Implementing High Performance Data Analytics Sample Clauses

Implementing High Performance Data Analytics. (HPDA) Task 5.4 starts in M25 and focusses on applications centred around the integration of HPDA techniques and simulation with clinical and/or experimental data. The first activities of this task will be focused on developing an incubation model specific for these types of applications and provide guidance to external users. We will gather information from the HPDA applications in the CompBioMed software stack and select exemplar cases, from both phases of CompBioMed, to develop the incubation model. During the first part of the project, several molecular research applications were developed which involved the creation of machine learning based models using extensive experimental datasets. These applications presented unique challenges in their development, validation and deployment. This task will leverage that experience to guide newly developed applications based on HPDA techniques, such as deep learning-based models or gaussian processes. There are also activities currently ongoing in the project related to the development of HPDA applications. In WP2, under Molecularly-based Medicine Exemplar Research (Task 2.2) there is a specific subtask working on combining Molecular Dynamics simulation data with state-of-the-art deep learning models, looking to solve the sampling issues of protein simulation. Activity associated with the development of Alya, described in Section 8.3.1 above also involves collaboration with cardiologists (x.x. Xxxxx Xxxx’Armellina in Leeds, Xxxx Xxxxx in Oxford) and medical image analysis team (e.g. Xxxxxxx Xxxx in UOXF as part of T3.4 and T3.5) to develop HPDA tools for analysing combination of data from clinics and our computer simulations. These exemplar activities with internal applications will be used to inform best practice guidance for HPDA techniques centred around data availability, curation and pre-processing. A particular focus for Machine Learning applications will be on the assessment of validation and/or reproducibility which imposes specific requirements on the level of information provided to describe the datasets used for research applications.
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