Project: Computational analysis of large-scale biological data
2022-05-02 – 2026-05-01
- Abstract
The purpose of this project is to design efficient computational algorithms for the analysis of large-scale biological data, and to develop software applications to make these algorithms easily available for domain experts (applied scientists) without a computational background. We will focus on a few applications in particular: (a) the use of long-read sequencing data, as generated by the MinION sequencer, for the improvement of metagenomic and pangenomic analyses of bacterial taxonomy and function, and (b) the analysis of 2D and 3D (medical) imaging data, obtained from microscopy, hyperspectroscopy, or through CT/MRI scans. Data of the relevant kind are in both cases already generated at GUGC. For the second part of the project, we will develop algorithmic proofs-of-concept into standalone, mature software applications, which will be made freely available to facilitate the analysis of large volumes of data.
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Grasshopper optimization algorithm (GOA) : a novel algorithm or a variant of PSO?
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Assessment of intraoperative scoring systems for predicting cytoreduction outcome in peritoneal metastatic disease : a systematic review and meta-analysis
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- Journal Article
- A1
- open access
Assessing the reliability of point mutation as data augmentation for deep learning with genomic data
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- Journal Article
- A1
- open access
Mutate and observe : utilizing deep neural networks to investigate the impact of mutations on translation initiation
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- Journal Article
- A1
- open access
How to make sense of 3D representations for plant phenotyping : a compendium of processing and analysis techniques