The AdaLab project is related to numerous ongoing research projects.
OpenPhacts (Open Pharmacological Concepts Triple Store) is a knowledge management project of the Innovative Medicines Initiative (IMI), a unique partnership between the European Community and the European Federation of Pharmaceutical Industries and Associations (EFPIA).
We will use Open PHACTS tools for knowledge discovery and data, when available, to update Eve’s background knowledge model and we will ensure full interoperability of semantic representations between OUK and Open-PHACTS.
IntellAct (Intelligent observation and execution of Actions and manipulations) addresses the problem of understanding and exploiting the meaning (semantics) of manipulations in terms of objects, actions and their consequences for reproducing human actions with machines. We will import to OUK relevant sematic representations for actions and objects and ensure that OUK is compatible with the formalisms developed within the IntellAct.
ConCreTe has the long-term vision of computer systems that can behave in ways comparable with human creativity, autonomously and interactively, with better interaction between human and machine, better autonomous systems in general, and possibly creativity of new kinds, not yet exhibited by humans. It uses semantic web technology to avoid the bottleneck of domain modelling to enable the advance of creative reasoning, and it is developing AI methodology for creative systems. The project is focusing on domains such as education and gaming, but the main outputs are expected to be of value to AdaLab.
ECHORD++ will create new opportunities for European robotics researchers to work directly with SME/start-ups and new users/customers to create innovative products. Robot system customers and users will be involved through pilots for potential pre-commercial procurement, connecting suppliers directly with the market. We will participate in this project activities in order both to inform and be informed about opportunities to promote AdaLab.
REFRAME (Rethinking the Essence, Flexibility and Reusability of Advanced Model Exploitation reframe) aims to develop an innovative approach to knowledge reuse which allows a range of known ML and data mining techniques to deal with common contextual changes, including: (i) changes in data representation; (ii) the availability of new background knowledge; (iii) predictions required at a different aggregation level. We are interested to apply the proposed by REFRAME ML approaches for knowledge reuse to update Eve’s background knowledge and also for making predictions about yeast diauxic shift.
This interdisciplinary project aims to develop new statistical and ML approaches to analyze high-dimensional, structured and heterogeneous biological data. It focuses on the cases where a relatively small number of samples are characterized by huge quantities of quantitative features; a common situation in large-scale genomic projects, but particularly challenging for statistical inference. AdaLab will aim to incorporate the approaches that will be developed by SMAC, particularly the encoding of prior probabilities, and statistical learning and analysis of high-dimensional datasets.
This project’s goals are targeted at biological discoveries (and ICT support to realize them) and brought the DTAI group expertise in regulatory networks which will be reused in AbaLab.