184 research outputs found

    LIPIcs, Volume 261, ICALP 2023, Complete Volume

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    LIPIcs, Volume 261, ICALP 2023, Complete Volum

    Undergraduate and Graduate Course Descriptions, 2023 Spring

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    Wright State University undergraduate and graduate course descriptions from Spring 2023

    Knowledge extraction from unstructured data

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    Data availability is becoming more essential, considering the current growth of web-based data. The data available on the web are represented as unstructured, semi-structured, or structured data. In order to make the web-based data available for several Natural Language Processing or Data Mining tasks, the data needs to be presented as machine-readable data in a structured format. Thus, techniques for addressing the problem of capturing knowledge from unstructured data sources are needed. Knowledge extraction methods are used by the research communities to address this problem; methods that are able to capture knowledge in a natural language text and map the extracted knowledge to existing knowledge presented in knowledge graphs (KGs). These knowledge extraction methods include Named-entity recognition, Named-entity Disambiguation, Relation Recognition, and Relation Linking. This thesis addresses the problem of extracting knowledge over unstructured data and discovering patterns in the extracted knowledge. We devise a rule-based approach for entity and relation recognition and linking. The defined approach effectively maps entities and relations within a text to their resources in a target KG. Additionally, it overcomes the challenges of recognizing and linking entities and relations to a specific KG by employing devised catalogs of linguistic and domain-specific rules that state the criteria to recognize entities in a sentence of a particular language, and a deductive database that encodes knowledge in community-maintained KGs. Moreover, we define a Neuro-symbolic approach for the tasks of knowledge extraction in encyclopedic and domain-specific domains; it combines symbolic and sub-symbolic components to overcome the challenges of entity recognition and linking and the limitation of the availability of training data while maintaining the accuracy of recognizing and linking entities. Additionally, we present a context-aware framework for unveiling semantically related posts in a corpus; it is a knowledge-driven framework that retrieves associated posts effectively. We cast the problem of unveiling semantically related posts in a corpus into the Vertex Coloring Problem. We evaluate the performance of our techniques on several benchmarks related to various domains for knowledge extraction tasks. Furthermore, we apply these methods in real-world scenarios from national and international projects. The outcomes show that our techniques are able to effectively extract knowledge encoded in unstructured data and discover patterns over the extracted knowledge presented as machine-readable data. More importantly, the evaluation results provide evidence to the effectiveness of combining the reasoning capacity of the symbolic frameworks with the power of pattern recognition and classification of sub-symbolic models

    ApHMM: Accelerating Profile Hidden Markov Models for Fast and Energy-Efficient Genome Analysis

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    Profile hidden Markov models (pHMMs) are widely employed in various bioinformatics applications to identify similarities between biological sequences, such as DNA or protein sequences. In pHMMs, sequences are represented as graph structures. These probabilities are subsequently used to compute the similarity score between a sequence and a pHMM graph. The Baum-Welch algorithm, a prevalent and highly accurate method, utilizes these probabilities to optimize and compute similarity scores. However, the Baum-Welch algorithm is computationally intensive, and existing solutions offer either software-only or hardware-only approaches with fixed pHMM designs. We identify an urgent need for a flexible, high-performance, and energy-efficient HW/SW co-design to address the major inefficiencies in the Baum-Welch algorithm for pHMMs. We introduce ApHMM, the first flexible acceleration framework designed to significantly reduce both computational and energy overheads associated with the Baum-Welch algorithm for pHMMs. ApHMM tackles the major inefficiencies in the Baum-Welch algorithm by 1) designing flexible hardware to accommodate various pHMM designs, 2) exploiting predictable data dependency patterns through on-chip memory with memoization techniques, 3) rapidly filtering out negligible computations using a hardware-based filter, and 4) minimizing redundant computations. ApHMM achieves substantial speedups of 15.55x - 260.03x, 1.83x - 5.34x, and 27.97x when compared to CPU, GPU, and FPGA implementations of the Baum-Welch algorithm, respectively. ApHMM outperforms state-of-the-art CPU implementations in three key bioinformatics applications: 1) error correction, 2) protein family search, and 3) multiple sequence alignment, by 1.29x - 59.94x, 1.03x - 1.75x, and 1.03x - 1.95x, respectively, while improving their energy efficiency by 64.24x - 115.46x, 1.75x, 1.96x.Comment: Accepted to ACM TAC

    Workflows for the Large-Scale Assessment of miRNA Evolution: Birth and Death of miRNA Genes in Tunicates

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    As described over 20 years ago with the discovery of RNA interference (RNAi), double-stranded RNAs occupied key roles in regulation and as defense-line in animal cells. This thesis focuses on metazoan microRNAs (miRNAs). These small non-coding RNAs are distinguished from their small-interfering RNA (siRNA) relatives by their tightly controlled, efficient and flexible biogenesis, together with a broader flexibility to target multiple mRNAs by a seed imperfect base-pairing. As potent regulators, miRNAs are involved in mRNA stability and post-transcriptional regulation tasks, being a conserved mechanism used repetitively by the evolution, not only in metazoans, but plants and unicellular organisms. Through a comprehensive revision of the current animal miRNA model, the canonical pathway dominates the extensive literature about miRNAs, and served as a scaffold to understand the scenes behind the regulatory landscape performed by the cell. The characterization of a diverse set of non-canonical pathways has expanded this view, suggesting a diverse, rich and flexible regulatory landscape to generate mature miRNAs. The production of miRNAs, derived from isolated or clustered transcripts, is an efficient and highly conserved mechanism traced back to animals with high fidelity at family level. In evolutionary terms, expansions of miRNA families have been associated with an increasing morphological and developmental complexity. In particular, the Chordata clade (the ancient cephalochordates, highly derived and secondary simplified tunicates, and the well-known vertebrates) represents an interesting scenario to study miRNA evolution. Despite clearly conserved miRNAs along these clades, tunicates display massive restructuring events, including emergence of highly derived miRNAs. As shown in this thesis, model organisms or vertebrate-specific bias exist in current animal miRNA annotations, misrepresenting more diverse groups, such as marine invertebrates. Current miRNA databases, such as miRBase and Rfam, classified miRNAs under different definitions and possessed annotations that are not simple to be linked. As an alternative, this thesis proposes a method to curate and merge those annotations, making use of miRBase precursor/mature annotations and genomes together with Rfam predicted sequences. This approach generated structural models for shared miRNA families, based on the alignment of their correct-positioned mature sequences as anchors. In this process, the developed structural curation steps flagged 33 miRNA families from the Rfam as questionable. Curated Rfam and miRBase anchored-structural alignments provided a rich resource for constructing predictive miRNA profiles, using correspondent hidden Markov (HMMs) and covariance models (CMs). As a direct application, the use of those models is time-consuming, and the user has to deal with multiple iterations to achieve a genome-wide non-overlapping annotation. To resolve this, the proposed miRNAture pipeline provides an automatic and flexible solution to annotate miRNAs. It combines multiple homology approaches to generate the best candidates validated at sequence and structural levels. This increases the achievable sensitivity to annotate canonical miRNAs, and the evaluation against human annotation shows that clear false positive calls are rare and additional counterparts lie in retained-introns, transcribed lncRNAs or repeat families. Further development of miRNAture suggests an inclusion of multiple rules to distinguish non-canonical miRNA families. This thesis describes multiple homology approaches to annotate the genomic information from a non-model chordate: the colonial tunicate Didemnum vexillum. Detected high levels of genetic variance and unexpected levels of DNA degradation were evidenced through a comprehensive analysis of genome-assembly methods and gene annotation. Despite those challenges, it was possible to find candidate homeobox and skeletogenesis- related genes. On its own, the ncRNA annotation included expected conserved families, and an extensive search of the Rhabdomyosarcoma 2-associated transcript (RMST) lncRNA family traced-back at the divergence of deuterostomes. In addition, a complete study of the annotation thresholds suggested variations to detect miRNAs, later implemented on the miRNAture tool. This chapter is a showcase of the usual workflow that should follow comprehensive sequencing, assembly and annotation project, in the light of the increasing research approaching DNA sequencing. In the last 10 years, the remarkable increment in tunicate sequencing projects boosted the access to an expanded miRNA annotation landscape. In this way, a comprehensive homology approach annotated the miRNA complement of 28 deuterostome genomes (including current 16 reported tunicates) using miRNAture. To get proper structural models as input, corrected miRBase structural alignments served as a scaffold for building correspondent CMs, based on a developed genetic algorithm. By this means, this automatic approach selected the set of sequences that composed the alignments, generating 2492 miRNA CMs. Despite the multiple sources and associated heterogeneity of the studied genomes, a clustering approach successfully gathered five groups of similar assemblies and highlighted low quality assemblies. The overall family and loci reduction on tunicates is notorious, showing on average 374 microRNA (miRNA) loci, in comparison to other clades: Cephalochordata (2119), Vertebrata (3638), Hemichordata (1092) and Echinodermata (2737). Detection of 533 miRNA families on the divergence of tunicates shows an expanded landscape regarding currently miRNA annotated families. Shared sets of ancestral, chordates, Olfactores, and specific clade-specific miRNAs were uncovered using a phyloge- netic conservation criteria. Compared to current annotations, the family repertories were expanded in all cases. Finally, relying on the adjacent elements from annotated miRNAs, this thesis proposes an additional syntenic support to cluster miRNA loci. In this way, the structural alignment of miR-1497, originally annotated in three model tunicates, was expanded with a clear syntenic support on tunicates

    Intelligent Biosignal Analysis Methods

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    This book describes recent efforts in improving intelligent systems for automatic biosignal analysis. It focuses on machine learning and deep learning methods used for classification of different organism states and disorders based on biomedical signals such as EEG, ECG, HRV, and others

    Improving the hierarchical classification of protein functions With swarm intelligence

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    This thesis investigates methods to improve the performance of hierarchical classification. In terms of this thesis hierarchical classification is a form of supervised learning, where the classes in a data set are arranged in a tree structure. As a base for our new methods we use the TDDC (top-down divide-and-conquer) approach for hierarchical classification, where each classifier is built only to discriminate between sibling classes. Firstly, we propose a swarm intelligence technique which varies the types of classifiers used at each divide within the TDDC tree. Our technique, PSO/ACO-CS (Particle Swarm Optimisation/Ant Colony Optimisation Classifier Selection), finds combinations of classifiers to be used in the TDDC tree using the global search ability of PSO/ACO. Secondly, we propose a technique that attempts to mitigate a major drawback of the TDDC approach. The drawback is that if at any point in the TDDC tree an example is misclassified it can never be correctly classified further down the TDDC tree. Our approach, PSO/ACO-RO (PSO/ACO-Recovery Optimisation) decides whether to redirect examples at a given classifier node using, again, the global search ability of PSO/ACO. Thirdly, we propose an ensemble based technique, HEHRS (Hierarchical Ensembles of Hierarchical Rule Sets), which attempts to boost the accuracy at each classifier node in the TDDC tree by using information from classifiers (rule sets) in the rest of that tree. We use Particle Swarm Optimisation to weight the individual rules within each ensemble. We evaluate these three new methods in hierarchical bioinformatics datasets that we have created for this research. These data sets represent the real world problem of protein function prediction. We find through extensive experimentation that the three proposed methods improve upon the baseline TDDC method to varying degrees. Overall the HEHRS and PSO/ACO- CS-RO approaches are most effective, although they are associated with a higher computational cost

    Optimizing text mining methods for improving biomedical natural language processing

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    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

    Undergraduate and Graduate Course Descriptions, 2022 Summer

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    Wright State University undergraduate and graduate course descriptions from Summer 2022
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