A5088659656- Bioinformatics and Genomic Networks
- Molecular Biology Techniques and Applications
- Epigenetics and DNA Methylation
- DNA Repair Mechanisms
- Gene expression and cancer classification
- CRISPR and Genetic Engineering
- Cancer Genomics and Diagnostics
- Machine Learning in Bioinformatics
- Computational Drug Discovery Methods
- Single-cell and spatial transcriptomics
- Cell Image Analysis Techniques
- Genomics and Phylogenetic Studies
- Genetic factors in colorectal cancer
- RNA modifications and cancer
Delft University of Technology
2022-2025
Abstract Motivation Understanding the factors involved in DNA double-strand break (DSB) repair is crucial for development of targeted anti-cancer therapies, yet roles many genes remain unclear. Recent studies show that perturbations certain can alter distribution sequence-specific mutations left behind after DSB repair. This suggests genome-wide screening could reveal novel by identifying whose perturbation causes mutational spectra observed at a given site to deviate significantly from...
Motivation: Controlling the outcomes of CRISPR editing is crucial for success gene therapy. Since donor template-based often inefficient, alternative strategies have emerged that leverage mutagenic end-joining repair instead. Existing machine learning models can accurately predict outcomes, however: generalisability beyond specific cell line used training remains a challenge, and interpretability typically limited by suboptimal feature representation model architecture. Results: We propose...
Anti-cancer therapies based on synthetic lethality (SL) exploit tumour vulnerabilities for treatment with reduced side effects, by targeting a gene that is jointly essential another whose function lost. Computational prediction key to expedite SL screening, yet existing methods are vulnerable prevalent selection bias in data and reliant cancer or tissue type-specific omics, which can be scarce. Notably, sequence similarity remains underexplored as proxy related joint essentiality.
Abstract Motivation Synthetic lethality (SL) between two genes occurs when simultaneous loss of function leads to cell death. This holds great promise for developing anti-cancer therapeutics that target synthetic lethal pairs endogenously disrupted genes. Identifying novel SL relationships through exhaustive experimental screens is challenging, due the vast number candidate pairs. Computational prediction therefore sought identify promising gene further experimentation. However, current...
Abstract Motivation Many tumours show deficiencies in DNA damage response (DDR), which influence tumorigenesis and progression, but also expose vulnerabilities with therapeutic potential. Assessing patients might benefit from DDR-targeting therapy requires knowledge of tumour DDR deficiency status, mutational signatures reportedly better predictors than loss function mutations select genes. However, are identified independently using unsupervised learning, therefore not optimised to...
Abstract Anti-cancer therapies based on synthetic lethality (SL) exploit tumor vulnerabilities for treatment with reduced side effects. Since simultaneous loss-of-function of SL genes causes cell death, tumors known gene disruptions can be treated by targeting partners. Computational selection promising candidates amongst all combinations is key to expedite experimental screening. However, current prediction models: (i) only use tissue type-specific molecular data, which scarce/noisy,...