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](/images/articles/phenoplier.png)
Extracting complex transcriptional signatures using not-only-linear correlation coefficients
![An efficient not-only-linear correlation coefficient based on machine learning](/images/articles/ccc.png)
Automatic revision of scholarly text using AI and large language models
![A publishing infrastructure for Artificial Intelligence (AI)-assisted academic authoring](/images/articles/manubot-ai-editor.png)
All
2024
![A publishing infrastructure for Artificial Intelligence (AI)-assisted academic authoring](/images/articles/manubot-ai-editor.png)
2023
![Projecting genetic associations through gene expression patterns highlights disease etiology and drug mechanisms](/images/articles/phenoplier.png)
![The high-dimensional space of human diseases built from diagnosis records and mapped to genetic loci](/images/articles/high-dim-diseases.png)
2022
![Discerning asthma endotypes through comorbidity mapping](/images/articles/asthma_endotypes.png)
![An efficient not-only-linear correlation coefficient based on machine learning](/images/articles/ccc.png)
2020
![Polygenic transcriptome risk scores improve portability of polygenic risk scores across ancestries](/images/articles/prs.png)
![PhenomeXcan: Mapping the genome to the phenome through the transcriptome](/images/articles/phenomexcan.png)
![Probabilistic Colocalization of Genetic Variants from Complex and Molecular Traits: Promise and Limitations](/images/articles/prob_coloc.png)
![DL4papers: a deep learning approach for the automatic interpretation of scientific articles](/images/articles/dl4papers.png)
2019
![Exploiting the GTEx resources to decipher the mechanisms at GWAS loci](/images/articles/exploiting.png)
![Predicting novel microRNA: a comprehensive comparison of machine learning approaches.](/images/articles/predicting-mirna.png)
![Shared and distinct genetic risk factors for childhood-onset and adult-onset asthma: genome-wide and transcriptome-wide studies](/images/articles/asthma.png)
![Integrating predicted transcriptome from multiple tissues improves association detection](/images/articles/multixcan.png)
2018
![ukbREST: efficient and streamlined data access for reproducible research in large biobanks](/images/articles/ukbrest.png)
![Clustermatch: discovering hidden relations in highly diverse kinds of qualitative and quantitative data without standardization](/images/articles/clustermatch.png)
2016
![Diversity control for improving the analysis of consensus clustering](/images/articles/divcontrol.png)
2015
![A very simple and fast way to access and validate algorithms in reproducible research](/images/articles/webdemo-builder.png)