Using machine learning to support better and intelligent visualisation for genomic data

Massive amounts of genomic data are created for the advent of Next Generation Sequencing technologies. Great technological advances in methods of characterising the human diseases, including genetic and environmental factors, make it a great opportunity to understand the diseases and to find new diagnoses and treatments. Translating medical data becomes more and more rich and challenging. Visualisation can greatly aid the processing and integration of complex data. Genomic data visual analytics is rapidly evolving alongside with advances in high-throughput technologies such as Artificial Intelligence (AI), and Virtual Reality (VR). Personalised medicine requires new genomic visualisation tools, which can efficiently extract knowledge from the genomic data effectively and speed up expert decisions about the best treatment of an individual patient’s needs. However, meaningful visual analysis of such large genomic data remains a serious challenge. Visualising these complex genomic data requires not only simply plotting of data but should also lead to better decisions. Machine learning has the ability to make prediction and aid in decision-making. Machine learning and visualisation are both effective ways to deal with big data, but they focus on different purposes. Machine learning applies statistical learning techniques to automatically identify patterns in data to make highly accurate prediction, while visualisation can leverage the human perceptual system to interpret and uncover hidden patterns in big data. Clinicians, experts and researchers intend to use both visualisation and machine learning to analyse their complex genomic data, but it is a serious challenge for them to understand and trust machine learning models in the serious medical industry. The main goal of this thesis is to study the feasibility of intelligent and interactive visualisation which combined with machine learning algorithms for medical data analysis. A prototype has also been developed to illustrate the concept that visualising genomics data from childhood cancers in meaningful and dynamic ways could lead to better decisions. Machine learning algorithms are used and illustrated during visualising the cancer genomic data in order to provide highly accurate predictions. This research could open a new and exciting path to discovery for disease diagnostics and therapies

How sketches work: a cognitive theory for improved system design

Evidence is presented that in the early stages of design or composition the mental processes used by artists for visual invention require a different type of support from those used for visualising a nearly complete object. Most research into machine visualisation has as its goal the production of realistic images which simulate the light pattern presented to the retina by real objects. In contrast sketch attributes preserve the results of cognitive processing which can be used interactively to amplify visual thought. The traditional attributes of sketches include many types of indeterminacy which may reflect the artist's need to be "vague". Drawing on contemporary theories of visual cognition and neuroscience this study discusses in detail the evidence for the following functions which are better served by rough sketches than by the very realistic imagery favoured in machine visualising systems. 1. Sketches are intermediate representational types which facilitate the mental translation between descriptive and depictive modes of representing visual thought. 2. Sketch attributes exploit automatic processes of perceptual retrieval and object recognition to improve the availability of tacit knowledge for visual invention. 3. Sketches are percept-image hybrids. The incomplete physical attributes of sketches elicit and stabilise a stream of super-imposed mental images which amplify inventive thought. 4. By segregating and isolating meaningful components of visual experience, sketches may assist the user to attend selectively to a limited part of a visual task, freeing otherwise over-loaded cognitive resources for visual thought. 5. Sequences of sketches and sketching acts support the short term episodic memory for cognitive actions. This assists creativity, providing voluntary control over highly practised mental processes which can otherwise become stereotyped. An attempt is made to unite the five hypothetical functions. Drawing on the Baddeley and Hitch model of working memory, it is speculated that the five functions may be related to a limited capacity monitoring mechanism which makes tacit visual knowledge explicitly available for conscious control and manipulation. It is suggested that the resources available to the human brain for imagining nonexistent objects are a cultural adaptation of visual mechanisms which evolved in early hominids for responding to confusing or incomplete stimuli from immediately present objects and events. Sketches are cultural inventions which artificially mimic aspects of such stimuli in order to capture these shared resources for the different purpose of imagining objects which do not yet exist. Finally the implications of the theory for the design of improved machine systems is discussed. The untidy attributes of traditional sketches are revealed to include cultural inventions which serve subtle cognitive functions. However traditional media have many short-comings which it should be possible to correct with new technology. Existing machine systems for sketching tend to imitate nonselectively the media bound properties of sketches without regard to the functions they serve. This may prove to be a mistake. It is concluded that new system designs are needed in which meaningfully structured data and specialised imagery amplify without interference or replacement the impressive but limited creative resources of the visual brain

On the effective visualisation of dynamic attribute cascades

Cascades appear in many applications, including biological graphs and social media analysis. In a cascade, a dynamic attribute propagates through a graph, following its edges. We present the results of a formal user study that tests the effectiveness of different types of cascade visualisations on node-link diagrams for the task of judging cascade spread. Overall, we found that a small multiples presentation was significantly faster than animation with no significant difference in terms of error rate. Participants generally preferred animation over small multiples and a hierarchical layout to a force-directed layout. Considering each presentation method separately, when comparing force-directed layouts to hierarchical layouts, hierarchical layouts were found to be significantly faster for both presentation methods and significantly more accurate for animation. Representing the history of the cascade had no significant effect. Thus, for our task, this experiment supports the use of a small multiples interface with hierarchically drawn graphs for the visualisation of cascades. This work is important because without these empirical results, designers of dynamic multivariate visualisations (in many applications) would base their design decisions on intuition with little empirical support as to whether these decisions enhance usability

A survey of visualisation for live cell imaging

Live cell imaging is an important biomedical research paradigm for studying dynamic cellular behaviour. Although phenotypic data derived from images are difficult to explore and analyse, some researchers have successfully addressed this with visualisation. Nonetheless, visualisation methods for live cell imaging data have been reported in an ad hoc and fragmented fashion. This leads to a knowledge gap where it is difficult for biologists and visualisation developers to evaluate the advantages and disadvantages of different visualisation methods, and for visualisation researchers to gain an overview of existing work to identify research priorities. To address this gap, we survey existing visualisation methods for live cell imaging from a visualisation research perspective for the first time. Based on recent visualisation theory, we perform a structured qualitative analysis of visualisation methods that includes characterising the domain and data, abstracting tasks, and describing visual encoding and interaction design. Based on our survey, we identify and discuss research gaps that future work should address: the broad analytical context of live cell imaging; the importance of behavioural comparisons; links with dynamic data visualisation; the consequences of different data modalities; shortcomings in interactive support; and, in addition to analysis, the value of the presentation of phenotypic data and insights to other stakeholders

Visualisation of multi-dimensional medical images with application to brain electrical impedance tomography

Medical imaging plays an important role in modem medicine. With the increasing complexity and information presented by medical images, visualisation is vital for medical research and clinical applications to interpret the information presented in these images. The aim of this research is to investigate improvements to medical image visualisation, particularly for multi-dimensional medical image datasets. A recently developed medical imaging technique known as Electrical Impedance Tomography (EIT) is presented as a demonstration. To fulfil the aim, three main efforts are included in this work. First, a novel scheme for the processmg of brain EIT data with SPM (Statistical Parametric Mapping) to detect ROI (Regions of Interest) in the data is proposed based on a theoretical analysis. To evaluate the feasibility of this scheme, two types of experiments are carried out: one is implemented with simulated EIT data, and the other is performed with human brain EIT data under visual stimulation. The experimental results demonstrate that: SPM is able to localise the expected ROI in EIT data correctly; and it is reasonable to use the balloon hemodynamic change model to simulate the impedance change during brain function activity. Secondly, to deal with the absence of human morphology information in EIT visualisation, an innovative landmark-based registration scheme is developed to register brain EIT image with a standard anatomical brain atlas. Finally, a new task typology model is derived for task exploration in medical image visualisation, and a task-based system development methodology is proposed for the visualisation of multi-dimensional medical images. As a case study, a prototype visualisation system, named EIT5DVis, has been developed, following this methodology. to visualise five-dimensional brain EIT data. The EIT5DVis system is able to accept visualisation tasks through a graphical user interface; apply appropriate methods to analyse tasks, which include the ROI detection approach and registration scheme mentioned in the preceding paragraphs; and produce various visualisations

Immersive analytics for oncology patient cohorts

This thesis proposes a novel interactive immersive analytics tool and methods to interrogate the cancer patient cohort in an immersive virtual environment, namely Virtual Reality to Observe Oncology data Models (VROOM). The overall objective is to develop an immersive analytics platform, which includes a data analytics pipeline from raw gene expression data to immersive visualisation on virtual and augmented reality platforms utilising a game engine. Unity3D has been used to implement the visualisation. Work in this thesis could provide oncologists and clinicians with an interactive visualisation and visual analytics platform that helps them to drive their analysis in treatment efficacy and achieve the goal of evidence-based personalised medicine. The thesis integrates the latest discovery and development in cancer patients’ prognoses, immersive technologies, machine learning, decision support system and interactive visualisation to form an immersive analytics platform of complex genomic data. For this thesis, the experimental paradigm that will be followed is in understanding transcriptomics in cancer samples. This thesis specifically investigates gene expression data to determine the biological similarity revealed by the patient's tumour samples' transcriptomic profiles revealing the active genes in different patients. In summary, the thesis contributes to i) a novel immersive analytics platform for patient cohort data interrogation in similarity space where the similarity space is based on the patient's biological and genomic similarity; ii) an effective immersive environment optimisation design based on the usability study of exocentric and egocentric visualisation, audio and sound design optimisation; iii) an integration of trusted and familiar 2D biomedical visual analytics methods into the immersive environment; iv) novel use of the game theory as the decision-making system engine to help the analytics process, and application of the optimal transport theory in missing data imputation to ensure the preservation of data distribution; and v) case studies to showcase the real-world application of the visualisation and its effectiveness

Knowledge-sourcing strategies for cross-disciplinarity in bionanotechnology

interdisciplinarity, collaboration, bionanotechnology, research, knowledge-sourcing, molecular motors

Visual analysis of anatomy ontologies and related genomic information

Challenges in scientific research include the difficulty in obtaining overviews of the large amount of data required for analysis, and in resolving the differences in terminology used to store and interpret information in multiple, independently created data sets. Ontologies provide one solution for analysis involving multiple data sources, improving cross-referencing and data integration. This thesis looks at harnessing advanced human perception to reduce the cognitive load in the analysis of the multiple, complex data sets the bioinformatics user group studied use in research, taking advantage also of users’ domain knowledge, to build mental models of data that map to its underlying structure. Guided by a user-centred approach, prototypes were developed to provide a visual method for exploring users’ information requirements and to identify solutions for these requirements. 2D and 3D node-link graphs were built to visualise the hierarchically structured ontology data, to improve analysis of individual and comparison of multiple data sets, by providing overviews of the data, followed by techniques for detailed analysis of regions of interest. Iterative, heuristic and structured user evaluations were used to assess and refine the options developed for the presentation and analysis of the ontology data. The evaluation results confirmed the advantages that visualisation provides over text-based analysis, and also highlighted the advantages of each of 2D and 3D for visual data analysis.Overseas Research Students Awards SchemeJames Watt Scholarshi