I have a broad range of research interests in bioinformatics, computational biology and machine learning, and welcome applications from students in any of these areas. In biology, my main interest is to understand gene regulation and how it is affected by genetic variation. In other words, how does the genome determine which genes are expressed (active) in different cell types, and how do genetic differences between individuals lead to differences in gene expression and ultimately to differences in health and disease traits? My group uses machine learning approaches and large sets of genetic and molecular data to answer these questions. Machine learning is a field at the interface of computer science and statistics that aims to identify correlations and other meaningful patterns in large data sets. Biology is an ideal area for testing and developing new machine learning algorithms, because in biology correlations alone are never enough. For instance, to know that high cholesterol and high blood pressure are often seen together in people with diabetes or heart disease is not very useful, until we establish that in fact, high cholesterol causes high blood pressure, and should therefore be the therapeutic target. To establish similar causal relations at the level of genes, where thousands of genes are expressed in every cell of the human body, influencing each other in untold ways through complex, unknown networks of genetic interactions, is the challenge that my group and I aim to address. In short, to paraphrase a well-known saying: nothing in biology makes sense, except in the light of causal inference.

I teach in the Bachelor and Master programs in Informatics.

• Spring 2021: BINF301
• Spring 2020: MNF130
• Fall 2019: BINF305
• Spring 2019: MNF130

I've also contributed to some NORBIS courses:

• NORBIS Summer School 2021
• NORBIS Course Genomics for Precision Medicine (2021)
• NORBIS Course Computational Approaches in Transcriptome Analysis (2019)

 

Academic article

• Hasibi, Ramin; Michoel, Tom Luk R; Oyarzún, Diego A. (2024). Integration of graph neural networks and genome-scale metabolic models for predicting gene essentiality. (external link)
• Salami, Dariush; Hasibi, Ramin; Savazzi, Stefano et al. (2024). Angle-Agnostic Radio Frequency Sensing Integrated into 5G-NR. (external link)
• Mocci, Giuseppe; Sukhavasi, Katyayani; Örd, Tiit et al. (2024). Single-Cell Gene-Regulatory Networks of Advanced Symptomatic Atherosclerosis. (external link)
• Khan, Mariyam; Ludl, Adriaan-Alexander; Bankier, Sean et al. (2024). Prediction of causal genes at GWAS loci with pleiotropic gene regulatory effects using sets of correlated instrumental variables. (external link)
• Mohammad, Gutama Ibrahim; Michoel, Tom (2024). Predicting the genetic component of gene expression using gene regulatory networks. (external link)
• Bankier, Sean Alexander; Wang, Lingfei; Crawford, Andrew et al. (2023). Plasma cortisol-linked gene networks in hepatic and adipose tissues implicate corticosteroid-binding globulin in modulating tissue glucocorticoid action and cardiovascular risk. (external link)
• Koplev, Simon; Seldin, Marcus; Sukhavasi, Katyayani et al. (2022). A mechanistic framework for cardiometabolic and coronary artery diseases. (external link)
• Malik, Muhammad Ammar; Michoel, Tom (2022). Restricted maximum-likelihood method for learning latent variance components in gene expression data with known and unknown confounders. (external link)
• Bankier, Sean Alexander; Michoel, Tom (2022). eQTLs as causal instruments for the reconstruction of hormone linked gene networks. (external link)
• Salami, Dariush; Hasibi, Ramin; Palipana, Sameera et al. (2022). Tesla-Rapture: A Lightweight Gesture Recognition System From mmWave Radar Sparse Point Clouds. (external link)
• Crawford, Andrew A.; Bankier, Sean; Altmaier, Elisabeth et al. (2021). Variation in the SERPINA6/SERPINA1 locus alters morning plasma cortisol, hepatic corticosteroid binding globulin expression, gene expression in peripheral tissues, and risk of cardiovascular disease. (external link)
• Hasibi, Ramin; Michoel, Tom (2021). A Graph Feature Auto-Encoder for the Prediction of Unobserved Node Features on Biological Networks. (external link)
• Kogelman, Lisette J.A.; Falkenberg, Katrine; Buil, Alfonso et al. (2021). Changes in the gene expression profile during spontaneous migraine attacks. (external link)
• Erola, Pau; Björkegren, Johan L.M.; Michoel, Tom Luk Robert (2020). Model-based clustering of multi-tissue gene expression data. (external link)
• Ludl, Adriaan-Alexander; Michoel, Tom Luk Robert (2020). Comparison between instrumental variable and mediation-based methods for reconstructing causal gene networks in yeast. (external link)
• Wang, Lingfei; Audenaert, Pieter; Michoel, Tom Luk Robert (2019). High-Dimensional Bayesian Network Inference From Systems Genetics Data Using Genetic Node Ordering. (external link)
• Vipin, Deepti; Wang, Lingfei; Devailly, Guillaume et al. (2018). Causal Transcription Regulatory Network Inference Using Enhancer Activity as a Causal Anchor.. (external link)

Academic literature review

• Malik, Muhammad Ammar; Faraone, Stephen; Michoel, Tom et al. (2023). Use of big data and machine learning algorithms to extract possible treatment targets in neurodevelopmental disorders. (external link)
• Michoel, Tom; Zhang, Jitao David (2023). Causal inference in drug discovery and development. (external link)

See a complete overview of publications in Cristin.

See http://lab.michoel.info/publications