Luca Albergante, Ph.D.

Data Scientist / Bioinformatician

Institut Curie, Paris, France

Biography

I’m a researcher working on the development of machine learning algorithms to gain insights from complex data, with a particular focus on biology.

In 2006, I obtained a M.Sc. in Computer Science from the University of Milan with a thesis on Cryptography. Then, in 2010, I obtained a Ph.D. in Applied Mathematics from the same University with a thesis centered on Computational biology.

In 2011, I joined the group of Computational Systems Biology at the University of Dundee (Dundee, UK), where I developed new computational tools to investigate cancer biology by analyzing gene networks, cellular karyotypes, DNA repair mechanisms, DNA mutations and epidemiological data.

In 2016, I joined the group of Computational Systems Biology of Cancer at the Institut Curie (Paris, France), where I am developing new computational tools to explore the activity of biological pathway and to infer gene expression dynamics from single-cell transcriptomics data.

Download my CV (Compact / Extended)

Interests

Machine Learning
Computational Intelligence
Computational Biology
Bioinformatics
Single cell data analysis

Education

Ph.D. in Mathematics and Statistics for Computational Sciences, 2010

University of Milan, Milan, Italy
M.Sc. in Computer Science, 2006

University of Milan, Milan, Italy

Selected Publications

Robust And Scalable Learning Of Complex Dataset Topologies Via Elpigraph

This article presents ElPiGraph, a flexible and robust manifold learning approach that I co-developed and implemented as an R package

L Albergante, EM Mirkes, H Chen, A Martin, L Faure, E Barillot, L Pinello, AN Gorban, A Zinovyev

preprint, 2018

Preprint

STREAM: Single-cell Trajectories Reconstruction, Exploration And Mapping of omics data

This article presents STREAM, an interactive pipeline capable of disentangling and visualizing complex branching trajectories from both single-cell transcriptomic and epigenomic data.

H Chen, L Albergante, JY Hsu, CA Lareau, G Lo Bosco, J Guan, S Zhou, AN Gorban, DE Bauer, MJ Aryee, DM Langenau, A Zinovyev, JD Buenrostro, GC Yuan, L Pinello

Preprint, 2018

Preprint

Thymic involution and rising disease incidence with age

In this article, I used statistical analysis on epidemiological data derived from different sources to show how a mathematical model based on immunesenescence is able to describe the incidence of various infectious diseases and cancers.

S Palmer, L Albergante, CC Blackburn, TJ Newman

PNAS, 2018

PDF Ageing immune system may explain age-related cancer risk increase Immune system could be key in fight against cancer Could ageing immune systems be a better predictor of cancer than genetic mutations? Aging immune system may explain age-related cancer risk increase Ageing immune system ‘key to cancer development risk’ Our aging immune system explains cancer, not genetic mutations says new study Tumori e invecchiamento: il ruolo sottovalutato del sistema immunitario Cancer Incidence can Increase with Aging Immune System Models Suggest that Declining T Cell Production is the Primary Reason for Age-Related Increases in Cancer Risk Aging immune system may explain age-related cancer risk increase Immune system an important cancer predictor Ageing immune system linked to cancer Men with ageing immune system more susceptible to cancer: Research Could ageing immune systems be a better predictor of cancer than genetic mutations? Aging immune system may explain age-related cancer risk increase Aging immune system may explain age-related cancer risk increase Aging immune system may explain age-related cancer risk increase

Unreplicated DNA remaining from unperturbed S phases passes through mitosis for resolution in daughter cells

In this article, I developed the bioinformatics pipeline used to study the binding profile of 53BP1 from ChIP-Seq data. I also developed the necessary computational tools to explore the connections between this profile and the prediction of a mathematical model of DNA replication. The article highlights the strong connection of 53BP1 with unreplicated DNA and shows how the propensity of different genomic areas to present DNA replication errors can be theoretically predicted

A Moreno, JT Carrington, L Albergante, M Al Mamun, EJ Haagensen, ES Komseli, VG Gorgoulis, TJ Newman, JJ Blow

PNAS, 2016

PDF Joint research shows daughter cells resolve their mother’s unreplicated DNA

Buffered Qualitative Stability explains the robustness and evolvability of transcriptional networks

In this article, I use mathematical modelling, network theory and statistical data analysis to study the robustness of gene networks. The article shows how robustness is able to explain many topological features of gene networks and highlighted the peculiar structure of gene regulation in cancer.

L Albergante, JJ Blow, TJ Newman

eLife, 2014

PDF Biological networks: interdisciplinary collaboration sheds light on what makes cells robust

A Petri net model of granulomatous inflammation: implications for IL-10 mediated control of Leishmania donovani infection

In this article, I present the analysis of a Petri net model that I developed to study experimental visceral leishmaniasis in the liver. The model integrates biological data obtained from various sources and is extensively studied with statistical and computational approaches. The predictions of the models shed light on the dynamics of the infection and suggest new immune-based therapeutic options.

L Albergante, J Timmis, L Beattie, PM Kaye

PLOS Comp Bio, 2013

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