Overview of BioCreative II gene normalization

Background: The goal of the gene normalization task is to link genes or gene products mentioned in the literature to biological databases. This is a key step in an accurate search of the biological literature. It is a challenging task, even for the human expert; genes are often described rather than referred to by gene symbol and, confusingly, one gene name may refer to different genes (often from different organisms). For BioCreative II, the task was to list the Entrez Gene identifiers for human genes or gene products mentioned in PubMed/MEDLINE abstracts. We selected abstracts associated with articles previously curated for human genes. We provided 281 expert-annotated abstracts containing 684 gene identifiers for training, and a blind test set of 262 documents containing 785 identifiers, with a gold standard created by expert annotators. Inter-annotator agreement was measured at over 90%. Results: Twenty groups submitted one to three runs each, for a total of 54 runs. Three systems achieved F-measures (balanced precision and recall) between 0.80 and 0.81. Combining the system outputs using simple voting schemes and classifiers obtained improved results; the best composite system achieved an F-measure of 0.92 with 10-fold cross-validation. A 'maximum recall' system based on the pooled responses of all participants gave a recall of 0.97 (with precision 0.23), identifying 763 out of 785 identifiers. Conclusion: Major advances for the BioCreative II gene normalization task include broader participation (20 versus 8 teams) and a pooled system performance comparable to human experts, at over 90% agreement. These results show promise as tools to link the literature with biological databases

A comparison of personal name matching: Techniques and practical issues

Finding and matching personal names is at the core of an increasing number of applications: from text and Web mining, information retrieval and extraction, search engines, to deduplication and data linkage systems. Variations and errors in names make exact string matching problematic, and approximate matching techniques based on phonetic encoding or pattern matching have to be applied. When compared to general text, however, personal names have different characteristics that need to be considered. ¶ In this paper we discuss the characteristics of personal names and present potential sources of variations and errors. We overview a comprehensive number of commonly used, as well as some recently developed name matching techniques. Experimental comparisons on four large name data sets indicate that there is no clear best technique. We provide a series of recommendations that will help researchers and practitioners to select a name matching technique suitable for a given data set

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

Approximate string matching methods for duplicate detection and clustering tasks

Approximate string matching methods are utilized by a vast number of duplicate detection and clustering applications in various knowledge domains. The application area is expected to grow due to the recent significant increase in the amount of digital data and knowledge sources. Despite the large number of existing string similarity metrics, there is a need for more precise approximate string matching methods to improve the efficiency of computer-driven data processing, thus decreasing labor-intensive human involvement. This work introduces a family of novel string similarity methods, which outperform a number of effective well-known and widely used string similarity functions. The new algorithms are designed to overcome the most common problem of the existing methods which is the lack of context sensitivity. In this evaluation, the Longest Approximately Common Prefix (LACP) method achieved the highest values of average precision and maximum F1 on three out of four medical informatics datasets used. The LACP demonstrated the lowest execution time ensured by the linear computational complexity within the set of evaluated algorithms. An online interactive spell checker of biomedical terms was developed based on the LACP method. The main goal of the spell checker was to evaluate the LACP method’s ability to make it possible to estimate the similarity of resulting sets at a glance. The Shortest Path Edit Distance (SPED) outperformed all evaluated similarity functions and gained the highest possible values of the average precision and maximum F1 measures on the bioinformatics datasets. The SPED design was inspired by the preceding work on the Markov Random Field Edit Distance (MRFED). The SPED eradicates two shortcomings of the MRFED, which are prolonged execution time and moderate performance. Four modifications of the Histogram Difference (HD) method demonstrated the best performance on the majority of the life and social sciences data sources used in the experiments. The modifications of the HD algorithm were achieved using several re- scorers: HD with Normalized Smith-Waterman Re-scorer, HD with TFIDF and Jaccard re-scorers, HD with the Longest Common Prefix and TFIDF re-scorers, and HD with the Unweighted Longest Common Prefix Re-scorer. Another contribution of this dissertation includes the extensive analysis of the string similarity methods evaluation for duplicate detection and clustering tasks on the life and social sciences, bioinformatics, and medical informatics domains. The experimental results are illustrated with precision-recall charts and a number of tables presenting the average precision, maximum F1, and execution time

Automated Data Digitization System for Vehicle Registration Certificates Using Google Cloud Vision API

This study aims to develop an automated data digitization system for the Thai vehicle registration certificate. It is the first system developed as a web service Application Programming Interface (API), which is essential for any enterprise to increase its business value. Currently, this system is available on “www.carjaidee.com”. The system involves four steps: 1) an embedded frame aligns a document to be correctly recognised in the image acquisition step; 2) sharpening and brightness filtering techniques to enhance image quality are applied in the pre-processing step; 3) the Google Cloud Vision API receives a prompt to proceed in the recognition step; 4) a specific domain dictionary to improve accuracy rate is developed for the post-processing step. This study defines 92 images for the experiment by counting the correct words and terms from the output. The findings suggest that the proposed method, which had an average accuracy of 93.28%, was significantly more accurate than the original method using only the Google Cloud Vision API. However, the system is limited because the dictionaries cannot automatically recognise a new word. In the future, we will explore solutions to this problem using natural language processing techniques. Doi: 10.28991/CEJ-2022-08-07-09 Full Text: PD

Extract interaction detection methods from the biological literature

Abstract Background Considerable efforts have been made to extract protein-protein interactions from the biological literature, but little work has been done on the extraction of interaction detection methods. It is crucial to annotate the detection methods in the literature, since different detection methods shed different degrees of reliability on the reported interactions. However, the diversity of method mentions in the literature makes the automatic extraction quite challenging. Results In this article, we develop a generative topic model, the Correlated Method-Word model (CMW model) to extract the detection methods from the literature. In the CMW model, we formulate the correlation between the different methods and related words in a probabilistic framework in order to infer the potential methods from the given document. By applying the model on a corpus of 5319 full text documents annotated by the MINT and IntAct databases, we observe promising results, which outperform the best result reported in the BioCreative II challenge evaluation. Conclusion From the promising experiment results, we can see that the CMW model overcomes the issues caused by the diversity in the method mentions and properly captures the in-depth correlations between the detection methods and related words. The performance outperforming the baseline methods confirms that the dependence assumptions of the model are reasonable and the model is competent for the practical processing.</p

A method for automatically extracting infectious disease-related primers and probes from the literature

BACKGROUND: Primer and probe sequences are the main components of nucleic acid-based detection systems. Biologists use primers and probes for different tasks, some related to the diagnosis and prescription of infectious diseases. The biological literature is the main information source for empirically validated primer and probe sequences. Therefore, it is becoming increasingly important for researchers to navigate this important information. In this paper, we present a four-phase method for extracting and annotating primer/probe sequences from the literature. These phases are: (1) convert each document into a tree of paper sections, (2) detect the candidate sequences using a set of finite state machine-based recognizers, (3) refine problem sequences using a rule-based expert system, and (4) annotate the extracted sequences with their related organism/gene information. RESULTS: We tested our approach using a test set composed of 297 manuscripts. The extracted sequences and their organism/gene annotations were manually evaluated by a panel of molecular biologists. The results of the evaluation show that our approach is suitable for automatically extracting DNA sequences, achieving precision/recall rates of 97.98% and 95.77%, respectively. In addition, 76.66% of the detected sequences were correctly annotated with their organism name. The system also provided correct gene-related information for 46.18% of the sequences assigned a correct organism name. CONCLUSIONS: We believe that the proposed method can facilitate routine tasks for biomedical researchers using molecular methods to diagnose and prescribe different infectious diseases. In addition, the proposed method can be expanded to detect and extract other biological sequences from the literature. The extracted information can also be used to readily update available primer/probe databases or to create new databases from scratch.The present work has been funded, in part, by the European Commission through the ACGT integrated project (FP6-2005-IST-026996) and the ACTION-Grid support action (FP7-ICT-2007-2-224176), the Spanish Ministry of Science and Innovation through the OntoMineBase project (ref. TSI2006-13021-C02-01), the ImGraSec project (ref. TIN2007-61768), FIS/AES PS09/00069 and COMBIOMED-RETICS, and the Comunidad de Madrid, Spain.S