Recent advances of HCI in decision-making tasks for optimized clinical workflows and precision medicine.

The ever-increasing amount of biomedical data is enabling new large-scale studies, even though ad hoc computational solutions are required. The most recent Machine Learning (ML) and Artificial Intelligence (AI) techniques have been achieving outstanding performance and an important impact in clinical research, aiming at precision medicine, as well as improving healthcare workflows. However, the inherent heterogeneity and uncertainty in the healthcare information sources pose new compelling challenges for clinicians in their decision-making tasks. Only the proper combination of AI and human intelligence capabilities, by explicitly taking into account effective and safe interaction paradigms, can permit the delivery of care that outperforms what either can do separately. Therefore, Human-Computer Interaction (HCI) plays a crucial role in the design of software oriented to decision-making in medicine. In this work, we systematically review and discuss several research fields strictly linked to HCI and clinical decision-making, by subdividing the articles into six themes, namely: Interfaces, Visualization, Electronic Health Records, Devices, Usability, and Clinical Decision Support Systems. However, these articles typically present overlaps among the themes, revealing that HCI inter-connects multiple topics. With the goal of focusing on HCI and design aspects, the articles under consideration were grouped into four clusters. The advances in AI can effectively support the physicians' cognitive processes, which certainly play a central role in decision-making tasks because the human mental behavior cannot be completely emulated and captured; the human mind might solve a complex problem even without a statistically significant amount of data by relying upon domain knowledge. For this reason, technology must focus on interactive solutions for supporting the physicians effectively in their daily activities, by exploiting their unique knowledge and evidence-based reasoning, as well as improving the various aspects highlighted in this review

Investigation of de novo mutations in human genomes using whole genome sequencing data

De novo mutations (DNMs) are novel mutations which occur for the first time in an offspring and are not inherited from the parents. High-Throughput Sequencing (HTS) technologies such as whole genome sequencing (WGS) and whole exome sequencing (WES) of trios have allowed the investigation of DNMs and their role in diseases. Increased contribution of DNMs in both rare monogenic and common complex disorders is now known. Identification of DNMs from WGS is challenging since the error rates in the HTS data are much higher than the expected DNM rate. To facilitate the evaluation of existing DNM callers and development of new callers, I developed TrioSim, the first automated tool to generate simulated WGS datasets for trios with a feature to spike-in DNMs in the offspring WGS data. Several computational methods have been developed to call DNMs from HTS data. I performed the first systematic evaluation of current DNM callers for WGS trio data using real dataset and simulated trio datasets and found that DNM callers have high sensitivity and can detect the majority of true DNMs. However, they suffer from very low specificity with thousands of false positive calls made by each caller. To address this, I developed MetaDeNovo, a consensus-based ensemble computational method to call DNMs using cloud-based technologies. MetaDeNovo is a fully automated methodology that utilises existing DNM callers and integrates their results. It demonstrates much higher specificity than all other callers while maintaining high sensitivity. Congenital Heart Disease (CHD) is the most common birth disorder worldwide. DNMs have been found to contribute to CHD causation. Most CHD cases are sporadic, suggesting role of DNMs in large proportion of them. I applied MetaDeNovo to detect DNMs in a WGS dataset of CHD trios to aid with genetic variant prioritisation. MetaDeNovo can dramatically reduce the number of false positive DNMs as compared to individual DNM callers. This has improved the current practices of identifying the genetic causes of disease in such cohorts. MetaDeNovo is applicable to other trio WGS datasets of other genetic diseases. This thesis has contributed new knowledge by in depth exploration of existing DNM callers, development of a novel tool (TrioSim) to simulate trio WGS data and an ensemble improved automated tool (MetaDeNovo) to identify DNMs with high specificity. MetaDeNovo demonstrates its use to identify disease-causing mutations in a trio analysis using WGS

Emerging technologies and their impact on regulatory science

There is an evolution and increasing need for the utilization of emerging cellular, molecular and in silico technologies and novel approaches for safety assessment of food, drugs, and personal care products. Convergence of these emerging technologies is also enabling rapid advances and approaches that may impact regulatory decisions and approvals. Although the development of emerging technologies may allow rapid advances in regulatory decision making, there is concern that these new technologies have not been thoroughly evaluated to determine if they are ready for regulatory application, singularly or in combinations. The magnitude of these combined technical advances may outpace the ability to assess fit for purpose and to allow routine application of these new methods for regulatory purposes. There is a need to develop strategies to evaluate the new technologies to determine which ones are ready for regulatory use. The opportunity to apply these potentially faster, more accurate, and cost-effective approaches remains an important goal to facilitate their incorporation into regulatory use. However, without a clear strategy to evaluate emerging technologies rapidly and appropriately, the value of these efforts may go unrecognized or may take longer. It is important for the regulatory science field to keep up with the research in these technically advanced areas and to understand the science behind these new approaches. The regulatory field must understand the critical quality attributes of these novel approaches and learn from each other's experience so that workforces can be trained to prepare for emerging global regulatory challenges. Moreover, it is essential that the regulatory community must work with the technology developers to harness collective capabilities towards developing a strategy for evaluation of these new and novel assessment tools