Kuijjer Lab

Recent Posts

  • November 01, 2022

    New BioRχiv pre-print

    We have posted a new version of our previous BioRχiv pre-print on large-scale network analysis by Tatiana. We modeled networks for individual soft-tissue sarcoma patients and identified large regulatory heterogeneity in leiomyosarcomas. To characterize this regulatory heterogeneity, Tatiana developed a new algorithm, called PORCUPINE. PORCUPINE is a permutation-based approach that uses Principal Components Analysis to identifies pathways that significantly contribute to regulatory heterogeneity in a patient population. We...

  • October 28, 2022

    New publication

    We have a new publication in the Journal of Pathology as part of a collaboration with David Adams from the Wellcome Sanger Institute. The work investigates the mutational landscape of primary cutaneous melanoma. More information on the publication can be found here.

  • October 06, 2022

    New publication

    We have a new publication in Frontiers in Digital Health as part of a collaboration with several groups through the NCI/DOE 2020 Ideas Lab: Toward Building a Cancer Patient "Digital Twin". The article describes five new approaches to build predictive cancer patient digital twins. More information on the publication can be found here.

  • August 31, 2022

    New publication

    We have a new publication in Cell Reports, as part of a collaboration with Victor Greiff, University of Oslo. Victor's team developed an approach to study immune receptor repertoires that uses similarity networks to derive a multidimensional picture of the immune repertoire landscape. More information on the publication can be found here.

  • August 10, 2022

    New group member

    We are very excited to announce that Joel Rodríguez Herrera has joined Kuijjer and Mathelier labs on a student internship. Joel is a Bachelor's student in Genomic Sciences from the National Autonomous University of Mexico (UNAM). He will be working on a joined project with Anthony Mathelier's group, predicting promoter-enhancer interactions using message passing approaches.