The last decade has been marked by unprecedented growth in both the production of biomedical data and the amount of published literature discussing it. Almost every known or postulated piece of information pertaining to genes, proteins, and their role in biological processes is reported somewhere in the vast amount of published biomedical literature. We believe the ability to rapidly survey and analyze this literature and extract pertinent information constitutes a necessary step toward both the design and the interpretation of any large-scale experiment. Moreover, automated literature mining offers a yet untapped opportunity to integrate many fragments of information gathered by researchers from multiple fields of expertise into a complete picture exposing the interrelated roles of various genes, proteins, and chemical reactions in cells and organisms. In this thesis, we show that functional keywords in biomedical literature, particularly Medline, represent very valuable information and can be used to discover new genomic knowledge. To validate our claim we present an investigation into text mining biomedical literature to assist microarray data analysis, yeast gene function classification, and biomedical literature categorization. We conduct following studies: 1. We test sets of genes to discover common functional keywords among them and use these keywords to cluster them into groups; 2. We show that it is possible to link genes to diseases by an expert human interpretation of the functional keywords for the genes- none of these diseases are as yet mentioned in public databases; 3. By clustering genes based on commonality of functional keywords it is possible to group genes into meaningful clusters that reveal more information about their functions, link to diseases and roles in metabolism pathways; 4. Using extracted functional keywords, we are able to demonstrate that for yeast genes, we can make a better functional grouping of genes in comparison to available public microarray and phylogenetic databases; 5. We show an application of our approach to literature classification. Using functional keywords as features, we are able to extract epidemiological abstracts automatically from Medline with higher sensitivity and accuracy than a human expert.

Narrative information in Electronic Health Records (EHRs) contains a wealth of clinical information about treatments, diagnosis, medication and family history. In addition, the scientific literature represents a rich source of information that summarises the latest results and new research findings relevant to different diseases. These two textual sources often contain different types of valuable phenotypic information that may be complementary to each other. Combining details from each source thus has the potential to be useful in uncovering new disease-phenotypic associations. In turn, these associations can help to identify patients with high risk factors, and they can be useful in developing solutions to control the causes responsible for the development of different diseases. However, clinicians at the point of care have limited time to review the large volume of potentially useful information that is locked away in unstructured text format. This in turn limits the utility of this “raw” information to clinical practitioners and computerised applications. Accordingly, the provision of automated and efficient means to extract, combine and present phenotype information that may be scattered amongst a large number of different textual sources in an easily digestible format is a prerequisite to the effective use and comprehensive understanding of details contained within both the records and the literature. The development of such facilities can in turn help in deriving information about disease correlations and supporting clinical decisions. This thesis is the first comprehensive study focussing on extracting and integrating phenotypic information from two different biomedical sources using Text Mining (TM) techniques. In this research, we describe our work on (1) extracting phenotypic information from both EHRs and the biomedical literature; (2) extracting the relations between phenotypic information and distilling them from EHRs using an event-based approach; and (3) using normalisation methods to link the phenotypic information found in EHRs with associated mentions found in the literature as a first step towards the automatic integration of information from these heterogeneous sources.

With the advent of online publishing of scientific research came an avalanche of electronic resources and repositories containing knowledge encoded in some form or another. In the domain of biomedical sciences, research is now being published at a faster-than-ever pace, with several thousand articles per day. It is impossible for any human being to process that amount of information in due time, let alone apply it to their own needs. Thus appeared the necessity of being able to automatically retrieve relevant documents and extract useful information from text. Although it is now possible to distil essential factual knowledge from text, it is difficult to interpret the connections between the extracted facts. These connections, also known as discourse relations, make the text coherent and cohesive, and their automatic discovery can lead to a better understanding of the conveyed knowledge. One fundamental discourse relation is causality, as it is the one which explains reasons and allows for inferences to be made. This thesis is the first comprehensive study which focusses on recognising discourse causality in biomedical scientific literature. We first construct a manually annotated corpus of discourse causality and analyse its characteristics. Then, a methodology for automatically recognising causal relations using text mining and natural language processing techniques is presented. Furthermore, we investigate the automatic identification of additional information about the polarity, certainty, knowledge type and source of causal relations. The entire methodology is evaluated by empirical experiments, whose results show that it is possible to successfully extract causal relations from biomedical literature. Finally, we provide an example of a direct application of our research and offer ideas for further research directions and possible improvements to our methodology.

Indiana University-Purdue University Indianapolis (IUPUI) / Phenotyping definitions are essential in cohort identification when conducting clinical research, but they become an obstacle when they are not readily available. Developing new definitions manually requires expert involvement that is labor-intensive, time-consuming, and unscalable. Moreover, automated approaches rely mostly on electronic health records’ data that suffer from bias, confounding, and incompleteness. Limited efforts established in utilizing text-mining and data-driven approaches to automate extraction and literature-based knowledge discovery of phenotyping definitions and to support their scalability. In this dissertation, we proposed a text-mining pipeline combining rule-based and machine-learning methods to automate retrieval, classification, and extraction of phenotyping definitions’ information from literature. To achieve this, we first developed an annotation guideline with ten dimensions to annotate sentences with evidence of phenotyping definitions' modalities, such as phenotypes and laboratories. Two annotators manually annotated a corpus of sentences (n=3,971) extracted from full-text observational studies’ methods sections (n=86). Percent and Kappa statistics showed high inter-annotator agreement on sentence-level annotations. Second, we constructed two validated text classifiers using our annotated corpora: abstract-level and full-text sentence-level. We applied the abstract-level classifier on a large-scale biomedical literature of over 20 million abstracts published between 1975 and 2018 to classify positive abstracts (n=459,406). After retrieving their full-texts (n=120,868), we extracted sentences from their methods sections and used the full-text sentence-level classifier to extract positive sentences (n=2,745,416). Third, we performed a literature-based discovery utilizing the positively classified sentences. Lexica-based methods were used to recognize medical concepts in these sentences (n=19,423). Co-occurrence and association methods were used to identify and rank phenotype candidates that are associated with a phenotype of interest. We derived 12,616,465 associations from our large-scale corpus. Our literature-based associations and large-scale corpus contribute in building new data-driven phenotyping definitions and expanding existing definitions with minimal expert involvement.

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Ces dernières années, la découverte de fraudes scientifiques majeures a créé des ondes de choc dans la communauté scientifique. Le nombre annuel de rétractations a considérablement augmenté, et la plupart sont dues à des cas de fraude. Bien qu’il soit généralement pris pour acquis que tous les coauteurs sont affectés par ces rétractations, l’objectif de cette étude est de vérifier cette présupposition empiriquement. Nous avons recensé toutes les rétractations du domaine biomédical (443) de 1996 à 2006 dans PubMed et mesuré, à l’aide du Web of Science (WOS), la productivité, l’impact et les pratiques de collaboration des coauteurs (1 818) sur une période de cinq ans avant et après la rétractation. Nos résultats montrent que les rétractations ont des conséquences sur la carrière des coauteurs, surtout au niveau du nombre de publications des années subséquentes. Cet impact est plus grand dans les cas de fraude, et pour les premiers auteurs. / Over the last few years, major cases of scientific fraud shocked the scientific community, and the number of retractions each year increased considerably. Scientific misconduct accounts for approximately more than half of those retractions. It is assumed that co-authors of retracted papers are affected by their colleagues’ misconduct, and the aim of this study is to provide empirical evidence of how researchers’ careers are affected by a retraction. We retrieved all (443) publications retracted from 1996 to 2006 from PubMed, signed by 1818 authors. Using the Web of Science (WOS), we measured the productivity, impact and collaboration of each of those authors for a period of five years before and after the retraction. Our results show that retractions affect the career of co-authors, mostly in terms of scientific output. This impact is felt more strongly in cases of fraud and for first authors.

Ces dernières années, la découverte de fraudes scientifiques majeures a créé des ondes de choc dans la communauté scientifique. Le nombre annuel de rétractations a considérablement augmenté, et la plupart sont dues à des cas de fraude. Bien qu’il soit généralement pris pour acquis que tous les coauteurs sont affectés par ces rétractations, l’objectif de cette étude est de vérifier cette présupposition empiriquement. Nous avons recensé toutes les rétractations du domaine biomédical (443) de 1996 à 2006 dans PubMed et mesuré, à l’aide du Web of Science (WOS), la productivité, l’impact et les pratiques de collaboration des coauteurs (1 818) sur une période de cinq ans avant et après la rétractation. Nos résultats montrent que les rétractations ont des conséquences sur la carrière des coauteurs, surtout au niveau du nombre de publications des années subséquentes. Cet impact est plus grand dans les cas de fraude, et pour les premiers auteurs. / Over the last few years, major cases of scientific fraud shocked the scientific community, and the number of retractions each year increased considerably. Scientific misconduct accounts for approximately more than half of those retractions. It is assumed that co-authors of retracted papers are affected by their colleagues’ misconduct, and the aim of this study is to provide empirical evidence of how researchers’ careers are affected by a retraction. We retrieved all (443) publications retracted from 1996 to 2006 from PubMed, signed by 1818 authors. Using the Web of Science (WOS), we measured the productivity, impact and collaboration of each of those authors for a period of five years before and after the retraction. Our results show that retractions affect the career of co-authors, mostly in terms of scientific output. This impact is felt more strongly in cases of fraud and for first authors.