Biomedical Discovery Acceleration, with Applications to Craniofacial Development

The profusion of high-throughput instruments and the explosion of new results in the scientific literature, particularly in molecular biomedicine, is both a blessing and a curse to the bench researcher. Even knowledgeable and experienced scientists can benefit from computational tools that help navigate this vast and rapidly evolving terrain. In this paper, we describe a novel computational approach to this challenge, a knowledge-based system that combines reading, reasoning, and reporting methods to facilitate analysis of experimental data. Reading methods extract information from external resources, either by parsing structured data or using biomedical language processing to extract information from unstructured data, and track knowledge provenance. Reasoning methods enrich the knowledge that results from reading by, for example, noting two genes that are annotated to the same ontology term or database entry. Reasoning is also used to combine all sources into a knowledge network that represents the integration of all sorts of relationships between a pair of genes, and to calculate a combined reliability score. Reporting methods combine the knowledge network with a congruent network constructed from experimental data and visualize the combined network in a tool that facilitates the knowledge-based analysis of that data. An implementation of this approach, called the Hanalyzer, is demonstrated on a large-scale gene expression array dataset relevant to craniofacial development. The use of the tool was critical in the creation of hypotheses regarding the roles of four genes never previously characterized as involved in craniofacial development; each of these hypotheses was validated by further experimental work

2100 AI: Reflections on the mechanisation of scientific discovery

The pace of research is nowadays extremely intensive, with datasets and publications being published at an unprecedented rate. In this context data science, artificial intelligence, machine learning and big data analytics are providing researchers with new automatic techniques which not only help them to manage this flow of information but are also able to identify automatically interesting patterns and insights in this vast sea of information. However, the emergence of mechanised scientific discovery is likely to dramatically change the way we do science, thus introducing and amplifying serious societal implications on the role of researchers themselves, which need to be analysed thoroughly

DoR Communicator - January 2014

The January 2014 issue of the Division of Research newsletter.https://digitalcommons.fiu.edu/research_newsletter/1007/thumbnail.jp

Dental and nondental stem cell based regeneration of the craniofacial region: a tissue based approach

Craniofacial reconstruction may be a necessary treatment for those who have been affected by trauma, disease, or pathological developmental conditions. The use of stem cell therapy and tissue engineering shows massive potential as a future treatment modality. Currently in the literature, there is a wide variety of published experimental studies utilising the different stem cell types available and the plethora of available scaffold materials. This review investigates different stem cell sources and their unique characteristics to suggest an ideal cell source for regeneration of individual craniofacial tissues. At present, understanding and clinical applications of stem cell therapy remain in their infancy with numerous challenges to overcome. In spite of this, the field displays immense capacity and will no doubt be utilised in future clinical treatments of craniofacial regeneration

Translational web robots for pathogen genome analysis

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