Meet ApplyKite Twin, the application assistant that learns with you

Meet your Twin, it learns with you

Your Twin learns with you

Launch week codes:30% off annual Pro20% off monthly Pro

Ends in00h00m00s

Natasa Przulj

Professor

University College London

United Kingdom

Research Interests

ApplyKite TwinLearns as you apply

Make your next move.

Prepare your application

Review drafts before you send them.

Explore related searches

Professors in United Kingdom Professors in Cell Biology Cell Biology professors in United Kingdom

About

Natasa Przulj is an Associate Professor at University College London, United Kingdom. Her research focuses on biological complexity, evolutionary dynamics in genetic networks, and the integration of multi-omics data, particularly in the context of diseases such as cancer and Parkinson's. Recent publications include works on network embedding paradigms and the identification of new COVID-19 related genes.

Articles (9)

Year:

2025

Year:

2025

Year:

2025

Year:

2024

Year:

2024

Year:

2024

Year:

2024

Year:

2023

Year:

2023

Collaborators (5)

Mihai Pop

Professor

University of Maryland at College Park

UNITED STATES

View details

Yves Moreau

Full Professor

KU Leuven

BELGIUM

View details

Teresa Attwood

The University of Manchester

UNITED KINGDOM

View details

Pascal Maurice

Directeur de Recherche CNRS (DR2)

CNRS Délégation Centre-Est

FRANCE

View details

Shoba Ranganathan

Macquarie University

AUSTRALIA

View details

ApplyKite TwinLearns as you apply

Make your next move.

Prepare your application

Review drafts before you send them.

Natasa Przulj

Research Interests

Make your next move.

Explore related searches

About

Articles (9)

Biological databases in the age of generative artificial intelligence

Author Correction: Systematic protein-protein interaction mapping for clinically relevant human GPCRs

Yield Determination in Major Small Grain Crops in Response to Nitrogen Fertilization

Simplicity within biological complexity

Graphlet-based hyperbolic embeddings capture evolutionary dynamics in genetic networks

MONFIT: multi-omics factorization-based integration of time-series data sheds light on Parkinson’s disease

The axes of biology: a novel axes-based network embedding paradigm to decipher the functional mechanisms of the cell

A functional analysis of omic network embedding spaces reveals key altered functions in cancer

Integrated Data Analysis Uncovers New COVID-19 Related Genes and Potential Drug Re-Purposing Candidates

Collaborators (5)

Mihai Pop

Yves Moreau

Teresa Attwood

Pascal Maurice

Shoba Ranganathan

Make your next move.