Infectious Disease Ontology

Technological developments have resulted in tremendous increases in the volume and diversity of the data and information that must be processed in the course of biomedical and clinical research and practice. Researchers are at the same time under ever greater pressure to share data and to take steps to ensure that data resources are interoperable. The use of ontologies to annotate data has proven successful in supporting these goals and in providing new possibilities for the automated processing of data and information. In this chapter, we describe different types of vocabulary resources and emphasize those features of formal ontologies that make them most useful for computational applications. We describe current uses of ontologies and discuss future goals for ontology-based computing, focusing on its use in the field of infectious diseases. We review the largest and most widely used vocabulary resources relevant to the study of infectious diseases and conclude with a description of the Infectious Disease Ontology (IDO) suite of interoperable ontology modules that together cover the entire infectious disease domain

Post-Processing of Discovered Association Rules Using Ontologies

In Data Mining, the usefulness of association rules is strongly limited by the huge amount of delivered rules. In this paper we propose a new approach to prune and filter discovered rules. Using Domain Ontologies, we strengthen the integration of user knowledge in the post-processing task. Furthermore, an interactive and iterative framework is designed to assist the user along the analyzing task. On the one hand, we represent user domain knowledge using a Domain Ontology over database. On the other hand, a novel technique is suggested to prune and to filter discovered rules. The proposed framework was applied successfully over the client database provided by Nantes Habitat

Chemical information matters: an e-Research perspective on information and data sharing in the chemical sciences

Recently, a number of organisations have called for open access to scientific information and especially to the data obtained from publicly funded research, among which the Royal Society report and the European Commission press release are particularly notable. It has long been accepted that building research on the foundations laid by other scientists is both effective and efficient. Regrettably, some disciplines, chemistry being one, have been slow to recognise the value of sharing and have thus been reluctant to curate their data and information in preparation for exchanging it. The very significant increases in both the volume and the complexity of the datasets produced has encouraged the expansion of e-Research, and stimulated the development of methodologies for managing, organising, and analysing "big data". We review the evolution of cheminformatics, the amalgam of chemistry, computer science, and information technology, and assess the wider e-Science and e-Research perspective. Chemical information does matter, as do matters of communicating data and collaborating with data. For chemistry, unique identifiers, structure representations, and property descriptors are essential to the activities of sharing and exchange. Open science entails the sharing of more than mere facts: for example, the publication of negative outcomes can facilitate better understanding of which synthetic routes to choose, an aspiration of the Dial-a-Molecule Grand Challenge. The protagonists of open notebook science go even further and exchange their thoughts and plans. We consider the concepts of preservation, curation, provenance, discovery, and access in the context of the research lifecycle, and then focus on the role of metadata, particularly the ontologies on which the emerging chemical Semantic Web will depend. Among our conclusions, we present our choice of the "grand challenges" for the preservation and sharing of chemical information

Prior knowledge based mining functional modules from Yeast PPI networks with gene ontology

<p>Abstract</p> <p>Background</p> <p>In the literature, there are fruitful algorithmic approaches for identification functional modules in protein-protein interactions (PPI) networks. Because of accumulation of large-scale interaction data on multiple organisms and non-recording interaction data in the existing PPI database, it is still emergent to design novel computational techniques that can be able to correctly and scalably analyze interaction data sets. Indeed there are a number of large scale biological data sets providing indirect evidence for protein-protein interaction relationships.</p> <p>Results</p> <p>The main aim of this paper is to present a prior knowledge based mining strategy to identify functional modules from PPI networks with the aid of Gene Ontology. Higher similarity value in Gene Ontology means that two gene products are more functionally related to each other, so it is better to group such gene products into one functional module. We study (i) to encode the functional pairs into the existing PPI networks; and (ii) to use these functional pairs as pairwise constraints to supervise the existing functional module identification algorithms. Topology-based modularity metric and complex annotation in MIPs will be used to evaluate the identified functional modules by these two approaches.</p> <p>Conclusions</p> <p>The experimental results on Yeast PPI networks and GO have shown that the prior knowledge based learning methods perform better than the existing algorithms.</p

Novel Natural Language Processing Models for Medical Terms and Symptoms Detection in Twitter

This dissertation focuses on disambiguation of language use on Twitter about drug use, consumption types of drugs, drug legalization, ontology-enhanced approaches, and prediction analysis of data-driven by developing novel NLP models. Three technical aims comprise this work: (a) leveraging pattern recognition techniques to improve the quality and quantity of crawled Twitter posts related to drug abuse; (b) using an expert-curated, domain-specific DsOn ontology model that improve knowledge extraction in the form of drug-to-symptom and drug-to-side effect relations; and (c) modeling the prediction of public perception of the drug’s legalization and the sentiment analysis of drug consumption on Twitter. We collected 7.5 million data from August 2015 to March 2016. This work leveraged a longstanding, multidisciplinary collaboration between researchers at the Population & Center for Interventions, Treatment, and Addictions Research (CITAR) in the Boonshoft School of Medicine and the Department of Computer Science and Engineering. In addition, we aimed to develop and deploy an innovative prediction analysis algorithm for eDrugTrends, capable of semi-automated processing of Twitter data to identify emerging trends in cannabis and synthetic cannabinoid use in the U.S. In addition, the study included aim four, a use case study defined by tweets content analyzing PLWH, medication patterns, and identifying keyword trends via Twitter-based, user-generated content. This case study leveraged a multidisciplinary collaboration between researchers at the Departments of Family Medicine and Population and Public Health Sciences at Wright State University’s Boonshoft School of Medicine and the Department of Computer Science and Engineering. We collected 65K data from February 2022 to July 2022 with the U.S.-based HIV knowledge domain recruited via the Twitter API streaming platform. For knowledge discovery, domain knowledge plays a significant role in powering many intelligent frameworks, such as data analysis, information retrieval, and pattern recognition. Recent NLP and semantic web advances have contributed to extending the domain knowledge of medical terms. These techniques required a bag of seeds for medical knowledge discovery. Various initiate seeds create irrelevant data to the noise and negatively impact the prediction analysis performance. The methodology of aim one, PatRDis classifier, applied for noisy and ambiguous issues, and aim two, DsOn Ontology model, applied for semantic parsing and enriching the online medical to classify the data for HIV care medications engagement and symptom detection from Twitter. By applying the methodology of aims 2 and 3, we solved the challenges of ambiguity and explored more than 1500 cannabis and cannabinoid slang terms. Sentiments measured preceding the election, such as states with high levels of positive sentiment preceding the election who were engaged in enhancing their legalization status. we also used the same dataset for prediction analysis for marijuana legalization and consumption trend analysis (Ohio public polling data). In Aim 4, we applied three experiments, ensemble-learning, the RNN-LSM, the NNBERT-CNN models, and five techniques to determine the tweets associated with medication adherence and HIV symptoms. The long short-term memory (LSTM) model and the CNN for sentence classification produce accurate results and have been recently used in NLP tasks. CNN models use convolutional layers and maximum pooling or max-overtime pooling layers to extract higher-level features, while LSTM models can capture long-term dependencies between word sequences hence are better used for text classification. We propose attention-based RNN, MLP, and CNN deep learning models that capitalize on the advantages of LSTM and BERT techniques with an additional attention mechanism. We trained the model using NNBERT to evaluate the proposed model\u27s performance. The test results showed that the proposed models produce more accurate classification results, and BERT obtained higher recall and F1 scores than MLP or LSTM models. In addition, We developed an intelligent tool capable of automated processing of Twitter data to identify emerging trends in HIV disease, HIV symptoms, and medication adherence

Patent Data for Engineering Design: A Critical Review and Future Directions

Patent data have long been used for engineering design research because of its large and expanding size, and widely varying massive amount of design information contained in patents. Recent advances in artificial intelligence and data science present unprecedented opportunities to develop data-driven design methods and tools, as well as advance design science, using the patent database. Herein, we survey and categorize the patent-for-design literature based on its contributions to design theories, methods, tools, and strategies, as well as the types of patent data and data-driven methods used in respective studies. Our review highlights promising future research directions in patent data-driven design research and practice.Comment: Accepted by JCIS

Integration and mining of malaria molecular, functional and pharmacological data: how far are we from a chemogenomic knowledge space?

The organization and mining of malaria genomic and post-genomic data is highly motivated by the necessity to predict and characterize new biological targets and new drugs. Biological targets are sought in a biological space designed from the genomic data from Plasmodium falciparum, but using also the millions of genomic data from other species. Drug candidates are sought in a chemical space containing the millions of small molecules stored in public and private chemolibraries. Data management should therefore be as reliable and versatile as possible. In this context, we examined five aspects of the organization and mining of malaria genomic and post-genomic data: 1) the comparison of protein sequences including compositionally atypical malaria sequences, 2) the high throughput reconstruction of molecular phylogenies, 3) the representation of biological processes particularly metabolic pathways, 4) the versatile methods to integrate genomic data, biological representations and functional profiling obtained from X-omic experiments after drug treatments and 5) the determination and prediction of protein structures and their molecular docking with drug candidate structures. Progresses toward a grid-enabled chemogenomic knowledge space are discussed.Comment: 43 pages, 4 figures, to appear in Malaria Journa

Natural Language Processing in-and-for Design Research

We review the scholarly contributions that utilise Natural Language Processing (NLP) methods to support the design process. Using a heuristic approach, we collected 223 articles published in 32 journals and within the period 1991-present. We present state-of-the-art NLP in-and-for design research by reviewing these articles according to the type of natural language text sources: internal reports, design concepts, discourse transcripts, technical publications, consumer opinions, and others. Upon summarizing and identifying the gaps in these contributions, we utilise an existing design innovation framework to identify the applications that are currently being supported by NLP. We then propose a few methodological and theoretical directions for future NLP in-and-for design research