Optimization and Applications of Large-Scale Biomedical Event Networks

The overwhelming amount and unprecedented speed of publication in the biomedical domain make it difficult for life science researchers to acquire and maintain a broad view of the field and gather all information that would be relevant for their research. As a response to this problem, the BioNLP (Biomedical Natural Language Processing) community of researches has emerged and strives to assist life science researchers by developing modern natural language processing (NLP), information extraction (IE) and information retrieval (IR) methods that can be applied at large-scale, to scan the whole publicly available biomedical literature and extract and aggregate the information found within, while automatically normalizing the variability of natural language statements. Among different tasks, biomedical event extraction has received much attention within BioNLP community recently. Biomedical event extraction constitutes the identification of biological processes and interactions described in biomedical literature, and their representation as a set of recursive event structures. The 2009–2013 series of BioNLP Shared Tasks on Event Extraction have given raise to a number of event extraction systems, several of which have been applied at a large scale (the full set of PubMed abstracts and PubMed Central Open Access full text articles), leading to creation of massive biomedical event databases, each of which containing millions of events. Sinece top-ranking event extraction systems are based on machine-learning approach and are trained on the narrow-domain, carefully selected Shared Task training data, their performance drops when being faced with the topically highly varied PubMed and PubMed Central documents. Specifically, false-positive predictions by these systems lead to generation of incorrect biomolecular events which are spotted by the end-users. This thesis proposes a novel post-processing approach, utilizing a combination of supervised and unsupervised learning techniques, that can automatically identify and filter out a considerable proportion of incorrect events from large-scale event databases, thus increasing the general credibility of those databases. The second part of this thesis is dedicated to a system we developed for hypothesis generation from large-scale event databases, which is able to discover novel biomolecular interactions among genes/gene-products. We cast the hypothesis generation problem as a supervised network topology prediction, i.e predicting new edges in the network, as well as types and directions for these edges, utilizing a set of features that can be extracted from large biomedical event networks. Routine machine learning evaluation results, as well as manual evaluation results suggest that the problem is indeed learnable. This work won the Best Paper Award in The 5th International Symposium on Languages in Biology and Medicine (LBM 2013).

Optimization of immunofluorescence protocols for detection of biomarkers in colorectal and breast cancer tissues

Cancer remains an undetermined question for modern medicine. Every year millions of people ranging from children to adult die since the modern treatment is unable to meet the challenge. Research must continue in the area of new biomarkers for tumors. Molecular biology has evolved during last years; however, this knowledge has not been applied into the medicine. Biological findings should be used to improve diagnostics and treatment modalities. In this thesis, human formalin-fixed paraffin embedded colorectal and breast cancer samples were used to optimize the double immunofluorescence staining protocol. Also, immunohistochemistry was performed in order to visualize expression patterns of each biomarker. Concerning double immunofluorescence, feasibility of primary antibodies raised in different and same host species was also tested. Finally, established methods for simultaneous multicolor immunofluorescence imaging of formalin-fixed paraffin embedded specimens were applied for the detection of pairs of potential biomarkers of colorectal cancer (EGFR, pmTOR, pAKT, Vimentin, Cytokeratin Pan, Ezrin, E-cadherin) and breast cancer (Securin, PTTG1IP, Cleaved caspase 3, ki67).