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 clustering
An efficient, not-only-linear correlation coefficient based on clustering
Milton Pividori, Marylyn D. Ritchie, Diego H. Milone, Casey S. Greene
Cell Systems  ·  01 Sep 2024  ·  doi:10.1016/j.cels.2024.08.005
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 Artificial Intelligence AI -assisted academic authoring
A publishing infrastructure for Artificial Intelligence (AI)-assisted academic authoring
Milton Pividori, Casey S Greene
Journal of the American Medical Informatics Association  ·  14 Jun 2024  ·  doi:10.1093/jamia/ocae139
We developed the Manubot AI Editor, a plugin to the Manubot publishing infrastructure that uses manuscript section-specific prompts and OpenAI’s models to automatically revise and improve the text.
Manubot
Manuscript source

All

2025

A Pathway-Level Information ExtractoR PLIER framework to gain mechanistic insights into obesity in Down syndrome.
A Pathway-Level Information ExtractoR (PLIER) framework to gain mechanistic insights into obesity in Down syndrome.
Sutanu Nandi, Yuehua Zhu, Lucas A Gillenwater, Marc Subirana-Granés, Haoyu Zhang, Negar Janani, Casey Greene, Milton Pividori, Maria Chikina, James C Costello
Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing  ·  01 Jan 2025  ·  pubmed:39670386

2024

Uncovering hidden gene-trait patterns through biclustering analysis of the UK Biobank
Uncovering hidden gene-trait patterns through biclustering analysis of the UK Biobank
Milton Pividori, Suraju Sadeeq, Arjun Krishnan, Barbara E. Stranger, Christopher R. Gignoux
Cold Spring Harbor Laboratory  ·  11 Nov 2024  ·  doi:10.1101/2024.11.08.622657
Manubot
Manuscript source
Genetic studies through the lens of gene networks.
Genetic studies through the lens of gene networks.
Marc Subirana-Granés, Jill Hoffman, Haoyu Zhang, Christina Akirtava, Sutanu Nandi, Kevin Fotso, Milton Pividori
ArXiv  ·  30 Oct 2024  ·  pubmed:39575117
Manubot
Manuscript source
An efficient, not-only-linear correlation coefficient based on clustering
An efficient, not-only-linear correlation coefficient based on clustering
Milton Pividori, Marylyn D. Ritchie, Diego H. Milone, Casey S. Greene
Cell Systems  ·  01 Sep 2024  ·  doi:10.1016/j.cels.2024.08.005
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
Chatbots in science: What can ChatGPT do for you
Chatbots in science: What can ChatGPT do for you?
Milton Pividori
Nature  ·  14 Aug 2024  ·  doi:10.1038/d41586-024-02630-z
A publishing infrastructure for Artificial Intelligence AI -assisted academic authoring
A publishing infrastructure for Artificial Intelligence (AI)-assisted academic authoring
Milton Pividori, Casey S Greene
Journal of the American Medical Informatics Association  ·  14 Jun 2024  ·  doi:10.1093/jamia/ocae139
We developed the Manubot AI Editor, a plugin to the Manubot publishing infrastructure that uses manuscript section-specific prompts and OpenAI’s models to automatically revise and improve the text.
Manubot
Manuscript source

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

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
Quality Control Procedures for Genome-Wide Association Studies.
Quality Control Procedures for Genome-Wide Association Studies.
Van Q Truong, Jakob A Woerner, Tess A Cherlin, Yuki Bradford, Anastasia M Lucas, …, S Chris Jones, Abigail C Bossa, Stephen D Turner, Marylyn D Ritchie, Shefali S Verma
Current protocols  ·  01 Nov 2022  ·  pubmed:36441943
Polygenic transcriptome risk scores PTRS can improve portability of polygenic risk scores across ancestries.
Polygenic transcriptome risk scores (PTRS) can improve portability of polygenic risk scores across ancestries.
Yanyu Liang, Milton Pividori, Ani Manichaikul, Abraham A Palmer, Nancy J Cox, Heather E Wheeler, Hae Kyung Im
Genome biology  ·  13 Jan 2022  ·  pubmed:35027082

2021

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
Genome Biology  ·  26 Jan 2021  ·  doi:10.1186/s13059-020-02252-4
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
The American Journal of Human Genetics  ·  01 Jan 2021  ·  doi:10.1016/j.ajhg.2020.11.012
Parameterized algorithms for identifying gene co-expression modules via weighted clique decomposition.
Parameterized algorithms for identifying gene co-expression modules via weighted clique decomposition.
Madison Cooley, Casey S Greene, Davis Issac, Milton Pividori, Blair D Sullivan
Proceedings of the 2021 SIAM Conference on Applied and Computational Discrete Algorithms. SIAM Conference on Applied and Computational Discrete Algorithms (2021 : Online)  ·  01 Jan 2021  ·  pubmed:35391741

2020

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
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

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
FAIA: Un framework para el desarrollo de agentes inteligentes
FAIA: Un framework para el desarrollo de agentes inteligentes
EdUTecNe  ·  01 Jan 2010  ·  isbn:978-987-25855-9-4