FEDER - UNICAEN - SCHISM - SPRINGBOARD
Kohesio
UNIVERSITE DE CAEN NORMANDIE
Early research on machine learning (ML) and data mining (DM) tried to fully automate knowledge discovery processes, and reduce human intervention. For good reasons: we have trouble dealing with large amounts of (high-dimensional) data, a tendency to see patterns everywhere, and technical progress has always relieved us of time-consuming tasks. This also motivates current work on automatic parameter tuning [26]. While this can work well in supervised settings where the label to predict gives feedback and a well-working black box model might be all that is needed, this consensus is increasingly being questioned in DM today. users’ assumptions [1], are relatively recent. Also recent is research on interactive data mining methods, which allow the user to give feedback during the mining process – not just before and after – to change exploration strategies, narrow or widen search spaces etc. [3]. The reasons for this shift have been multifold:1. In unsupervised settings such as clustering and pattern mining, labels are by definition absent and automation based on label-information therefore impossible. Yet even in many real-life “supervised” problem settings, a large proportion of data might be unlabeled, and existing labels might be unreliable, so that a user finds herself at best in a semi-supervised setting.2. In an unsupervised or semi-supervised setting, it is almost impossible for users to a priori specify their assumptions, expectations, and goals. If they manage, translating them into, somewhat limited, available constraint languages ​​is difficult. The current framework, where users set parameters before mining, and sift through and interpret output after, wastes time (and money), and is counterintuitive to how we process information. What users can do, however, is react to (partial) results and indicate whether those agree with their intuition, appear interesting etc.3. Often, experts need to understand why algorithms produce the results they do, because large amounts of money or resources are in play, e.g. in drug development or infrastructure deployment, or even lives at stake, e.g. in medicine, disaster preparedness, or military settings. Or they want to understand them because unsupervised DM serves as a hypothesis generator: observing the results of a pattern mining operation or a produced clustering can trigger new insights and inform new research directions - the final step in the “knowledge discovery” process. In the context of SCHISM, we think this process starting from a chemoinformatics setting. Chemoinformatics deals with the description of molecular entities, and both pattern mining and clustering have been used to derive new knowledge: a) Pattern mining, to identify promising substructures for drug development, or shared subpatterns of toxic compounds [19, 13] b) Clustering to identify and select representatives of molecular libraries for virtual or experimental screening, to design novel diverse or focused libraries, and compare libraries from various providers [11], and more generally to actively investigate structural similarity. chemogenomic analysis to pre-clinical studies, cluster-analysis can be used:¿ to get the level of diversity of a molecular dataset,¿ to find molecular structures that share a given structural scaffold,¿ to get a first view of structure-activity relationship by a comparison of chemicals in each cluster,¿ to decide which compounds to prioritize in an acquisition decision, ¿ to guide a lead selection. See application file
Smarter Europe / Research and innovation / Research and innovation activities in public research centres and centres of competence including networking