Computing and Information Systems - Research Publications
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ItemPropagation, detection and correction of errors using the sequence database network(OXFORD UNIV PRESS, 2022-10-20)Nucleotide and protein sequences stored in public databases are the cornerstone of many bioinformatics analyses. The records containing these sequences are prone to a wide range of errors, including incorrect functional annotation, sequence contamination and taxonomic misclassification. One source of information that can help to detect errors are the strong interdependency between records. Novel sequences in one database draw their annotations from existing records, may generate new records in multiple other locations and will have varying degrees of similarity with existing records across a range of attributes. A network perspective of these relationships between sequence records, within and across databases, offers new opportunities to detect-or even correct-erroneous entries and more broadly to make inferences about record quality. Here, we describe this novel perspective of sequence database records as a rich network, which we call the sequence database network, and illustrate the opportunities this perspective offers for quantification of database quality and detection of spurious entries. We provide an overview of the relevant databases and describe how the interdependencies between sequence records across these databases can be exploited by network analyses. We review the process of sequence annotation and provide a classification of sources of error, highlighting propagation as a major source. We illustrate the value of a network perspective through three case studies that use network analysis to detect errors, and explore the quality and quantity of critical relationships that would inform such network analyses. This systematic description of a network perspective of sequence database records provides a novel direction to combat the proliferation of errors within these critical bioinformatics resources.
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ItemA structural biology community assessment of AlphaFold2 applications.(Springer Science and Business Media LLC, 2022-11)Most proteins fold into 3D structures that determine how they function and orchestrate the biological processes of the cell. Recent developments in computational methods for protein structure predictions have reached the accuracy of experimentally determined models. Although this has been independently verified, the implementation of these methods across structural-biology applications remains to be tested. Here, we evaluate the use of AlphaFold2 (AF2) predictions in the study of characteristic structural elements; the impact of missense variants; function and ligand binding site predictions; modeling of interactions; and modeling of experimental structural data. For 11 proteomes, an average of 25% additional residues can be confidently modeled when compared with homology modeling, identifying structural features rarely seen in the Protein Data Bank. AF2-based predictions of protein disorder and complexes surpass dedicated tools, and AF2 models can be used across diverse applications equally well compared with experimentally determined structures, when the confidence metrics are critically considered. In summary, we find that these advances are likely to have a transformative impact in structural biology and broader life-science research.
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ItemEditorial: Robots for learning(FRONTIERS MEDIA SA, 2022-10-21)
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ItemkinCSM: Using graph-based signatures to predict small molecule CDK2 inhibitors(WILEY, 2022-11-01)Protein phosphorylation acts as an essential on/off switch in many cellular signaling pathways. This has led to ongoing interest in targeting kinases for therapeutic intervention. Computer-aided drug discovery has been proven a useful and cost-effective approach for facilitating prioritization and enrichment of screening libraries, but limited effort has been devoted providing insights on what makes a potent kinase inhibitor. To fill this gap, here we developed kinCSM, an integrative computational tool capable of accurately identifying potent cyclin-dependent kinase 2 (CDK2) inhibitors, quantitatively predicting CDK2 ligand-kinase inhibition constants (pKi ) and classifying different types of inhibitors based on their favorable binding modes. kinCSM predictive models were built using supervised learning and leveraged the concept of graph-based signatures to capture both physicochemical properties and geometry properties of small molecules. CDK2 inhibitors were accurately identified with Matthew's Correlation Coefficients (MCC) of up to 0.74, and inhibition constants predicted with Pearson's correlation of up to 0.76, both with consistent performances of 0.66 and 0.68 on a nonredundant blind test, respectively. kinCSM was also able to identify the potential type of inhibition for a given molecule, achieving MCC of up to 0.80 on cross-validation and 0.73 on the blind test. Analyzing the molecular composition of revealed enriched chemical fragments in CDK2 inhibitors and different types of inhibitors, which provides insights into the molecular mechanisms behind ligand-kinase interactions. kinCSM will be an invaluable tool to guide future kinase drug discovery. To aid the fast and accurate screening of CDK2 inhibitors, kinCSM is freely available at https://biosig.lab.uq.edu.au/kin_csm/.
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ItemLivestock Identification Using Deep Learning for Traceability(MDPI, 2022-11-01)Farm livestock identification and welfare assessment using non-invasive digital technology have gained interest in agriculture in the last decade, especially for accurate traceability. This study aimed to develop a face recognition system for dairy farm cows using advanced deep-learning models and computer vision techniques. This approach is non-invasive and potentially applicable to other farm animals of importance for identification and welfare assessment. The video analysis pipeline follows standard human face recognition systems made of four significant steps: (i) face detection, (ii) face cropping, (iii) face encoding, and (iv) face lookup. Three deep learning (DL) models were used within the analysis pipeline: (i) face detector, (ii) landmark predictor, and (iii) face encoder. All DL models were finetuned through transfer learning on a dairy cow dataset collected from a robotic dairy farm located in the Dookie campus at The University of Melbourne, Australia. Results showed that the accuracy across videos from 89 different dairy cows achieved an overall accuracy of 84%. The computer program developed may be deployed on edge devices, and it was tested on NVIDIA Jetson Nano board with a camera stream. Furthermore, it could be integrated into welfare assessment previously developed by our research group.
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ItemThe semantic inflation of "trauma" in psychology(University of Warsaw, 2023-01-01)Trauma is an increasingly prominent concept in psychology and society at large. According to the theory of concept creep, it is one of several harm-related concepts that have undergone semantic inflation in recent decades, expanding to encompass new kinds of phenomena (horizontal expansion) and less severe phenomena (vertical expansion). Previous research has demonstrated that "trauma"has come to be used in a widening range of semantic contexts, implying horizontal expansion, but has not investigated vertical expansion. The present study developed a methodology for evaluating vertical expansion and implemented it using an English-language corpus of 825,628 scientific psychology article abstracts from 1970 to 2017. Findings indicate that "trauma"has come to be used in less severe contexts, and this trend may be linked to its rising frequency of use. These findings support the predictions of the concept creep theory and provide a new method for investigating the language dynamics of harm-related concepts.
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ItemTell Touch – A digital health intervention for the Aged Care Sector: An evaluation pilot( 2022)This project reports on a pilot evaluation of Tell Touch - a digital communication application. The evaluation had two purposes. Firstly, the developers of Tell Touch wanted to understand the benefits and challenges of instigating a full evaluation of Tell Touch. Secondly, the effectiveness of Tell Touch as a communication platform for complaints and feedback handling in an Aged Care Home was examined from the perspective of the staff who use the application tool. Tell Touch was developed as a feedback and complaints application tool (app) for use in Aged Care Homes (ACHs). The objective of the app is to improve the quality of care provided to residents by facilitating ACHs to be more consumer-oriented and comply with or exceed the four requirements of the Aged Care Quality and Safety Commission (ACQ&SC) Standard 6. A review of the literature determined the Technology Adoption Model (TAM) as one of the most effective frameworks used in health care settings to assess the adoption of technology. The TAM has been validated in research as a conceptual model that can predict a substantial portion of the use or acceptance of IT health-related settings. Thus, the TAM was used to develop hypothesis to be explored using quantitative data. Qualitative data was collected to better understand the experience of ACH staff in using Tell Touch; specifically, to understand if Tell Touch was perceived as useful, and if Tell Touch satisfied the needs of ACH management for information that would improve services to residents and meet accreditation requirements. The data collected came from eight operational and top managers working in six ACHs across Victoria, and was collected over the period April to October 2022. Findings suggest a full evaluation of Tell Touch is feasible using the research design, tools and methods adopted in this project. Furthermore, early findings from this pilot evaluation indicate Tell Touch does meet the purposes for which it was developed; that is it is an effective IT communication platform for complaints and feedback handling in ACHs.
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ItemEngaging with Nature through Technology: a Scoping Review of HCI Research(ACM, 2023)Technological progress has often been measured by the extent to which it shields and protects us from the harshness of nature. At the same time, it has long been recognised that our resulting disengagement from nature negatively affects our wellbeing, and impedes the awareness of our vital dependence on natural environments. To understand how HCI has considered the possibilities that digital technology offers for engaging with nature, we conducted a scoping review encompassing more than 20 years of HCI research on nature engagement. We compare the orientations, motivations, and methodologies of different threads within this growing body of work. We show how HCI research has enabled varied forms of direct and indirect engagement with nature, and we develop a typology of the roles proposed for technology in this work. We highlight promising and under-utilised approaches to designing for nature engagement, and discuss directions for future research
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ItemNo Preview AvailableProgramming to Learn: Logic and Computation from a Programming Perspective(ACM, 2022-07-07)Programming problems are commonly used as a learning and assessment activity for learning to program. We believe that programming problems can be effective for broader learning goals. In our large-enrolment course, we have designed special programming problems relevant to logic, discrete mathematics, and the theory of computation, and we have used them for formative and summative assessment. In this report, we reflect on our experience. We aim to leverage our students' programming backgrounds by offering a code-based formalism for our mathematical syllabus. We find we can translate many traditional questions into programming problems of a special kind - calling for 'programs' as simple as a single expression, such as a formula or automaton represented in code. A web-based platform enables self-paced learning with rapid contextual corrective feedback, and helps us scale summative assessment to the size of our cohort. We identify several barriers arising with our approach and discuss how we have attempted to negate them. We highlight the potential of programming problems as a digital learning activity even beyond a logic and computation course.