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

Review of innovative immersive technologies for healthcare applications

Immersive technologies, including virtual reality (VR), augmented reality (AR), and mixed reality (MR), can connect people using enhanced data visualizations to better involve stakeholders as integral members of the process. Immersive technologies have started to change the research on multidimensional genomic data analysis for disease diagnostics and treatments. Immersive technologies are highlighted in some research for health and clinical needs, especially for precision medicine innovation. The use of immersive technology for genomic data analysis has recently received attention from the research community. Genomic data analytics research seeks to integrate immersive technologies to build more natural human-computer interactions that allow better perception engagements. Immersive technologies, especially VR, help humans perceive the digital world as real and give learning output with lower performance errors and higher accuracy. However, there are limited reviews about immersive technologies used in healthcare and genomic data analysis with specific digital health applications. This paper contributes a comprehensive review of using immersive technologies for digital health applications, including patient-centric applications, medical domain education, and data analysis, especially genomic data visual analytics. We highlight the evolution of a visual analysis using VR as a case study for how immersive technologies step, can by step, move into the genomic data analysis domain. The discussion and conclusion summarize the current immersive technology applications’ usability, innovation, and future work in the healthcare domain, and digital health data visual analytics

Guidelines for the use and interpretation of assays for monitoring autophagy (3rd edition)

In 2008 we published the first set of guidelines for standardizing research in autophagy. Since then, research on this topic has continued to accelerate, and many new scientists have entered the field. Our knowledge base and relevant new technologies have also been expanding. Accordingly, it is important to update these guidelines for monitoring autophagy in different organisms. Various reviews have described the range of assays that have been used for this purpose. Nevertheless, there continues to be confusion regarding acceptable methods to measure autophagy, especially in multicellular eukaryotes. For example, a key point that needs to be emphasized is that there is a difference between measurements that monitor the numbers or volume of autophagic elements (e.g., autophagosomes or autolysosomes) at any stage of the autophagic process versus those that measure fl ux through the autophagy pathway (i.e., the complete process including the amount and rate of cargo sequestered and degraded). In particular, a block in macroautophagy that results in autophagosome accumulation must be differentiated from stimuli that increase autophagic activity, defi ned as increased autophagy induction coupled with increased delivery to, and degradation within, lysosomes (inmost higher eukaryotes and some protists such as Dictyostelium ) or the vacuole (in plants and fungi). In other words, it is especially important that investigators new to the fi eld understand that the appearance of more autophagosomes does not necessarily equate with more autophagy. In fact, in many cases, autophagosomes accumulate because of a block in trafficking to lysosomes without a concomitant change in autophagosome biogenesis, whereas an increase in autolysosomes may reflect a reduction in degradative activity. It is worth emphasizing here that lysosomal digestion is a stage of autophagy and evaluating its competence is a crucial part of the evaluation of autophagic flux, or complete autophagy. Here, we present a set of guidelines for the selection and interpretation of methods for use by investigators who aim to examine macroautophagy and related processes, as well as for reviewers who need to provide realistic and reasonable critiques of papers that are focused on these processes. These guidelines are not meant to be a formulaic set of rules, because the appropriate assays depend in part on the question being asked and the system being used. In addition, we emphasize that no individual assay is guaranteed to be the most appropriate one in every situation, and we strongly recommend the use of multiple assays to monitor autophagy. Along these lines, because of the potential for pleiotropic effects due to blocking autophagy through genetic manipulation it is imperative to delete or knock down more than one autophagy-related gene. In addition, some individual Atg proteins, or groups of proteins, are involved in other cellular pathways so not all Atg proteins can be used as a specific marker for an autophagic process. In these guidelines, we consider these various methods of assessing autophagy and what information can, or cannot, be obtained from them. Finally, by discussing the merits and limits of particular autophagy assays, we hope to encourage technical innovation in the field

Parallel nonlinear dimensionality reduction using GPU acceleration

Dimensionality reduction is usually an essential step in data mining and classical machine learning from high-dimensional data. Uniform Manifold Approximations Projections (UMAP) is a recently developed nonlinear dimensionality reduction method that is being widely applied in biomedical informatics. However, the UMAP implementation is still not efficient enough for processing the recent big omics data from biomedicine. This paper proposes and implements a method that reduces UMAP runtime using GPU-acceleration on the GPU-RAPIDS platform. Our experiments showed that the parallel UMAP implementation performed hundred times faster than the original UMAP implementation on a cluster computer, while maintaining the effectiveness on identifying leukemic cells from clinical flow cytometry data

Understanding cancer patient cohorts in virtual reality environment for better clinical decisions: a usability study

Abstract Background Visualising patient genomic data in a cohort with embedding data analytics models can provide relevant and sensible patient comparisons to assist a clinician with treatment decisions. As immersive technology is actively used around the medical world, there is a rising demand for an efficient environment that can effectively display genomic data visualisations on immersive devices such as a Virtual Reality (VR) environment. The VR technology will allow clinicians, biologists, and computer scientists to explore a cohort of individual patients within the 3D environment. However, demonstrating the feasibility of the VR prototype needs domain users’ feedback for future user-centred design and a better cognitive model of human–computer interactions. There is limited research work for collecting and integrating domain knowledge into the prototype design. Objective A usability study for the VR prototype–-Virtual Reality to Observe Oncology data Models (VROOM) was implemented. VROOM was designed based on a preliminary study among medical users. The goals of this usability study included establishing a baseline of user experience, validating user performance measures, and identifying potential design improvements that are to be addressed to improve efficiency, functionality, and end-user satisfaction. Methods The study was conducted with a group of domain users (10 males, 10 females) with portable VR devices and camera equipment. These domain users included medical users such as clinicians and genetic scientists and computing domain users such as bioinformatics and data analysts. Users were asked to complete routine tasks based on a clinical scenario. Sessions were recorded and analysed to identify potential areas for improvement to the data visual analytics projects in the VR environment. The one-hour usability study included learning VR interaction gestures, running visual analytics tool, and collecting before and after feedback. The feedback was analysed with different methods to measure effectiveness. The statistical method Mann–Whitney U test was used to analyse various task performances among the different participant groups, and multiple data visualisations were created to find insights from questionnaire answers. Results The usability study investigated the feasibility of using VR for genomic data analysis in domain users’ daily work. From the feedback, 65% of the participants, especially clinicians (75% of them), indicated that the VR prototype is potentially helpful for domain users’ daily work but needed more flexibility, such as allowing them to define their features for machine learning part, adding new patient data, and importing their datasets in a better way. We calculated the engaged time for each task and compared them among different user groups. Computing domain users spent 50% more time exploring the algorithms and datasets than medical domain users. Additionally, the medical domain users engaged in the data visual analytics parts (approximately 20%) longer than the computing domain users

Visual analytics of genomic and cancer data : a systematic review

Visual analytics and visualisation can leverage the human perceptual system to interpret and uncover hidden patterns in big data. The advent of next-generation sequencing technologies has allowed the rapid production of massive amounts of genomic data and created a corresponding need for new tools and methods for visualising and interpreting these data. Visualising genomic data requires not only simply plotting of data but should also offer a decision or a choice about what the message should be conveyed in the particular plot; which methodologies should be used to represent the results must provide an easy, clear, and accurate way to the clinicians, experts, or researchers to interact with the data. Genomic data visual analytics is rapidly evolving in parallel 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 and speed up expert decisions about the best treatment of individual patient’s needs. However, meaningful visual analytics of such large genomic data remains a serious challenge. This article provides a comprehensive systematic review and discussion on the tools, methods, and trends for visual analytics of cancer-related genomic data. We reviewed methods for genomic data visualisation including traditional approaches such as scatter plots, heatmaps, coordinates, and networks, as well as emerging technologies using AI and VR. We also demonstrate the development of genomic data visualisation tools over time and analyse the evolution of visualising genomic data

Intelligent and immersive visual analytics of health data

Massive amounts of health data have been created together with the advent of computer technologies and next generation sequencing technologies. Analytical techniques can significantly aid in the processing, integration and interpretation of the complex data. Visual analytics field has been rapidly evolving together with the advancement in automated analysis methods such as data mining, machine learning and statistics, visualization, and immersive technologies. Although automated analysis processes greatly support the decision making, conservative domains such as medicine, banking, and insurance need trusts on machine learning models. Explainable artificial intelligence could open the black boxes of the machine learning models to improve the trusts for decision makers. Immersive technologies allow the users to engage naturally with the blended reality in where they can look the information in different angles in addition to traditional screens. This chapter reviews and discusses the intelligent visualization, artificial intelligence and immersive technologies in health domain. We also illustrate the ideas with various case studies in genomic data visual analytics

Intracellular Delivery Platform for “Recalcitrant” Cells: When Polymeric Carrier Marries Photoporation

The intracellular delivery of exogenous macromolecules is of great interest for both fundamental biological research and clinical applications. Although traditional delivery systems based on either carrier mediation or membrane disruption have some advantages; however, they are generally limited with respect to delivery efficiency and cytotoxicity. Herein, a collaborative intracellular delivery platform with excellent comprehensive performance is developed using polyethylenimine of low molecular weight (LPEI) as a gene carrier in conjunction with a gold nanoparticle layer (GNPL) acting as a photoporation agent. In this system, the LPEI protects the plasmid DNA (pDNA) to avoid possible nuclease degradation, and the GNPL improves the delivery efficiency of the LPEI/pDNA complex to the cells. The collaboration of LPEI and GNPL is shown to give significantly higher transfection efficiencies for hard-to-transfect cells (88.5 ± 9.2% for mouse embryonic fibroblasts, 94.0 ± 6.3% for human umbilical vein endothelial cells) compared to existing techniques without compromising cell viability

Virtual reality for the observation of oncology models (VROOM) : immersive analytics for oncology patient cohorts

The significant advancement of inexpensive and portable virtual reality (VR) and augmented reality devices has re-energised the research in the immersive analytics field. The immersive environment is different from a traditional 2D display used to analyse 3D data as it provides a unified environment that supports immersion in a 3D scene, gestural interaction, haptic feedback and spatial audio. Genomic data analysis has been used in oncology to understand better the relationship between genetic profile, cancer type, and treatment option. This paper proposes a novel immersive analytics tool for cancer patient cohorts in a virtual reality environment, virtual reality to observe oncology data models. We utilise immersive technologies to analyse the gene expression and clinical data of a cohort of cancer patients. Various machine learning algorithms and visualisation methods have also been deployed in VR to enhance the data interrogation process. This is supported with established 2D visual analytics and graphical methods in bioinformatics, such as scatter plots, descriptive statistical information, linear regression, box plot and heatmap into our visualisation. Our approach allows the clinician to interrogate the information that is familiar and meaningful to them while providing them immersive analytics capabilities to make new discoveries toward personalised medicine