88 citations
- Peter Sollich2 profiles2 · h 49
- Teddy Lazebnik2 profiles2 · h 6
- Alexander Schmidt1 · h 3
- Andreas Beyer1 · h 1
- Andrew E. Teschendorff1
- Anne-Florence Bitbol1 · h 23
- A. Teschendorff1 · h 80
- C. Banerji1 · h 17
- Christine A. Orengo1 · h 10
- Damiano Sgarbossa1 · h 5
- Francesc-Xavier Marsellach1 · h 7
- Hagit Ben-Shoshan1 · h 2
- University College LondonGB6 papers
- King's College LondonGB2 papers
- London CancerGB2 papers
- Biotechnologisches ZentrumDE1 paper
- Cancer Research UKGB1 paper
- Center for Systems Biology DresdenDE1 paper
- Chinese Academy of SciencesCN1 paper
- Department of Science and TechnologyIN1 paper
- École Polytechnique Fédérale de LausanneCH1 paper
- Genomics (United Kingdom)GB1 paper
- Institute of Infection and ImmunityCA1 paper
- Institute of Structural and Molecular BiologyGB1 paper
6 papers
Interpretable Transformation and Analysis of Timelines through Learning via Surprisability
Osnat Mokryn, Teddy Lazebnik, Hagit Ben Shoshan
The analysis of high-dimensional timeline data and the identification of outliers and anomalies is critical across diverse domains, including sensor readings, biological and medica…
DiffPaSS -- High-performance differentiable pairing of protein sequences using soft scores
Umberto Lupo, Damiano Sgarbossa, Martina Milighetti +1
Identifying interacting partners from two sets of protein sequences has important applications in computational biology. Interacting partners share similarities across species due…
Cancer-inspired Genomics Mapper Model for the Generation of Synthetic DNA Sequences with Desired Genomics Signatures
Teddy Lazebnik, Liron Simon-Keren
Genome data are crucial in modern medicine, offering significant potential for diagnosis and treatment. Thanks to technological advancements, many millions of healthy and diseased…
Increased signaling entropy in cancer requires the scale-free property of protein interaction networks
Andrew E. Teschendorff, Christopher R. S. Banerji, Simone Severini +2
One of the key characteristics of cancer cells is an increased phenotypic plasticity, driven by underlying genetic and epigenetic perturbations. However, at a systems-level it is u…
Signalling Entropy: a novel network-theoretical framework for systems analysis and interpretation of functional omic data
Andrew Teschendorff, Peter Sollich, Reimer Kuehn
A key challenge in systems biology is the elucidation of the underlying principles, or fundamental laws, which determine the cellular phenotype. Understanding how these fundamental…
Stress induces remodelling of yeast interaction and co-expression networks
Sonja Lehtinen, Francesc Xavier Marsellach, Sandra Codlin +6
Network analysis provides a powerful framework for the interpretation of genome-wide data. While static network approaches have proved fruitful, there is increasing interest in the…