Browsing by Author "Micallef, Luana"

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  • Improving genomics-based predictions for precision medicine through active elicitation of expert knowledge
    (2018-06-27) Sundin, Iiris; Peltola, Tomi; Micallef, Luana; Afrabandpey, Homayun; Soare, Marta; Majumder, Muntasir Mamun; Daee, Pedram; He, Chen; Serim, Baris; Havulinna, Aki; Heckman, Caroline; Jacucci, Giulio; Marttinen, Pekka; Kaski, Samuel
     A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
    Motivation Precision medicine requires the ability to predict the efficacies of different treatments for a given individual using high-dimensional genomic measurements. However, identifying predictive features remains a challenge when the sample size is small. Incorporating expert knowledge offers a promising approach to improve predictions, but collecting such knowledge is laborious if the number of candidate features is very large. Results: We introduce a probabilistic framework to incorporate expert feedback about the impact of genomic measurements on the outcome of interest and present a novel approach to collect the feedback efficiently, based on Bayesian experimental design. The new approach outperformed other recent alternatives in two medical applications: prediction of metabolic traits and prediction of sensitivity of cancer cells to different drugs, both using genomic features as predictors. Furthermore, the intelligent approach to collect feedback reduced the workload of the expert to approximately 11%, compared to a baseline approach. Availability and implementation: Source code implementing the introduced computational methods is freely available at https://github.com/AaltoPML/knowledge-elicitation-for-precision-medicine. Supplementary information: Supplementary data are available at Bioinformatics online.
  • Interaction and Visualization Techniques for the Analysis of Large Tabular Heatmaps
    (2015-08-21) Rantanen, Johanna
     Perustieteiden korkeakoulu | Bachelor's thesis
  • MediSyn: Uncertainty-aware Visualization of Multiple Biomedical Datasets to Support Drug Treatment Selection
    (2017-09-13) He, Chen; Micallef, Luana; Tanoli, Zia-Ur-Rehman; Kaski, Samuel; Aittokallio, Tero; Jacucci, Giulio
     A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
    Background: Dispersed biomedical databases limit user exploration to generate structured knowledge. Linked Data unifies data structures and makes the dispersed data easy to search across resources, but it lacks supporting human cognition to achieve insights. In addition, potential errors in the data are difficult to detect in their free formats. Devising a visualization that synthesizes multiple sources in such a way that links between data sources are transparent, and uncertainties, such as data conflicts, are salient is challenging. Results: To investigate the requirements and challenges of uncertainty-aware visualizations of linked data, we developed MediSyn, a system that synthesizes medical datasets to support drug treatment selection. It uses a matrix-based layout to visually link drugs, targets (e.g., mutations), and tumor types. Data uncertainties are salient in MediSyn; for example, (i) missing data are exposed in the matrix view of drug-target relations; (ii) inconsistencies between datasets are shown via overlaid layers; and (iii) data credibility is conveyed through links to data provenance. Conclusions: Through the synthesis of two manually curated datasets, cancer treatment biomarkers and drug-target bioactivities, a use case shows how MediSyn effectively supports the discovery of drug-repurposing opportunities. A study with six domain experts indicated that MediSyn benefited the drug selection and data inconsistency discovery. Though linked publication sources supported user exploration for further information, the causes of inconsistencies were not easy to find. Additionally, MediSyn could embrace more patient data to increase its informativeness. We derive design implications from the findings.
  • Set Visualization Challenges for Big Data
    (2016) Micallef, Luana
     A4 Artikkeli konferenssijulkaisussa
    This talk will provide a brief overview of the state-of-the-art of set visualization, followed by an in-depth discussion of challenges and open questions when dealing with real-world set-typed data.
  • A task-based evaluation of combined set and network visualization
    (2016-11-01) Rodgers, Peter; Stapleton, Gem; Alsallakh, Bilal; Micallef, Luana; Baker, Rob; Thompson, Simon
     A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
    This paper addresses the problem of how best to visualize network data grouped into overlapping sets. We address it by evaluating various existing techniques alongside a new technique. Such data arise in many areas, including social network analysis, gene expression data, and crime analysis. We begin by investigating the strengths and weakness of four existing techniques, namely Bubble Sets, EulerView, KelpFusion, and LineSets, using principles from psychology and known layout guides. Using insights gained, we propose a new technique, SetNet, that may overcome limitations of earlier methods. We conducted a comparative crowdsourced user study to evaluate all five techniques based on tasks that require information from both the network and the sets. We established that EulerView and SetNet, both of which draw the sets first, yield significantly faster user responses than Bubble Sets, KelpFusion and LineSets, all of which draw the network first.
  • Towards Perceptual Optimization of the Visual Design of Scatterplots
    (2017) Micallef, Luana; Palmas, Gregorio; Oulasvirta, Antti; Weinkauf, Tino
     A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
    Designing a good scatterplot can be difficult for non-experts in visualization, because they need to decide on many parameters, such as marker size and opacity, aspect ratio, color, and rendering order. This paper contributes to research exploring the use of perceptual models and quality metrics to set such parameters automatically for enhanced visual quality of a scatterplot. A key consideration in this paper is the construction of a cost function to capture several relevant aspects of the human visual system, examining a scatterplot design for some data analysis task. We show how the cost function can be used in an optimizer to search for the optimal visual design for a user’s dataset and task objectives (e.g., "reliable linear correlation estimation is more important than class separation"). The approach is extensible to different analysis tasks. To test its performance in a realistic setting, we pre-calibrated it for correlation estimation, class separation, and outlier detection. The optimizer was able to produce designs that achieved a level of speed and success comparable to that of those using human-designed presets (e.g., in R or MATLAB). Case studies demonstrate that the approach can adapt a design to the data, to reveal patterns without user intervention.