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In Electrochimica Acta 10 January 2025 510
Identifying the mutations that drive cancer growth is key in clinical decision making and precision oncology. As driver mutations confer selective advantage and thus have an increased likelihood of occurrence, frequency-based statistical models are c
Externí odkaz:
http://arxiv.org/abs/2105.00469
Autor:
Dubourg-Felonneau, Geoffroy, Darwish, Omar, Parsons, Christopher, Rebergen, Dami, Cassidy, John W, Patel, Nirmesh, Clifford, Harry W
The emerging field of precision oncology relies on the accurate pinpointing of alterations in the molecular profile of a tumor to provide personalized targeted treatments. Current methodologies in the field commonly include the application of next ge
Externí odkaz:
http://arxiv.org/abs/1912.04174
Autor:
Dubourg-Felonneau, Geoffroy, Darwish, Omar, Parsons, Christopher, Rebergen, Dami, Cassidy, John W, Patel, Nirmesh, Clifford, Harry W
The genomic profile underlying an individual tumor can be highly informative in the creation of a personalized cancer treatment strategy for a given patient; a practice known as precision oncology. This involves next generation sequencing of a tumor
Externí odkaz:
http://arxiv.org/abs/1912.02065
Autor:
Akbar, Adnan, Dubourg-Felonneau, Geoffroy, Solovyev, Andrey, Cassidy, John W, Patel, Nirmesh, Clifford, Harry W
The majority of cancer treatments end in failure due to Intra-Tumor Heterogeneity (ITH). ITH in cancer is represented by clonal evolution where different sub-clones compete with each other for resources under conditions of Darwinian natural selection
Externí odkaz:
http://arxiv.org/abs/1911.12774
Autor:
Dubourg-Felonneau, Geoffroy, Kussad, Yasmeen, Kirkham, Dominic, Cassidy, John W, Patel, Nirmesh, Clifford, Harry W
In this study, we present Flatsomatic - a Variational Auto Encoder (VAE) optimized to compress somatic mutations that allow for unbiased data compression whilst maintaining the signal. We compared two different neural network architectures for the VA
Externí odkaz:
http://arxiv.org/abs/1911.13259
Autor:
Dubourg-Felonneau, Geoffroy, Kussad, Yasmeen, Kirkham, Dominic, Cassidy, John W, Patel, Nirmesh, Clifford, Harry W
Analysis of somatic mutation profiles from cancer patients is essential in the development of cancer research. However, the low frequency of most mutations and the varying rates of mutations across patients makes the data extremely challenging to sta
Externí odkaz:
http://arxiv.org/abs/1911.09008