Figure Text Extraction in Biomedical Literature

Background: Figures are ubiquitous in biomedical full-text articles, and they represent important biomedical knowledge. However, the sheer volume of biomedical publications has made it necessary to develop computational approaches for accessing figures. Therefore, we are developing the Biomedical Figure Search engin

Wndchrm – an open source utility for biological image analysis

<p>Abstract</p> <p>Background</p> <p>Biological imaging is an emerging field, covering a wide range of applications in biological and clinical research. However, while machinery for automated experimenting and data acquisition has been developing rapidly in the past years, automated image analysis often introduces a bottleneck in high content screening.</p> <p>Methods</p> <p><it>Wndchrm </it>is an open source utility for biological image analysis. The software works by first extracting image content descriptors from the raw image, image transforms, and compound image transforms. Then, the most informative features are selected, and the feature vector of each image is used for classification and similarity measurement.</p> <p>Results</p> <p><it>Wndchrm </it>has been tested using several publicly available biological datasets, and provided results which are favorably comparable to the performance of task-specific algorithms developed for these datasets. The simple user interface allows researchers who are not knowledgeable in computer vision methods and have no background in computer programming to apply image analysis to their data.</p> <p>Conclusion</p> <p>We suggest that <it>wndchrm </it>can be effectively used for a wide range of biological image analysis tasks. Using <it>wndchrm </it>can allow scientists to perform automated biological image analysis while avoiding the costly challenge of implementing computer vision and pattern recognition algorithms.</p

Raman spectral imaging in tissue engineering & regenerative medicine applications

The label-free nature of Raman spectroscopy makes it a valuable tool for cellular and tissue characterisation. Its ability to probe molecular vibrations within biological structures without affecting their biochemistry offers an advantage over conventional histological and biochemical assays. Providing a pure investigation of unperturbed biological processes, without the need for introduction of exogenous molecules for labelling, makes the information Raman spectroscopy offers very valuable in deciphering complex biological functions and mechanisms. Raman spectral signatures are unique "fingerprints" of each biomolecule probed and can be used for cellular phenotype characterisation, tissue composition, disease development in a cellular or tissue level and much more. This thesis focuses on the use of Raman spectral imaging in novel biological applications displaying its flexibility across the fields of tissue engineering and regenerative medicine. Bone regeneration was the first biological process investigated, where Raman spectral imaging was used to characterise bioactive glass-assisted bone repair using standard and novel glass compositions. Newly-formed bone quality was assessed using multivariate analysis, showing similar quality between glass compositions and existing bone. Morphological analysis after in vivo implantation of bioactive glass particles showed distinct spectral zones confirming results from existing in vitro models. The second application, focused on the development of a novel Raman-based gene delivery tracking methodology. Viral particles, containing modified viral-nucleotides with alkyne bonds were produced were successfully detected using Raman spectral imaging in cells after infection. The implications of this technology offer a new cell screening methodology for gene therapy. Finally, the potential of Raman spectral imaging as a complementary technique for 3D cell culture systems was explored. A computational framework was developed which allows for the visualisation and quantification of subcellular structures. The accurate 3D reconstruction of whole cells of known architecture from a volumetric hyperspectral Raman dataset was reported here for the first time. Moreover, using spectral unmixing algorithms to quantify subcellular components, revealed an unprecedented molecular specificity. This allowed imaging of cells within hydrogel-based 3D cell culture systems. The synergy of Raman spectral imaging, multivariate and image analysis to answer complex biological questions offers objective biomolecular characterisation, quantification and visualisation of molecular architecture. This work demonstrates the potential of Raman spectroscopy as a valuable complementary tool in tissue engineering and regenerative medicine applications.Open Acces

Automatic Figure Ranking and User Interfacing for Intelligent Figure Search

Figures are important experimental results that are typically reported in full-text bioscience articles. Bioscience researchers need to access figures to validate research facts and to formulate or to test novel research hypotheses. On the other hand, the sheer volume of bioscience literature has made it difficult to access figures. Therefore, we are developing an intelligent figure search engine (http://figuresearch.askhermes.org). Existing research in figure search treats each figure equally, but we introduce a novel concept of "figure ranking": figures appearing in a full-text biomedical article can be ranked by their contribution to the knowledge discovery.We empirically validated the hypothesis of figure ranking with over 100 bioscience researchers, and then developed unsupervised natural language processing (NLP) approaches to automatically rank figures. Evaluating on a collection of 202 full-text articles in which authors have ranked the figures based on importance, our best system achieved a weighted error rate of 0.2, which is significantly better than several other baseline systems we explored. We further explored a user interfacing application in which we built novel user interfaces (UIs) incorporating figure ranking, allowing bioscience researchers to efficiently access important figures. Our evaluation results show that 92% of the bioscience researchers prefer as the top two choices the user interfaces in which the most important figures are enlarged. With our automatic figure ranking NLP system, bioscience researchers preferred the UIs in which the most important figures were predicted by our NLP system than the UIs in which the most important figures were randomly assigned. In addition, our results show that there was no statistical difference in bioscience researchers' preference in the UIs generated by automatic figure ranking and UIs by human ranking annotation.The evaluation results conclude that automatic figure ranking and user interfacing as we reported in this study can be fully implemented in online publishing. The novel user interface integrated with the automatic figure ranking system provides a more efficient and robust way to access scientific information in the biomedical domain, which will further enhance our existing figure search engine to better facilitate accessing figures of interest for bioscientists