Automated annotation of chemical names in the literature with tunable accuracy

<p>Abstract</p> <p>Background</p> <p>A significant portion of the biomedical and chemical literature refers to small molecules. The accurate identification and annotation of compound name that are relevant to the topic of the given literature can establish links between scientific publications and various chemical and life science databases. Manual annotation is the preferred method for these works because well-trained indexers can understand the paper topics as well as recognize key terms. However, considering the hundreds of thousands of new papers published annually, an automatic annotation system with high precision and relevance can be a useful complement to manual annotation.</p> <p>Results</p> <p>An automated chemical name annotation system, MeSH Automated Annotations (MAA), was developed to annotate small molecule names in scientific abstracts with tunable accuracy. This system aims to reproduce the MeSH term annotations on biomedical and chemical literature that would be created by indexers. When comparing automated free text matching to those indexed manually of 26 thousand MEDLINE abstracts, more than 40% of the annotations were false-positive (FP) cases. To reduce the FP rate, MAA incorporated several filters to remove "incorrect" annotations caused by nonspecific, partial, and low relevance chemical names. In part, relevance was measured by the position of the chemical name in the text. Tunable accuracy was obtained by adding or restricting the sections of the text scanned for chemical names. The best precision obtained was 96% with a 28% recall rate. The best performance of MAA, as measured with the F statistic was 66%, which favorably compares to other chemical name annotation systems.</p> <p>Conclusions</p> <p>Accurate chemical name annotation can help researchers not only identify important chemical names in abstracts, but also match unindexed and unstructured abstracts to chemical records. The current work is tested against MEDLINE, but the algorithm is not specific to this corpus and it is possible that the algorithm can be applied to papers from chemical physics, material, polymer and environmental science, as well as patents, biological assay descriptions and other textual data.</p

Image-based Automated Chemical Database Annotation with Ensemble of Machine-Vision Classifiers

This paper presents an image-based annotation strategy for automated annotation of chemical databases. The proposed strategy is based on the use of a machine vision-based classifier for extracting a 2D chemical structure diagram in research articles and converting them into standard chemical file formats, a virtual Chemical Expert" system for screening the converted structures based on the level of estimated conversion accuracy, and a fragment-based measure for calculation intermolecular similarity. In particular, in order to overcome limited accuracies of individual machine-vision classifier, inspired by ensemble methods in machine learning, it is attempted to use of the ensemble of machine-vision classifiers. For annotation, calculated chemical similarity between the converted structures and entries in a virtual small molecule database is used to establish the links. Annotation test to link 121 journal articles to entries in PubChem database demonstrates that ensemble approach increases the coverage of annotation, while keeping the annotation quality (e.g., recall and precision rates) comparable to using a single machine-vision classifier.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/87266/4/Saitou55.pd

Band gap information extraction from materials science literature – a pilot study

Purpose The purpose of this paper is to present a preliminary work on extracting band gap information of materials from academic papers. With increasing demand for renewable energy, band gap information will help material scientists design and implement novel photovoltaic (PV) cells. Design/methodology/approach The authors collected 1.44 million titles and abstracts of scholarly articles related to materials science, and then filtered the collection to 11,939 articles that potentially contain relevant information about materials and their band gap values. ChemDataExtractor was extended to extract information about PV materials and their band gap information. Evaluation was performed on randomly sampled information records of 415 papers. Findings The findings of this study show that the current system is able to correctly extract information for 51.32% articles, with partially correct extraction for 36.62% articles and incorrect for 12.04%. The authors have also identified the errors belonging to three main categories pertaining to chemical entity identification, band gap information and interdependency resolution. Future work will focus on addressing these errors to improve the performance of the system. Originality/value The authors did not find any literature to date on band gap information extraction from academic text using automated methods. This work is unique and original. Band gap information is of importance to materials scientists in applications such as solar cells, light emitting diodes and laser diodes

Fast and accurate semantic annotation of bioassays exploiting a hybrid of machine learning and user confirmation

Bioinformatics and computer aided drug design rely on the curation of a large number of protocols for biological assays that measure the ability of potential drugs to achieve a therapeutic effect. These assay protocols are generally published by scientists in the form of plain text, which needs to be more precisely annotated in order to be useful to software methods. We have developed a pragmatic approach to describing assays according to the semantic definitions of the BioAssay Ontology (BAO) project, using a hybrid of machine learning based on natural language processing, and a simplified user interface designed to help scientists curate their data with minimum effort. We have carried out this work based on the premise that pure machine learning is insufficiently accurate, and that expecting scientists to find the time to annotate their protocols manually is unrealistic. By combining these approaches, we have created an effective prototype for which annotation of bioassay text within the domain of the training set can be accomplished very quickly. Well-trained annotations require single-click user approval, while annotations from outside the training set domain can be identified using the search feature of a well-designed user interface, and subsequently used to improve the underlying models. By drastically reducing the time required for scientists to annotate their assays, we can realistically advocate for semantic annotation to become a standard part of the publication process. Once even a small proportion of the public body of bioassay data is marked up, bioinformatics researchers can begin to construct sophisticated and useful searching and analysis algorithms that will provide a diverse and powerful set of tools for drug discovery researchers

Ono: an open platform for social robotics

In recent times, the focal point of research in robotics has shifted from industrial ro- bots toward robots that interact with humans in an intuitive and safe manner. This evolution has resulted in the subfield of social robotics, which pertains to robots that function in a human environment and that can communicate with humans in an int- uitive way, e.g. with facial expressions. Social robots have the potential to impact many different aspects of our lives, but one particularly promising application is the use of robots in therapy, such as the treatment of children with autism. Unfortunately, many of the existing social robots are neither suited for practical use in therapy nor for large scale studies, mainly because they are expensive, one-of-a-kind robots that are hard to modify to suit a specific need. We created Ono, a social robotics platform, to tackle these issues. Ono is composed entirely from off-the-shelf components and cheap materials, and can be built at a local FabLab at the fraction of the cost of other robots. Ono is also entirely open source and the modular design further encourages modification and reuse of parts of the platform

Topical Classification of Food Safety Publications with a Knowledge Base

The vast body of scientific publications presents an increasing challenge of finding those that are relevant to a given research question, and making informed decisions on their basis. This becomes extremely difficult without the use of automated tools. Here, one possible area for improvement is automatic classification of publication abstracts according to their topic. This work introduces a novel, knowledge base-oriented publication classifier. The proposed method focuses on achieving scalability and easy adaptability to other domains. Classification speed and accuracy are shown to be satisfactory, in the very demanding field of food safety. Further development and evaluation of the method is needed, as the proposed approach shows much potential