A resource-saving collective approach to biomedical semantic role labeling

BACKGROUND: Biomedical semantic role labeling (BioSRL) is a natural language processing technique that identifies the semantic roles of the words or phrases in sentences describing biological processes and expresses them as predicate-argument structures (PAS’s). Currently, a major problem of BioSRL is that most systems label every node in a full parse tree independently; however, some nodes always exhibit dependency. In general SRL, collective approaches based on the Markov logic network (MLN) model have been successful in dealing with this problem. However, in BioSRL such an approach has not been attempted because it would require more training data to recognize the more specialized and diverse terms found in biomedical literature, increasing training time and computational complexity. RESULTS: We first constructed a collective BioSRL system based on MLN. This system, called collective BIOSMILE (CBIOSMILE), is trained on the BioProp corpus. To reduce the resources used in BioSRL training, we employ a tree-pruning filter to remove unlikely nodes from the parse tree and four argument candidate identifiers to retain candidate nodes in the tree. Nodes not recognized by any candidate identifier are discarded. The pruned annotated parse trees are used to train a resource-saving MLN-based system, which is referred to as resource-saving collective BIOSMILE (RCBIOSMILE). Our experimental results show that our proposed CBIOSMILE system outperforms BIOSMILE, which is the top BioSRL system. Furthermore, our proposed RCBIOSMILE maintains the same level of accuracy as CBIOSMILE using 92% less memory and 57% less training time. CONCLUSIONS: This greatly improved efficiency makes RCBIOSMILE potentially suitable for training on much larger BioSRL corpora over more biomedical domains. Compared to real-world biomedical corpora, BioProp is relatively small, containing only 445 MEDLINE abstracts and 30 event triggers. It is not large enough for practical applications, such as pathway construction. We consider it of primary importance to pursue SRL training on large corpora in the future

Joint Learning-based Causal Relation Extraction from Biomedical Literature

Causal relation extraction of biomedical entities is one of the most complex tasks in biomedical text mining, which involves two kinds of information: entity relations and entity functions. One feasible approach is to take relation extraction and function detection as two independent sub-tasks. However, this separate learning method ignores the intrinsic correlation between them and leads to unsatisfactory performance. In this paper, we propose a joint learning model, which combines entity relation extraction and entity function detection to exploit their commonality and capture their inter-relationship, so as to improve the performance of biomedical causal relation extraction. Meanwhile, during the model training stage, different function types in the loss function are assigned different weights. Specifically, the penalty coefficient for negative function instances increases to effectively improve the precision of function detection. Experimental results on the BioCreative-V Track 4 corpus show that our joint learning model outperforms the separate models in BEL statement extraction, achieving the F1 scores of 58.4% and 37.3% on the test set in Stage 2 and Stage 1 evaluations, respectively. This demonstrates that our joint learning system reaches the state-of-the-art performance in Stage 2 compared with other systems.Comment: 15 pages, 3 figure

Entity Linking for the Biomedical Domain

Entity linking is the process of detecting mentions of different concepts in text documents and linking them to canonical entities in a target lexicon. However, one of the biggest issues in entity linking is the ambiguity in entity names. The ambiguity is an issue that many text mining tools have yet to address since different names can represent the same thing and every mention could indicate a different thing. For instance, search engines that rely on heuristic string matches frequently return irrelevant results, because they are unable to satisfactorily resolve ambiguity. Thus, resolving named entity ambiguity is a crucial step in entity linking. To solve the problem of ambiguity, this work proposes a heuristic method for entity recognition and entity linking over the biomedical knowledge graph concerning the semantic similarity of entities in the knowledge graph. Named entity recognition (NER), relation extraction (RE), and relationship linking make up a conventional entity linking (EL) system pipeline (RL). We have used the accuracy metric in this thesis. Therefore, for each identified relation or entity, the solution comprises identifying the correct one and matching it to its corresponding unique CUI in the knowledge base. Because KBs contain a substantial number of relations and entities, each with only one natural language label, the second phase is directly dependent on the accuracy of the first. The framework developed in this thesis enables the extraction of relations and entities from the text and their mapping to the associated CUI in the UMLS knowledge base. This approach derives a new representation of the knowledge base that lends it to the easy comparison. Our idea to select the best candidates is to build a graph of relations and determine the shortest path distance using a ranking approach. We test our suggested approach on two well-known benchmarks in the biomedical field and show that our method exceeds the search engine's top result and provides us with around 4% more accuracy. In general, when it comes to fine-tuning, we notice that entity linking contains subjective characteristics and modifications may be required depending on the task at hand. The performance of the framework is evaluated based on a Python implementation

Superpixel labeling for medical image segmentation

openNowadays, most methods for image segmentation consider images in a pixel- wise manner, which is a huge job and also time-consuming. On the other hand, superpixel labeling can make the segmentation task easier in some aspects. First, superpixels carry more information than pixels because they usually follow the edges present in the image. Furthermore, superpixels have perceptual meaning, and finally, they can be very useful in computationally demanding problems, since by mapping pixels to superpixels we are reducing the complexity of the problem. In this thesis, we propose to do superpixel-wise labeling on two med- ical image datasets including ISIC Lesion Skin and Chest X-ray, then we feed them to the U-Net Convolutional Neural Network (CNN) DoubleU-Net and Dual-Aggregation Transformer (DuAT) network to segment our images in term of superpixels. Three different methods of labeling are used in this thesis: Su- perpixel labeling, Extended Superpixel Labeling (Distance-base Labeling), and Random Walk Superpixel labeling. The Superpixel labeled ground truths are used just for training. For the evaluation, we consider the original image and also the original binary ground truth. We considered four different superpixel algorithms, namely Simple Linear Iterative Clustering (SLIC), Felsenszwalb Hut- tenlocher (FH), QuickShift (QS) , and Superpixels Extracted via Energy-Driven Sampling (SEEDS). We evaluate the segmentation result with metrics such as Dice Coefficient, Precision, Intersection Over Union (IOU), and Sensitivity. Our results show the accuracy of 0.89 and 0.95 percent in dice coefficient for skin lesion and chest X-ray datasets respectively. Key Words: Superpixels, Medical Images, U-Net, DoubleU-Net, Image seg- mentation, CNN, DuAT, SEEDS.Nowadays, most methods for image segmentation consider images in a pixel- wise manner, which is a huge job and also time-consuming. On the other hand, superpixel labeling can make the segmentation task easier in some aspects. First, superpixels carry more information than pixels because they usually follow the edges present in the image. Furthermore, superpixels have perceptual meaning, and finally, they can be very useful in computationally demanding problems, since by mapping pixels to superpixels we are reducing the complexity of the problem. In this thesis, we propose to do superpixel-wise labeling on two med- ical image datasets including ISIC Lesion Skin and Chest X-ray, then we feed them to the U-Net Convolutional Neural Network (CNN) DoubleU-Net and Dual-Aggregation Transformer (DuAT) network to segment our images in term of superpixels. Three different methods of labeling are used in this thesis: Su- perpixel labeling, Extended Superpixel Labeling (Distance-base Labeling), and Random Walk Superpixel labeling. The Superpixel labeled ground truths are used just for training. For the evaluation, we consider the original image and also the original binary ground truth. We considered four different superpixel algorithms, namely Simple Linear Iterative Clustering (SLIC), Felsenszwalb Hut- tenlocher (FH), QuickShift (QS) , and Superpixels Extracted via Energy-Driven Sampling (SEEDS). We evaluate the segmentation result with metrics such as Dice Coefficient, Precision, Intersection Over Union (IOU), and Sensitivity. Our results show the accuracy of 0.89 and 0.95 percent in dice coefficient for skin lesion and chest X-ray datasets respectively. Key Words: Superpixels, Medical Images, U-Net, DoubleU-Net, Image seg- mentation, CNN, DuAT, SEEDS

Scientific Knowledge fit for society - Scoring scientific accuracy in climate change related news articles

The quantity of information is increasing exponentially, and there is a vast amount of content viewed on the internet that lacks an indicator as to whether it is scientifically accurate and correct or scientifically inaccurate and incorrect. This thesis proposes the development of an indicator of scientific accuracy in online media. This should help in public debates and help in the detection of misinformation. The thesis presents a baseline score and clear interfaces for further improvement. The necessity for such a score has been validated by a user survey, and the employed methodologies were evaluated and updated through interviews with experts from the ORKG team. Furthermore, an overview of the knowledge required to conduct research in this field and a discussion for future work is provided.Die Menge an Informationen nimmt exponentiell zu, und es gibt eine riesige Menge an Inhalten im Internet, für die es keinen Indikator dafür gibt, ob sie wissenschaftlich genau und korrekt oder wissenschaftlich ungenau und falsch sind. In dieser Arbeit wird die Entwicklung eines Indikators für wissenschaftliche Genauigkeit in Online- Medien vorgeschlagen. Dies sollte in öffentlichen Debatten und bei der Aufdeckung von Fehlinformationen helfen. Die Arbeit legt einen Grundstein und schafft klare Schnittstellen für weitere Verbesserungen. Die Notwendigkeit eines solchen Scores wurde durch eine Nutzerbefragung validiert, und die verwendeten Methoden wurden durch Interviews mit Experten aus dem ORKG-Team evaluiert und aktualisiert. Darüber hinaus wird ein Überblick über das für die Forschung in diesem Bereich erforderliche Wissen gegeben und eine Diskussion über künftige Arbeiten geführt

Information retrieval and text mining technologies for chemistry

Efficient access to chemical information contained in scientific literature, patents, technical reports, or the web is a pressing need shared by researchers and patent attorneys from different chemical disciplines. Retrieval of important chemical information in most cases starts with finding relevant documents for a particular chemical compound or family. Targeted retrieval of chemical documents is closely connected to the automatic recognition of chemical entities in the text, which commonly involves the extraction of the entire list of chemicals mentioned in a document, including any associated information. In this Review, we provide a comprehensive and in-depth description of fundamental concepts, technical implementations, and current technologies for meeting these information demands. A strong focus is placed on community challenges addressing systems performance, more particularly CHEMDNER and CHEMDNER patents tasks of BioCreative IV and V, respectively. Considering the growing interest in the construction of automatically annotated chemical knowledge bases that integrate chemical information and biological data, cheminformatics approaches for mapping the extracted chemical names into chemical structures and their subsequent annotation together with text mining applications for linking chemistry with biological information are also presented. Finally, future trends and current challenges are highlighted as a roadmap proposal for research in this emerging field.A.V. and M.K. acknowledge funding from the European Community’s Horizon 2020 Program (project reference: 654021 - OpenMinted). M.K. additionally acknowledges the Encomienda MINETAD-CNIO as part of the Plan for the Advancement of Language Technology. O.R. and J.O. thank the Foundation for Applied Medical Research (FIMA), University of Navarra (Pamplona, Spain). This work was partially funded by Consellería de Cultura, Educación e Ordenación Universitaria (Xunta de Galicia), and FEDER (European Union), and the Portuguese Foundation for Science and Technology (FCT) under the scope of the strategic funding of UID/BIO/04469/2013 unit and COMPETE 2020 (POCI-01-0145-FEDER-006684). We thank Iñigo Garciá -Yoldi for useful feedback and discussions during the preparation of the manuscript.info:eu-repo/semantics/publishedVersio

Medicine - Religion - Spirituality: Global Perspectives on Traditional, Complementary, and Alternative Healing

In modern societies the functional differentiation of medicine and religion is the predominant paradigm. Contemporary therapeutic practices and concepts in healing systems, such as Transpersonal Psychology, Ayurveda, as well as Buddhist and Anthroposophic medicine, however, are shaped by medical as well as religious or spiritual elements. This book investigates configurations of the entanglement between medicine, religion, and spirituality in Europe, Asia, North America, and Africa. How do political and legal conditions affect these healing systems? How do they relate to religious and scientific discourses? How do therapeutic practitioners position themselves between medicine and religion, and what is their appeal for patients

Medicine – Religion – Spirituality

In modern societies the functional differentiation of medicine and religion is the predominant paradigm. Contemporary therapeutic practices and concepts in healing systems, such as Transpersonal Psychology, Ayurveda, as well as Buddhist and Anthroposophic medicine, however, are shaped by medical as well as religious or spiritual elements. This book investigates configurations of the entanglement between medicine, religion, and spirituality in Europe, Asia, North America, and Africa. How do political and legal conditions affect these healing systems? How do they relate to religious and scientific discourses? How do therapeutic practitioners position themselves between medicine and religion, and what is their appeal for patients