Automated extension of biomedical ontologies

Developing and extending a biomedical ontology is a very demanding process, particularly because biomedical knowledge is diverse, complex and continuously changing and growing. Existing automated and semi-automated techniques are not tailored to handling the issues in extending biomedical ontologies. This thesis advances the state of the art in semi-automated ontology extension by presenting a framework as well as methods and methodologies for automating ontology extension specifically designed to address the features of biomedical ontologies.The overall strategy is based on first predicting the areas of the ontology that are in need of extension and then applying ontology learning and ontology matching techniques to extend them. A novel machine learning approach for predicting these areas based on features of past ontology versions was developed and successfully applied to the Gene Ontology. Methods and techniques were also specifically designed for matching biomedical ontologies and retrieving relevant biomedical concepts from text, which were shown to be successful in several applications.O desenvolvimento e extensão de uma ontologia biomédica é um processo muito exigente, dada a diversidade, complexidade e crescimento contínuo do conhecimento biomédico. As técnicas existentes nesta área não estão preparadas para lidar com os desafios da extensão de uma ontologia biomédica. Esta tese avança o estado da arte na extensão semi-automática de ontologias, apresentando uma framework assim como métodos e metodologias para a automação da extensão de ontologias especificamente desenhados tendo em conta as características das ontologias biomédicas. A estratégia global é baseada em primeiro prever quais as áreas da ontologia que necessitam extensão, e depois usá-las como enfoque para técnicas de alinhamento e aprendizagem de ontologias, com o objectivo de as estender. Uma nova estratégia de aprendizagem automática para prever estas áreas baseada em atributos de antigas versões de ontologias foi desenvolvida e testada com sucesso na Gene Ontology. Foram também especificamente desenvolvidos métodos e técnicas para o alinhamento de ontologias biomédicas e extracção de conceitos relevantes de texto, cujo sucesso foi demonstrado em várias aplicações.Fundação para a Ciência e a Tecnologi

Optimizing text mining methods for improving biomedical natural language processing

The overwhelming amount and the increasing rate of publication in the biomedical domain make it difficult for life sciences researchers to acquire and maintain all information that is necessary for their research. Pubmed (the primary citation database for the biomedical literature) currently contains over 21 million article abstracts and more than one million of them were published in 2020 alone. Even though existing article databases provide capable keyword search services, typical everyday-life queries usually return thousands of relevant articles. For instance, a cancer research scientist may need to acquire a complete list of genes that interact with BRCA1 (breast cancer 1) gene. The PubMed keyword search for BRCA1 returns over 16,500 article abstracts, making manual inspection of the retrieved documents impractical. Missing even one of the interacting gene partners in this scenario may jeopardize successful development of a potential new drug or vaccine. Although manually curated databases of biomolecular interactions exist, they are usually not up-to-date and they require notable human effort to maintain. To summarize, new discoveries are constantly being shared within the community via scientific publishing, but unfortunately the probability of missing vital information for research in life sciences is increasing. In response to this problem, the biomedical natural language processing (BioNLP) community of researchers has emerged and strives to assist life sciences researchers by building modern language processing and text mining tools that can be applied at large-scale and scan the whole publicly available literature and extract, classify, and aggregate the information found within, thus keeping life sciences researchers always up-to-date with the recent relevant discoveries and facilitating their research in numerous fields such as molecular biology, biomedical engineering, bioinformatics, genetics engineering and biochemistry. My research has almost exclusively focused on biomedical relation and event extraction tasks. These foundational information extraction tasks deal with automatic detection of biological processes, interactions and relations described in the biomedical literature. Precisely speaking, biomedical relation and event extraction systems can scan through a vast amount of biomedical texts and automatically detect and extract the semantic relations of biomedical named entities (e.g. genes, proteins, chemical compounds, and diseases). The structured outputs of such systems (i.e., the extracted relations or events) can be stored as relational databases or molecular interaction networks which can easily be queried, filtered, analyzed, visualized and integrated with other structured data sources. Extracting biomolecular interactions has always been the primary interest of BioNLP researcher because having knowledge about such interactions is crucially important in various research areas including precision medicine, drug discovery, drug repurposing, hypothesis generation, construction and curation of signaling pathways, and protein function and structure prediction. State-of-the-art relation and event extraction methods are based on supervised machine learning, requiring manually annotated data for training. Manual annotation for the biomedical domain requires domain expertise and it is time-consuming. Hence, having minimal training data for building information extraction systems is a common case in the biomedical domain. This demands development of methods that can make the most out of available training data and this thesis gathers all my research efforts and contributions in that direction. It is worth mentioning that biomedical natural language processing has undergone a revolution since I started my research in this field almost ten years ago. As a member of the BioNLP community, I have witnessed the emergence, improvement– and in some cases, the disappearance–of many methods, each pushing the performance of the best previous method one step further. I can broadly divide the last ten years into three periods. Once I started my research, feature-based methods that relied on heavy feature engineering were dominant and popular. Then, significant advancements in the hardware technology, as well as several breakthroughs in the algorithms and methods enabled machine learning practitioners to seriously utilize artificial neural networks for real-world applications. In this period, convolutional, recurrent, and attention-based neural network models became dominant and superior. Finally, the introduction of transformer-based language representation models such as BERT and GPT impacted the field and resulted in unprecedented performance improvements on many data sets. When reading this thesis, I demand the reader to take into account the course of history and judge the methods and results based on what could have been done in that particular period of the history

A Multimodal and Multi-Algorithmic Architecture for Data Fusion in Biometric Systems

Software di autenticazione basato su tratti biometric

Knowledge Modelling and Learning through Cognitive Networks

One of the most promising developments in modelling knowledge is cognitive network science, which aims to investigate cognitive phenomena driven by the networked, associative organization of knowledge. For example, investigating the structure of semantic memory via semantic networks has illuminated how memory recall patterns influence phenomena such as creativity, memory search, learning, and more generally, knowledge acquisition, exploration, and exploitation. In parallel, neural network models for artificial intelligence (AI) are also becoming more widespread as inferential models for understanding which features drive language-related phenomena such as meaning reconstruction, stance detection, and emotional profiling. Whereas cognitive networks map explicitly which entities engage in associative relationships, neural networks perform an implicit mapping of correlations in cognitive data as weights, obtained after training over labelled data and whose interpretation is not immediately evident to the experimenter. This book aims to bring together quantitative, innovative research that focuses on modelling knowledge through cognitive and neural networks to gain insight into mechanisms driving cognitive processes related to knowledge structuring, exploration, and learning. The book comprises a variety of publication types, including reviews and theoretical papers, empirical research, computational modelling, and big data analysis. All papers here share a commonality: they demonstrate how the application of network science and AI can extend and broaden cognitive science in ways that traditional approaches cannot

Information Extraction from Text for Improving Research on Small Molecules and Histone Modifications

The cumulative number of publications, in particular in the life sciences, requires efficient methods for the automated extraction of information and semantic information retrieval. The recognition and identification of information-carrying units in text – concept denominations and named entities – relevant to a certain domain is a fundamental step. The focus of this thesis lies on the recognition of chemical entities and the new biological named entity type histone modifications, which are both important in the field of drug discovery. As the emergence of new research fields as well as the discovery and generation of novel entities goes along with the coinage of new terms, the perpetual adaptation of respective named entity recognition approaches to new domains is an important step for information extraction. Two methodologies have been investigated in this concern: the state-of-the-art machine learning method, Conditional Random Fields (CRF), and an approximate string search method based on dictionaries. Recognition methods that rely on dictionaries are strongly dependent on the availability of entity terminology collections as well as on its quality. In the case of chemical entities the terminology is distributed over more than 7 publicly available data sources. The join of entries and accompanied terminology from selected resources enables the generation of a new dictionary comprising chemical named entities. Combined with the automatic processing of respective terminology – the dictionary curation – the recognition performance reached an F1 measure of 0.54. That is an improvement by 29 % in comparison to the raw dictionary. The highest recall was achieved for the class of TRIVIAL-names with 0.79. The recognition and identification of chemical named entities provides a prerequisite for the extraction of related pharmacological relevant information from literature data. Therefore, lexico-syntactic patterns were defined that support the automated extraction of hypernymic phrases comprising pharmacological function terminology related to chemical compounds. It was shown that 29-50 % of the automatically extracted terms can be proposed for novel functional annotation of chemical entities provided by the reference database DrugBank. Furthermore, they are a basis for building up concept hierarchies and ontologies or for extending existing ones. Successively, the pharmacological function and biological activity concepts obtained from text were included into a novel descriptor for chemical compounds. Its successful application for the prediction of pharmacological function of molecules and the extension of chemical classification schemes, such as the the Anatomical Therapeutic Chemical (ATC), is demonstrated. In contrast to chemical entities, no comprehensive terminology resource has been available for histone modifications. Thus, histone modification concept terminology was primary recognized in text via CRFs with a F1 measure of 0.86. Subsequent, linguistic variants of extracted histone modification terms were mapped to standard representations that were organized into a newly assembled histone modification hierarchy. The mapping was accomplished by a novel developed term mapping approach described in the thesis. The combination of term recognition and term variant resolution builds up a new procedure for the assembly of novel terminology collections. It supports the generation of a term list that is applicable in dictionary-based methods. For the recognition of histone modification in text it could be shown that the named entity recognition method based on dictionaries is superior to the used machine learning approach. In conclusion, the present thesis provides techniques which enable an enhanced utilization of textual data, hence, supporting research in epigenomics and drug discovery

On cross-domain social semantic learning

Approximately 2.4 billion people are now connected to the Internet, generating massive amounts of data through laptops, mobile phones, sensors and other electronic devices or gadgets. Not surprisingly then, ninety percent of the world's digital data was created in the last two years. This massive explosion of data provides tremendous opportunity to study, model and improve conceptual and physical systems from which the data is produced. It also permits scientists to test pre-existing hypotheses in various fields with large scale experimental evidence. Thus, developing computational algorithms that automatically explores this data is the holy grail of the current generation of computer scientists. Making sense of this data algorithmically can be a complex process, specifically due to two reasons. Firstly, the data is generated by different devices, capturing different aspects of information and resides in different web resources/ platforms on the Internet. Therefore, even if two pieces of data bear singular conceptual similarity, their generation, format and domain of existence on the web can make them seem considerably dissimilar. Secondly, since humans are social creatures, the data often possesses inherent but murky correlations, primarily caused by the causal nature of direct or indirect social interactions. This drastically alters what algorithms must now achieve, necessitating intelligent comprehension of the underlying social nature and semantic contexts within the disparate domain data and a quantifiable way of transferring knowledge gained from one domain to another. Finally, the data is often encountered as a stream and not as static pages on the Internet. Therefore, we must learn, and re-learn as the stream propagates. The main objective of this dissertation is to develop learning algorithms that can identify specific patterns in one domain of data which can consequently augment predictive performance in another domain. The research explores existence of specific data domains which can function in synergy with another and more importantly, proposes models to quantify the synergetic information transfer among such domains. We include large-scale data from various domains in our study: social media data from Twitter, multimedia video data from YouTube, video search query data from Bing Videos, Natural Language search queries from the web, Internet resources in form of web logs (blogs) and spatio-temporal social trends from Twitter. Our work presents a series of solutions to address the key challenges in cross-domain learning, particularly in the field of social and semantic data. We propose the concept of bridging media from disparate sources by building a common latent topic space, which represents one of the first attempts toward answering sociological problems using cross-domain (social) media. This allows information transfer between social and non-social domains, fostering real-time socially relevant applications. We also engineer a concept network from the semantic web, called semNet, that can assist in identifying concept relations and modeling information granularity for robust natural language search. Further, by studying spatio-temporal patterns in this data, we can discover categorical concepts that stimulate collective attention within user groups.Includes bibliographical references (pages 210-214)

Tracking the Temporal-Evolution of Supernova Bubbles in Numerical Simulations

The study of low-dimensional, noisy manifolds embedded in a higher dimensional space has been extremely useful in many applications, from the chemical analysis of multi-phase flows to simulations of galactic mergers. Building a probabilistic model of the manifolds has helped in describing their essential properties and how they vary in space. However, when the manifold is evolving through time, a joint spatio-temporal modelling is needed, in order to fully comprehend its nature. We propose a first-order Markovian process that propagates the spatial probabilistic model of a manifold at fixed time, to its adjacent temporal stages. The proposed methodology is demonstrated using a particle simulation of an interacting dwarf galaxy to describe the evolution of a cavity generated by a Supernov

Computational approaches to virtual screening in human central nervous system therapeutic targets

In the past several years of drug design, advanced high-throughput synthetic and analytical chemical technologies are continuously producing a large number of compounds. These large collections of chemical structures have resulted in many public and commercial molecular databases. Thus, the availability of larger data sets provided the opportunity for developing new knowledge mining or virtual screening (VS) methods. Therefore, this research work is motivated by the fact that one of the main interests in the modern drug discovery process is the development of new methods to predict compounds with large therapeutic profiles (multi-targeting activity), which is essential for the discovery of novel drug candidates against complex multifactorial diseases like central nervous system (CNS) disorders. This work aims to advance VS approaches by providing a deeper understanding of the relationship between chemical structure and pharmacological properties and design new fast and robust tools for drug designing against different targets/pathways. To accomplish the defined goals, the first challenge is dealing with big data set of diverse molecular structures to derive a correlation between structures and activity. However, an extendable and a customizable fully automated in-silico Quantitative-Structure Activity Relationship (QSAR) modeling framework was developed in the first phase of this work. QSAR models are computationally fast and powerful tool to screen huge databases of compounds to determine the biological properties of chemical molecules based on their chemical structure. The generated framework reliably implemented a full QSAR modeling pipeline from data preparation to model building and validation. The main distinctive features of the designed framework include a)efficient data curation b) prior estimation of data modelability and, c)an-optimized variable selection methodology that was able to identify the most biologically relevant features responsible for compound activity. Since the underlying principle in QSAR modeling is the assumption that the structures of molecules are mainly responsible for their pharmacological activity, the accuracy of different structural representation approaches to decode molecular structural information largely influence model predictability. However, to find the best approach in QSAR modeling, a comparative analysis of two main categories of molecular representations that included descriptor-based (vector space) and distance-based (metric space) methods was carried out. Results obtained from five QSAR data sets showed that distance-based method was superior to capture the more relevant structural elements for the accurate characterization of molecular properties in highly diverse data sets (remote chemical space regions). This finding further assisted to the development of a novel tool for molecular space visualization to increase the understanding of structure-activity relationships (SAR) in drug discovery projects by exploring the diversity of large heterogeneous chemical data. In the proposed visual approach, four nonlinear DR methods were tested to represent molecules lower dimensionality (2D projected space) on which a non-parametric 2D kernel density estimation (KDE) was applied to map the most likely activity regions (activity surfaces). The analysis of the produced probabilistic surface of molecular activities (PSMAs) from the four datasets showed that these maps have both descriptive and predictive power, thus can be used as a spatial classification model, a tool to perform VS using only structural similarity of molecules. The above QSAR modeling approach was complemented with molecular docking, an approach that predicts the best mode of drug-target interaction. Both approaches were integrated to develop a rational and re-usable polypharmacology-based VS pipeline with improved hits identification rate. For the validation of the developed pipeline, a dual-targeting drug designing model against Parkinson’s disease (PD) was derived to identify novel inhibitors for improving the motor functions of PD patients by enhancing the bioavailability of dopamine and avoiding neurotoxicity. The proposed approach can easily be extended to more complex multi-targeting disease models containing several targets and anti/offtargets to achieve increased efficacy and reduced toxicity in multifactorial diseases like CNS disorders and cancer. This thesis addresses several issues of cheminformatics methods (e.g., molecular structures representation, machine learning, and molecular similarity analysis) to improve and design new computational approaches used in chemical data mining. Moreover, an integrative drug-designing pipeline is designed to improve polypharmacology-based VS approach. This presented methodology can identify the most promising multi-targeting candidates for experimental validation of drug-targets network at the systems biology level in the drug discovery process