Evaluation of the dynamic construct competition miner for an eHealth system

Business processes of some domains are highly dynamic and increasingly complex due to their dependencies on a multitude of services provided by various providers. The quality of services directly impacts the business process’s efficiency. A first prerequisite for any optimization initiative requires a better understanding of the deployed business processes. However, the business processes are either not documented at all or are only poorly documented. Since the actual behaviour of the business processes and underlying services can change over time it is required to detect the dynamically changing behaviour in order to carry out correct analyses. This paper presents and evaluates the integration of the Dynamic Construct Competition Miner (DCCM) as process monitor in the TIMBUS architecture. The DCCM discovers business processes and recognizes changes directly from an event stream at run-time. The evaluation is carried out in the context of an industrial use-case from the eHealth domain. We will describe the key aspects of the use-case and the DCCM as well as present the relevant evaluation results

An Open Drug Discovery Competition: Experimental Validation of Predictive Models in a Series of Novel Antimalarials.

The Open Source Malaria (OSM) consortium is developing compounds that kill the human malaria parasite, Plasmodium falciparum, by targeting PfATP4, an essential ion pump on the parasite surface. The structure of PfATP4 has not been determined. Here, we describe a public competition created to develop a predictive model for the identification of PfATP4 inhibitors, thereby reducing project costs associated with the synthesis of inactive compounds. Competition participants could see all entries as they were submitted. In the final round, featuring private sector entrants specializing in machine learning methods, the best-performing models were used to predict novel inhibitors, of which several were synthesized and evaluated against the parasite. Half possessed biological activity, with one featuring a motif that the human chemists familiar with this series would have dismissed as "ill-advised". Since all data and participant interactions remain in the public domain, this research project "lives" and may be improved by others

Discovering and managing similarity knowledge in temporal case-based reasoning systems

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SOPHIA in Enterprise Track

task). Given a topic our task was to find an ordered list of up to 100 experts (from a predefined list of candidate experts) and for every expert create an ordered list of up to 20 support documents. Support document should prove that given person is indeed an expert in the domain presented by the topic. We implemented 3 algorithms to solve this task which resulted in 3 runs sophiarun1, sophiarun2 and sophiarun3. All runs are based on Contextual Document Clustering (CDC) algorithm [1,2] applied to a part of W3C document corpus. Document clustering W3C collection contains documents of different types. In our experiments we used only two document types: www and lists. Examples of www documents are drafts and final versions of official W3C documents, slides from presentations given by W3C members and so on. Documents of lists type are e-mails. We split www documents into parts, based on 1000 word long segments and considered every part as a separate document. We didn’t split mails (lists type documents)

Digital Preservation Of Business Processes with TIMBUS Architecture: Paper - iPRES 2012 - Digital Curation Institute, iSchool, Toronto

The majority of existing digital preservation solutions are focusing on the long-term storage of digital content such as documents, images, video, audio files and other domain specific data. Preservation of an Information Technology infrastructure for supporting business processes is a much more challenging task. It requires the preservation of software and hardware stacks as well as relevant contexts, which together, provide an execution layer for running business processes. The proposed TIMBUS architecture addresses limitations of existing digital preservation solutions and provides a complete framework for preserving business processes implemented upon a service oriented architecture

An Open Drug Discovery Competition: Experimental Validation of Predictive Models in a Series of Novel Antimalarials

The discovery of new antimalarial medicines with novel mechanisms of action is key to combating the problem of increasing resistance to our frontline treatments. The Open Source Malaria (OSM) consortium has been developing compounds ("Series 4") that have potent activity against Plasmodium falciparum in vitro and in vivo and that have been suggested to act through the inhibition of PfATP4, an essential membrane ion pump that regulates the parasite’s intracellular Na+ concentration. The structure of PfATP4 is yet to be determined. In the absence of structural information about this target, a public competition was created to develop a model that would allow the prediction of anti-PfATP4 activity among Series 4 compounds, thereby reducing project costs associated with the unnecessary synthesis of inactive compounds.In the first round, in 2016, six participants used the open data collated by OSM to develop moderately predictive models using diverse methods. Notably, all submitted models were available to all other participants in real time. Since then further bioactivity data have been acquired and machine learning methods have rapidly developed, so a second round of the competition was undertaken, in 2019, again with freely-donated models that other participants could see. The best-performing models from this second round were used to predict novel inhibitory molecules, of which several were synthesised and evaluated against the parasite. One such compound, containing a motif that the human chemists familiar with this series would have dismissed as ill-advised, was active. The project demonstrated the abilities of new machine learning methods in the prediction of active compounds where there is no biological target structure, frequently the central problem in phenotypic drug discovery. Since all data and participant interactions remain in the public domain, this research project “lives” and may be improved by others