Clusters of male and female Alzheimer's disease patients in the Alzheimer's Disease Neuroimaging Initiative (ADNI) database

This paper presents homogeneous clusters of patients, identified in the Alzheimer’s Disease Neuroimaging Initiative (ADNI) data population of 317 females and 342 males, described by a total of 243 biological and clinical descriptors. Clustering was performed with a novel methodology, which supports identification of patient subpopulations that are homogeneous regarding both clinical and biological descriptors. Properties of the constructed clusters clearly demonstrate the differences between female and male Alzheimer’s disease patient groups. The major difference is the existence of two male subpopulations with unexpected values of intracerebral and whole brain volumes

Interactive exploration of heterogeneous biological networks with Biomine Explorer

Peer reviewe

Bisociative knowledge discovery for microarray data analysis

Peer reviewe

Baseline Methods for Automated Fictional Ideation

The invention of fictional ideas (ideation) is often a central process in the creative production of artefacts such as poems, music and paintings, but has barely been studied in the Computational Creativity community. We present here three baseline approaches for automated fictional ideation, using methods which invert and alter facts from the ConceptNet and ReVerb databases, and perform bisociative discovery. For each method, we present a curation analysis, by calculating the proportion of ideas which pass a typicality evaluation. We further evaluate one ideation approach through a crowd- sourcing experiment in which participants were asked to rank ideas. The results from this study, and the baseline methods and methodologies presented here, constitute a firm basis on which to build more sophisticated models for automated ideation with evaluative capacity

SegMine workflows for semantic microarray data analysis in Orange4WS

Peer reviewe

Co-Bidding Graphs for Constrained Paper Clustering

The information for many important problems can be found in various formats and modalities. Besides standard tabular form, these include also text and graphs. To solve such problems fusion of different data sources is required. We demonstrate a methodology which is capable to enrich textual information with graph based data and utilize both in an innovative machine learning application of clustering. The proposed solution is helpful in organization of academic conferences and automates one of its time consuming tasks. Conference organizers can currently use a small number of software tools that allow managing of the paper review process with no/little support for automated conference scheduling. We present a two-tier constrained clustering method for automatic conference scheduling that can automatically assign paper presentations into predefined schedule slots instead of requiring the program chairs to assign them manually. The method uses clustering algorithms to group papers into clusters based on similarities between papers. We use two types of similarities: text similarities (paper similarity with respect to their abstract and title), together with graph similarity based on reviewers\u27 co-bidding information collected during the conference reviewing phase. In this way reviewers\u27 preferences serve as a proxy for preferences of conference attendees. As a result of the proposed two-tier clustering process similar papers are assigned to predefined conference schedule slots. We show that using graph based information in addition to text based similarity increases clustering performance. The source code of the solution is freely available