Section 7: Bioinformatics: Bioinformatics and its Impact on Clinical Research Methods

There is global attention on new data analytic methods. Data scraping (a typical first step for advanced data analytics), text and data mining (TDM, the extraction of knowledge from data) and machine learning (ML, often also simply referred to as Artificial Intelligence or AI) are seen as critical technologies. The legal issues involved in the regulation of data range from privacy and data protection (such as the GDPR) to proprietary approaches (such as copyright, database rights, or proposed new rights in data themselves). This paper focusses on one specific intervention, the introduction of two exceptions for text and data mining in the Directive on Copyright in the Digital Single Market (CDSM). Art. 3 is a mandatory exception for text and data mining (TDM) for the purposes of scientific research; Art. 4 permits text and data mining by anyone but with rightsholders able to “contract-out” (Art. 4), for example preventing TDM use of publicly available online content by “machine-readable means”. We trace the context of using the lever of copyright law to enable emerging technologies and support innovation. Within the EU copyright intervention, elements that may underpin a transparent legal framework for AI are identified, such as the possibility of retention of (permanent) copies for further verification. On the other hand, we identify several pitfalls, including an excessively broad definition of TDM which makes the entire field of data-driven AI development dependent on an exception. We analyse the implications of limiting the scope of the exceptions to the right of reproduction (which leaves the communication of research results in a grey zone). We also argue that the limitation of Art. 3 to certain beneficiaries remains problematic; and that the requirement of lawful access is difficult to operationalize. In conclusion, we argue that there should be no need for a TDM exception for the act of extracting informational value from protected works. The EU’s CDSM provisions paradoxically may favour the development of biased AI systems due to price and accessibility conditions for accessing training data that offer the wrong incentives. We also identify some old and new areas of the EU acquis which will play a crucial role in the future relationship of EU copyright law with technological innovation.

Text Mining and Medicine: Usefulness in Respiratory Diseases

of works held in research and academic libraries and other relevant institutions, for research and private study; * clarify the current EU exception permitting the use of works that were made to be permanently located in the public space (the 'panorama exception'), to take into account new dissemination channels.

DEFINING TEXT MINING Text mining can be broadly defined as a knowledge-intensive process in which a user interacts with a document collection over time by using a suite of analysis tools. In a manner analogous to data mining, text mining seeks to extract useful information from data sources through the identification and exploration of interesting patterns. In the case of text mining, however, the data sources are document collections, and interesting patterns are found not among formalized database records but in the unstructured textual data in the documents in these collections. Certainly, text mining derives much of its inspiration and direction from seminal research on data mining. Therefore, it is not surprising to find that text mining and data mining systems evince many high-level architectural similarities. For instance, both types of systems rely on preprocessing routines, pattern-discovery algorithms, and presentation-layer elements such as visualization tools to enhance the browsing of answer sets. Further, text mining adopts many of the specific types of patterns in its core knowledge discovery operations that were first introduced and vetted in data mining research. Because data mining assumes that data have already been stored in a structured format, much of its preprocessing focus falls on two critical tasks: Scrubbing and normalizing data and creating extensive numbers of table joins. In contrast, for text mining systems, preprocessing operations center on the identification and extraction of representative features for natural language documents.

In Article 3 of the “Proposal for a Directive on copyright in the Digital Single Market COM(2016) 593 final” the European Commission suggests an exception for text and data mining (TDM). While, in principle, a clear legal framework for TDM is to be welcomed, the proposed provisions are to be criticized regarding their scope and the applied regulatory method. This Position Statement develops an alternative proposal: Since TDM is to be seen as a normal use of works and other protected subject-matter, a field exemption is suggested allowing everyone to carry out TDM related to lawfully accessible works or other subject-matter. This includes the permission to extract contents of databases and to make reproductions for the sole purpose of TDM. Moreover, research organizations also need to carry out TDM regarding content to which they do not have lawful access. The proposal includes a specific provision obliging rightholders who market works or other subject-matter primarily for research purposes to provide datasets suitable for TDM only, for which they may request a reasonable payment.

Text mining applies the same analytical functions of data mining to the domain of textual information, relying on sophisticated text analysis techniques that distill information from free-text documents. IBM’s Intelligent Miner for Text provides the necessary tools to unlock the business information that is “trapped” in email, insurance claims, news feeds, or other document repositories. It has been successfully applied in analyzing patent portfolios, customer complaint letters, and even competitors’ Web pages. After defining our notion of “text mining”, we focus on the differences between text and data mining and describe in some more detail the unique technologies that are key to successful text mining.

This study aims to uncover recent trends and themes in text mining research, focusing on online news and annual report data sources. These sources are rich in content, real-world context, and domain-specific information, making them crucial for text mining. The primary research questions guiding this study are: 1) What are the emerging trends in text mining research applied to online news and annual reports? 2) What are the themes generated based on systematic literature review and text mining technique? To address this question, the study systematically reviews a large number of related studies using the PRISMA review protocol and text mining techniques from the SCOPUS and Web of Science databases. After thorough evaluation, 34 selected articles were analyzed. The PRISMA review protocol ensures transparency and completeness in reporting the review process through its standardized approach for systematic reviews. Additionally, this systematic review explores advancements in text mining techniques such as document clustering and topic modeling, which have facilitated the identification and extraction of relevant evidence from vast amounts of textual data. The study’s findings identified four primary themes (text mining/text analytics, machine learning, deep learning, ensemble methods) with 19 sub-themes related to each theme’s methodology when applying the PRISMA protocol. By utilizing a text mining techniques, five topics were uncovered based on article keywords (text mining, text analytics, machine learning, deep learning, and ensemble) and ten topics emerged based on article abstracts. Underlying both approaches is the consistent recognition of four main areas: text mining/text analytics, machine learning, deep learning, and ensemble methods. This systematic review offers a comprehensive overview of recent text mining research and the emerging trends in this field. It highlights the importance of systematic reviews in synthesizing existing literature and identifying areas for future research.

The fields of taxonomy and biodiversity research have witnessed an exponential growth in published literature. This vast corpus of articles holds information on the diverse biological traits of organisms and their ecologies. However, access to and extraction of relevant data from this extensive resource remain challenging. Advances in text and data mining (TDM) and Natural Language Processing (NLP) techniques offer new opportunities for liberating such information from literature. Testing and using such approaches to annotate articles in machine-actionable formats is, therefore, necessary to enable the exploitation of existing knowledge in new biology, ecology and evolution research. Here, we explore the potential of these methods to annotate and extract organismal trait data for the most diverse animal group on Earth, the arthropods. The article processing workflow uses manually curated trait dictionaries with trained NLP models to perform labelling of entities and relationships of thousands of articles. A subset of manually annotated documents facilitated the formal evaluation of the performance of the workflow in terms of entity recognition and normalisation and relationship extraction, highlighting several important technical challenges. The results are made available to the scientific community through an interactive web tool and queryable resource, the ArTraDB Arthropod Trait Database. These methodological explorations provide a framework that could be extended beyond the arthropods, where TDM and NLP approaches applied to the taxonomy and biodiversity literature will greatly facilitate data synthesis studies and literature reviews, the identification of knowledge gaps and biases, as well as the data-informed investigation of ecological and evolutionary trends and patterns.

Text mining is a special case of data mining which explore unstructured or semi-structured text documents, to establish valuable patterns and rules that indicate trends and significant features about specific topics. Text mining has been in pattern recognition, predictive studies, sentiment analysis and statistical theories in many areas of research, medicine, financial analysis, social life analysis, and business intelligence. Text mining uses concept of natural language processing and machine learning. Machine learning algorithms have been used and reported to give great results, but their performance of machine learning algorithms is affected by factors such as dataset domain, number of classes, length of the corpus, and feature selection techniques used. Redundant attribute affects the performance of the classification algorithm, but this can be reduced by using different feature selection techniques and dimensionality reduction techniques. Feature selection is a data preprocessing step that chooses a subset of input variable while eliminating features with little or no predictive information. Feature selection techniques are Information gain, Term Frequency, Term Frequency-Inverse document frequency, Mutual Information, and Chi-Square, which can use a filters, wrappers, or embedded approaches. To get the most value from machine learning, pairing the best algorithms with the right tools and processes is necessary. Little research has been done on the effect of feature selection techniques on classification accuracy for pairing of these algorithms with the best feature selection techniques for optimal results. In this research, a text classification experiment was conducted using incident management dataset, where incidents were classified into their resolver groups. Support vector machine (SVM), K-Nearest Neighbors (KNN), Naïve Bayes (NB) and Decision tree (DT) machine learning algorithms were examined. Filtering approach was used on the feature selection techniques, with different ranking indices applied for optimal feature set and classification accuracy results analyzed. The classification accuracy results obtained using TF were, 88% for SVM, 70% for NB, 79% for Decision tree, and KNN had 55%, while Boolean registered 90%, 83%, 82% and 75%, for SVM, NB, DT, and KNN respectively. TF-IDF, had 91%, 83%, 76%, and 56% for SVM, NB, DT, and KNN respectively. The results showed that algorithm performance is affected by feature selection technique applied. SVM performed best, followed by DT, KNN and finally NB. In conclusion, presence of noisy data leads to poor learning performance and increases the computational time. The classifiers performed differently depending on the feature selection technique applied. For optimal results, the classifier that performed best together with the feature selection technique with the best feature subset should be applied for all types of data for accurate classification performance. Keywords: Text Classification, Supervised Machine Learning, Feature Selection DOI : 10.7176/JIEA/9-3-06 Publication date :May 31 st 2019

The real-time system of the JT-60 has a cascade structure with the major and the minor control loops. The multi-variable feedback control is adopted in the major control loop in order to adjust non-linear effects which will appear in the high-/beta/region. By applying the concept of 'phase shift control' to the control program of the major control loop, a flexible tokamak discharge control is expected to be performed with taking account of machine protection.

Innovations in science and technology boost-up the generation of an immense volume of digital data in the form of text, audio, video, images and paved the way for a new era called Big Data. A majority of this data is generated as a semi-structured and unstructured format poses a challenge for Natural Language Processing (NLP) and text analytics. This chapter seeks to layout the text mining perspective of big data analytics with an emphasis on applications in biomedicine and healthcare. The chapter aims to provide a better understanding of areas such as Big Data, Text Mining, Data Mining, Natural Language Processing and Machine Learning and its inter linkage. The chapter illustrates phases and task of text mining in big data scope. Finally, the chapter ends with a description of the two application areas biomedicine and healthcare where text mining using big data analytics will be used.

Now-a-days information’s are stored electronically in databases. Extracting reliable, unknown and useful information from the abundant source is an eminent task. Data mining and Text mining are the process for extracting unknown and useful information. Text Mining is the process of extracting interesting and non-trivial patterns or knowledge from text documents. This paper presents the related activities and focuses on preprocessing steps in text mining. Keywords— DBMS, Data Mining, Text Mining, Preprocessing, Tokenization, Stemming, POS tagging.

The discipline of text and data mining (TDM) is growing in visibility in the scholarly research community. In May 2014, CrossRef launched CrossRef Text and Data Mining Services, which aims to alleviate the issues that text mining researchers encounter when trying to collect the corpus of content that they want to mine from the different publishers who have produced it. This article will discuss what the CrossRef service does, the problem that it is trying to solve and the current status of the rollout of the service across CrossRef member publishers.

The analysis of the text content in emails, blogs, tweets, forums and other forms of textual communication constitutes what we call text analytics. Text analytics is applicable to most industries: it can help analyze millions of emails; you can analyze customers’ comments and questions in forums; you can perform sentiment analysis using text analytics by measuring positive or negative perceptions of a company, brand, or product. Text Analytics has also been called text mining, and is a subcategory of the Natural Language Processing (NLP) field, which is one of the founding branches of Artificial Intelligence, back in the 1950s, when an interest in understanding text originally developed. Currently Text Analytics is often considered as the next step in Big Data analysis. Text Analytics has a number of subdivisions: Information Extraction, Named Entity Recognition, Semantic Web annotated domain’s representation, and many more. Several techniques are currently used and some of them have gained a lot of attention, such as Machine Learning, to show a semisupervised enhancement of systems, but they also present a number of limitations which make them not always the only or the best choice. We conclude with current and near future applications of Text Analytics.

The molecular basis of diabetes mellitus is yet to be fully elucidated. We aimed to identify the most frequently reported and differential expressed genes (DEGs) in diabetes by using bioinformatics approaches. Text mining was used to screen 40,225 article abstracts from diabetes literature. These studies highlighted 5939 diabetes-related genes spread across 22 human chromosomes, with 112 genes mentioned in more than 50 studies. Among these genes, HNF4A, PPARA, VEGFA, TCF7L2, HLA-DRB1, PPARG, NOS3, KCNJ11, PRKAA2, and HNF1A were mentioned in more than 200 articles. These genes are correlated with the regulation of glycogen and polysaccharide, adipogenesis, AGE/RAGE, and macrophage differentiation. Three datasets (44 patients and 57 controls) were subjected to gene expression analysis. The analysis revealed 135 significant DEGs, of which CEACAM6, ENPP4, HDAC5, HPCAL1, PARVG, STYXL1, VPS28, ZBTB33, ZFP37 and CCDC58 were the top 10 DEGs. These genes were enriched in aerobic respiration, T-cell antigen receptor pathway, tricarboxylic acid metabolic process, vitamin D receptor pathway, toll-like receptor signaling, and endoplasmic reticulum (ER) unfolded protein response. The results of text mining and gene expression analyses used as attribute values for machine learning (ML) analysis. The decision tree, extra-tree regressor and random forest algorithms were used in ML analysis to identify unique markers that could be used as diabetes diagnosis tools. These algorithms produced prediction models with accuracy ranges from 0.6364 to 0.88 and overall confidence interval (CI) of 95%. There were 39 biomarkers that could distinguish diabetic and non-diabetic patients, 12 of which were repeated multiple times. The majority of these genes are associated with stress response, signalling regulation, locomotion, cell motility, growth, and muscle adaptation. Machine learning algorithms highlighted the use of the HLA-DQB1 gene as a biomarker for diabetes early detection. Our data mining and gene expression analysis have provided useful information about potential biomarkers in diabetes.