnología Educativa al Servicio de la Educación Tecnológica: Experiencias e investigaciones en la UTN  ·  02 Oct 2020  ·  isbn:978-987-25855-9-4
PhenomeXcan: Mapping the genome to the phenome through the transcriptome
PhenomeXcan: Mapping the genome to the phenome through the transcriptome
Milton Pividori, Padma S. Rajagopal, Alvaro Barbeira, Yanyu Liang, Owen Melia, Lisa Bastarache, YoSon Park, GTEx Consortium, Xiaoquan Wen, Hae K. Im
Science Advances  ·  11 Sep 2020  ·  doi:10.1126/sciadv.aba2083
Probabilistic Colocalization of Genetic Variants from Complex and Molecular Traits: Promise and Limitations
Probabilistic Colocalization of Genetic Variants from Complex and Molecular Traits: Promise and Limitations
Abhay Hukku, Milton Pividori, Francesca Luca, Roger Pique-Regi, Hae Kyung Im, Xiaoquan Wen
Cold Spring Harbor Laboratory  ·  01 Jul 2020  ·  doi:10.1101/2020.07.01.182097
DL4papers: a deep learning approach for the automatic interpretation of scientific articles
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
Bioinformatics  ·  24 Feb 2020  ·  doi:10.1093/bioinformatics/btaa111

2019

Exploiting the GTEx resources to decipher the mechanisms at GWAS loci
Exploiting the GTEx resources to decipher the mechanisms at GWAS loci
Alvaro N Barbeira, Rodrigo Bonazzola, Eric R Gamazon, Yanyu Liang, YoSon Park, …, Ayellet V Segrè, Christopher D. Brown, Tuuli Lappalainen, Xiaoquan Wen, Hae Kyung Im
Cold Spring Harbor Laboratory  ·  22 Oct 2019  ·  doi:10.1101/814350
Predicting novel microRNA: a comprehensive comparison of machine learning approaches.
Predicting novel microRNA: a comprehensive comparison of machine learning approaches.
Georgina Stegmayer, Leandro E Di Persia, Mariano Rubiolo, Matias Gerard, Milton Pividori, Cristian Yones, Leandro A Bugnon, Tadeo Rodriguez, Jonathan Raad, Diego H Milone
Briefings in bioinformatics  ·  27 Sep 2019  ·  pmid:29800232
Shared and distinct genetic risk factors for childhood-onset and adult-onset asthma: genome-wide and transcriptome-wide studies
Shared and distinct genetic risk factors for childhood-onset and adult-onset asthma: genome-wide and transcriptome-wide studies
Milton Pividori, Nathan Schoettler, Dan L Nicolae, Carole Ober, Hae Kyung Im
The Lancet Respiratory Medicine  ·  01 Jun 2019  ·  doi:10.1016/S2213-2600(19)30055-4
Integrating predicted transcriptome from multiple tissues improves association detection
Integrating predicted transcriptome from multiple tissues improves association detection
Alvaro N. Barbeira, Milton Pividori, Jiamao Zheng, Heather E. Wheeler, Dan L. Nicolae, Hae Kyung Im
PLOS Genetics  ·  22 Jan 2019  ·  doi:10.1371/journal.pgen.1007889

2018

ukbREST: efficient and streamlined data access for reproducible research in large biobanks
ukbREST: efficient and streamlined data access for reproducible research in large biobanks
Milton Pividori, Hae Kyung Im
Bioinformatics  ·  05 Nov 2018  ·  doi:10.1093/bioinformatics/bty925
Clustermatch: discovering hidden relations in highly diverse kinds of qualitative and quantitative data without standardization
Clustermatch: discovering hidden relations in highly diverse kinds of qualitative and quantitative data without standardization
Milton Pividori, Andres Cernadas, Luis A de Haro, Fernando Carrari, Georgina Stegmayer, Diego H Milone
Bioinformatics  ·  24 Oct 2018  ·  doi:10.1093/bioinformatics/bty899

2016

Diversity control for improving the analysis of consensus clustering
Diversity control for improving the analysis of consensus clustering
Milton Pividori, Georgina Stegmayer, Diego H. Milone
Information Sciences  ·  01 Sep 2016  ·  doi:10.1016/j.ins.2016.04.027

2015

A very simple and fast way to access and validate algorithms in reproducible research
A very simple and fast way to access and validate algorithms in reproducible research
Georgina Stegmayer, Milton Pividori, Diego H. Milone
Briefings in Bioinformatics  ·  28 Jul 2015  ·  doi:10.1093/bib/bbv054

2010

How to Develop Intelligent Agents in an Easy Way with FAIA
How to Develop Intelligent Agents in an Easy Way with FAIA
Jorge Roa, Milton Pividori, Ma. De los Milagros Gutiérrez, Georgina Stegmayer
Quality and Communicability for Interactive Hypermedia Systems  ·  01 Jan 2010  ·  doi:10.4018/978-1-61520-763-3.ch006