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).

Russian Enterprise Restructuring - the Effect of Privatisation and Market Liberalisation on the Performance of Large Enterprises

The fundamental question in the transitional economies of the former Eastern Europe and Soviet Union has been whether privatisation and market liberalisation have had an effect on the performance of former state-owned enterprises. This study examines the effect of privatisation, capital market discipline, price liberalisation and international price exposure on the restructuring of large Russian enterprises. The performance indicators are sales, profitability, labour productivity and stock market valuations. The results do not show performance differences between state-owned and privatised enterprises. On the other hand, the expansion of the de novo private sector has been strong. New enterprises have significantly higher sales growth, profitability and labour productivity. However, the results indicate a diminishing effect of ownership. The international stock market listing has a significant positive effect on profitability, while the effect of domestic stock market listing is insignificant. The international price exposure has a significant positive increasing effect on profitability and labour productivity. International enterprises have higher profitability only when operating on price liberalised markets, however. The main results of the study are strong evidence on the positive effects of international linkages on the enterprise restructuring and the higher than expected role of new enterprises in the Russian economy.