Implications and challenges to using data mining in educational research in the Canadian context

Canadian institutions of higher education are major players on the international arena for educating future generations and producing leaders around the world in various fields. In the last decade, Canadian universities have seen an influx in their incoming international students, who contribute over 3.5 billion to the Canadian economy (Madgett & Bélanger 2008, p. 195). Research in Canadian post-secondary institutions is booming, especially in education (SSHRC, 2011)—for the academic year 2010-2011, of the 12 subject areas, the total SSHRC funding for projects in education, ranked fourth, exceeding 27 million. All of these variables place Canadian higher education in a leading and strategic position in several educational research fields. One can imagine the wealth of knowledge about trends in higher education that could be revealed if the large amount of data generated by Canadian universities were systematically analyzed and handled using techniques such as data mining. However, not much can be achieved from the unharnessed knowledge accumulated on a daily basis, as the advancement of data mining research that would provide the ultimate tool to learn about trends and changes in Canadian institutions is often held back by inadequate data warehousing, as well as by privacy, confidentiality, and copyright regulations. In this paper, we engage in a critical discussion/analysis of the interface between data mining research in higher education and the legal implications of such a tool.Les établissements canadiens d'enseignement supérieur jouent un rôle majeur sur la scène internationale dans l'éducation des générations futures et dans la formation de leaders dans divers domaines à travers le monde. Au cours de la dernière décennie, les universités canadiennes ont connu un afflux d'étudiants internationaux, qui contribuent plus de 3,5 milliards de dollars à l'économie canadienne (Bélanger & Madgett 2008, p. 195). La recherche dans les institutions canadiennes d'enseignement postsecondaire est en plein essor, en particulier en matière d'éducation (CRSH, 2011) - pour l'année académique 2010-2011, parmi les 12 domaines de recherche, le financement total du CRSH pour les projets portant sur l'éducation, au quatrième rang, s'élevait à plus de 27 millions de dollars. Toutes ces variables placent l'enseignement supérieur canadien dans une position stratégique et de premier plan dans plusieurs domaines de recherche en éducation. On peut imaginer la richesse des informations sur les tendances dans l'enseignement supérieur qui pourrait être révélée si la masse de données générée par les universités canadiennes était systématiquement analysée et traitée en utilisant des techniques telle que l'exploration de données. Cependant, on ne peut guère obtenir grand chose à partir des informations accumulées sur une base quotidienne, étant donné que l'avancement de la recherche à exploration de données, qui serait l'outil ultime pour en apprendre davantage sur les tendances et les changements dans les institutions canadiennes, est souvent freinée par un entreposage de données insuffisant, ainsi que par les règlementations sur la protection des renseignements personnels, la confidentialité et le droit d'auteur. Dans cet article, nous engageons une discussion et une analyse critiques de l'interface entre la recherche à exploration de données dans l'enseignement supérieur et les implications juridiques d'un tel outil

Applications of Machine Learning in Human Microbiome Studies: A Review on Feature Selection, Biomarker Identification, Disease Prediction and Treatment

The number of microbiome-related studies has notably increased the availability of data on human microbiome composition and function. These studies provide the essential material to deeply explore host-microbiome associations and their relation to the development and progression of various complex diseases. Improved data-analytical tools are needed to exploit all information from these biological datasets, taking into account the peculiarities of microbiome data, i.e., compositional, heterogeneous and sparse nature of these datasets. The possibility of predicting host-phenotypes based on taxonomy-informed feature selection to establish an association between microbiome and predict disease states is beneficial for personalized medicine. In this regard, machine learning (ML) provides new insights into the development of models that can be used to predict outputs, such as classification and prediction in microbiology, infer host phenotypes to predict diseases and use microbial communities to stratify patients by their characterization of state-specific microbial signatures. Here we review the state-of-the-art ML methods and respective software applied in human microbiome studies, performed as part of the COST Action ML4Microbiome activities. This scoping review focuses on the application of ML in microbiome studies related to association and clinical use for diagnostics, prognostics, and therapeutics. Although the data presented here is more related to the bacterial community, many algorithms could be applied in general, regardless of the feature type. This literature and software review covering this broad topic is aligned with the scoping review methodology. The manual identification of data sources has been complemented with: (1) automated publication search through digital libraries of the three major publishers using natural language processing (NLP) Toolkit, and (2) an automated identification of relevant software repositories on GitHub and ranking of the related research papers relying on learning to rank approach

Projected Impact of Compositional Verification on Current and Future Aviation Safety Risk

The projected impact of compositional verification research conducted by the National Aeronautic and Space Administration System-Wide Safety and Assurance Technologies on aviation safety risk was assessed. Software and compositional verification was described. Traditional verification techniques have two major problems: testing at the prototype stage where error discovery can be quite costly and the inability to test for all potential interactions leaving some errors undetected until used by the end user. Increasingly complex and nondeterministic aviation systems are becoming too large for these tools to check and verify. Compositional verification is a "divide and conquer" solution to addressing increasingly larger and more complex systems. A review of compositional verification research being conducted by academia, industry, and Government agencies is provided. Forty-four aviation safety risks in the Biennial NextGen Safety Issues Survey were identified that could be impacted by compositional verification and grouped into five categories: automation design; system complexity; software, flight control, or equipment failure or malfunction; new technology or operations; and verification and validation. One capability, 1 research action, 5 operational improvements, and 13 enablers within the Federal Aviation Administration Joint Planning and Development Office Integrated Work Plan that could be addressed by compositional verification were identified

Moving beyond DNA: towards functional analysis of the vaginal microbiome by non-sequencing-based methods

Over the last two decades, sequencing-based methods have revolutionised our understanding of niche-specific microbial complexity. In the lower female reproductive tract, these approaches have enabled identification of bacterial compositional structures associated with health and disease. Application of metagenomics and metatranscriptomics strategies have provided insight into the putative function of these communities but it is increasingly clear that direct measures of microbial and host cell function are required to understand the contribution of microbe–host interactions to pathophysiology. Here we explore and discuss current methods and approaches, many of which rely upon mass-spectrometry, being used to capture functional insight into the vaginal mucosal interface. In addition to improving mechanistic understanding, these methods offer innovative solutions for the development of diagnostic and therapeutic strategies designed to improve women’s health