Biodiversity informatics : organizing and linking information across the spectrum of life

This article has been accepted for publication in Briefings in Bioinformatics © 2007 The Author Published by Oxford University Press. All rights reserved. This is a pre-print, electronic version of an article published in Briefings in Bioinformatics 8 (2007) 347-357, doi:10.1093/bib/bbm037Biological knowledge can be inferred from three major levels of information: molecules, organisms, and ecologies. Bioinformatics is an established field that has made significant advances in the development of systems and techniques to organize contemporary molecular data; biodiversity informatics is an emerging discipline that strives to develop methods to organize knowledge at the organismal level extending back to the earliest dates of recorded natural history. Furthermore, while bioinformatics studies generally focus on detailed examinations of key “model” organisms, biodiversity informatics aims to develop over-arching hypotheses that span the entire tree of life. Biodiversity informatics is presented here as a discipline that unifies biological information from a range of contemporary and historical sources across the spectrum of life using organisms as the linking thread. The present review primarily focuses on the use of organism names as a universal meta-data element to link and integrate biodiversity data across a range of data sources

Ontologies and Information Extraction

This report argues that, even in the simplest cases, IE is an ontology-driven process. It is not a mere text filtering method based on simple pattern matching and keywords, because the extracted pieces of texts are interpreted with respect to a predefined partial domain model. This report shows that depending on the nature and the depth of the interpretation to be done for extracting the information, more or less knowledge must be involved. This report is mainly illustrated in biology, a domain in which there are critical needs for content-based exploration of the scientific literature and which becomes a major application domain for IE

Creating, Modeling, and Visualizing Metabolic Networks

Metabolic networks combine metabolism and regulation. These complex networks are difficult to understand and create due to the diverse types of information that need to be represented. This chapter describes a suite of interlinked tools for developing, displaying, and modeling metabolic networks. The metabolic network interactions database, MetNetDB, contains information on regulatory and metabolic interactions derived from a combination of web databases and input from biologists in their area of expertise. PathBinderA mines the biological “literaturome” by searching for new interactions or supporting evidence for existing interactions in metabolic networks. Sentences from abstracts are ranked in terms of the likelihood that an interaction is described and combined with evidence provided by other sentences. FCModeler, a publicly available software package, enables the biologist to visualize and model metabolic and regulatory network maps. FCModeler aids in the development and evaluation of hypotheses, and provides a modeling framework for assessing the large amounts of data captured by high-throughput gene expression experiments

PathBinder: a sentence repository of biochemical interactions extracted from MEDLINE

MEDLINE is a fast growing online scientific literature database covering the fields of life science, medicine, health care, etc. It provides attractive opportunities for automatic information extraction for tasks such as extracting networks of protein interactions, as well as for benefiting researchers who need to efficiently sift through the literature to find work relating to small sets of biochemicals of interest. PathBinder is a software system that extracts sentences containing potential biochemical interactions from the baseline MEDLINE database annual distribution. Interactions between two biochemicals are assumed if they co-occur in a single sentence. Single sentences were parsed from MEDLINE abstracts, and scanned against a dictionary containing more than 80,000 entries (\u3e40,000 biochemicals and their aliases) for at least two different biochemicals. The dictionary was constructed automatically by extracting names and synonyms of protein and non-protein biochemicals from four databases. The extracted sentences are organized in a repository, about 11 GB in size, easily retrievable through a 2-level index system based on two biochemical names. The performance of PathBinder in terms of information extraction metrics (e.g. precision and recall) was evaluated using a sample MEDLINE file. Sentence parsing has a precision of 99.6% and a recall of 99.5%. Biochemical labeling had a precision of 80.5% and a recall of 57.3%

Computing Network of Diseases and Pharmacological Entities through the Integration of Distributed Literature Mining and Ontology Mapping

The proliferation of -omics (such as, Genomics, Proteomics) and -ology (such as, System Biology, Cell Biology, Pharmacology) have spawned new frontiers of research in drug discovery and personalized medicine. A vast amount (21 million) of published research results are archived in the PubMed and are continually growing in size. To improve the accessibility and utility of such a large number of literatures, it is critical to develop a suit of semantic sensitive technology that is capable of discovering knowledge and can also infer possible new relationships based on statistical co-occurrences of meaningful terms or concepts. In this context, this thesis presents a unified framework to mine a large number of literatures through the integration of latent semantic analysis (LSA) and ontology mapping. In particular, a parameter optimized, robust, scalable, and distributed LSA (DiLSA) technique was designed and implemented on a carefully selected 7.4 million PubMed records related to pharmacology. The DiLSA model was integrated with MeSH to make the model effective and efficient for a specific domain. An optimized multi-gram dictionary was customized by mapping the MeSH to build the DiLSA model. A fully integrated web-based application, called PharmNet, was developed to bridge the gap between biological knowledge and clinical practices. Preliminary analysis using the PharmNet shows an improved performance over global LSA model. A limited expert evaluation was performed to validate the retrieved results and network with biological literatures. A thorough performance evaluation and validation of results is in progress

Context specific text mining for annotating protein interactions with experimental evidence

Indiana University-Purdue University Indianapolis (IUPUI)Proteins are the building blocks in a biological system. They interact with other proteins to make unique biological phenomenon. Protein-protein interactions play a valuable role in understanding the molecular mechanisms occurring in any biological system. Protein interaction databases are a rich source on protein interaction related information. They gather large amounts of information from published literature to enrich their data. Expert curators put in most of these efforts manually. The amount of accessible and publicly available literature is growing very rapidly. Manual annotation is a time consuming process. And with the rate at which available information is growing, it cannot be dealt with only manual curation. There need to be tools to process this huge amounts of data to bring out valuable gist than can help curators proceed faster. In case of extracting protein-protein interaction evidences from literature, just a mere mention of a certain protein by look-up approaches cannot help validate the interaction. Supporting protein interaction information with experimental evidence can help this cause. In this study, we are applying machine learning based classification techniques to classify and given protein interaction related document into an interaction detection method. We use biological attributes and experimental factors, different combination of which define any particular interaction detection method. Then using predicted detection methods, proteins identified using named entity recognition techniques and decomposing the parts-of-speech composition we search for sentences with experimental evidence for a protein-protein interaction. We report an accuracy of 75.1% with a F-score of 47.6% on a dataset containing 2035 training documents and 300 test documents

Automated Natural-Language Processing for Integration and Functional Annotation of Complex Biological Systems.

This dissertation discusses the use of automated natural language processing (NLP) for characterization of biomolecular events in signal transduction pathway databases. I also discuss the use of a dynamic map engine for efficiently navigating large biomedical document collections and functionally annotating high-throughput genomic data. An application is presented where NLP software, beginning with genomic expression data, automatically identifies and joins disparate experimental observations supporting biochemical interaction relationships between candidate genes in the Wnt signaling pathway. I discuss the need for accurate named entity resolution to the biological sequence databases and how sequence-based approaches can unambiguously link automatically-extracted assertions to their respective biomolecules in a high-speed manner. I then demonstrate a search engine, BioSearch-2D, which renders the contents of large biomedical document collections into a single, dynamic map. With this engine, the prostate cancer epigenetics literature is analyzed and I demonstrate that the summarization map closely matches that provided by expert human review articles. Examples include displays which prominently feature genes such as the androgen receptor and glutathione S-transferase P1 together with the National Library of Medicine’s Medical Subject Heading (MeSH) descriptions which match the roles described for those genes in the human review articles. In a second application of BioSearch-2D, I demonstrate the engine’s application as a context-specific functional annotation system for cancer-related gene signatures. Our engine matches the annotation produced by a Gene Ontology-based annotation engine for 6 cancer-related gene signatures. Additionally, it assigns highly-significant MeSH terms as annotation for the gene list which are not produced by the GO-based engine. I find that the BioSearch-2D display facilitates both the exploration of large document collections in the biomedical literature as well as provides users with an accurate annotation engine for ad-hoc gene sets. In the future, the use of both large-scale biomedical literature summarization engines and automated protein-protein interaction discovery software could greatly assist manual and expensive data curation efforts involving describing complex biological processes or disease states.Ph.D.BioinformaticsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/58394/1/csantos_1.pd

A Rule-based Methodology and Feature-based Methodology for Effect Relation Extraction in Chinese Unstructured Text

The Chinese language differs significantly from English, both in lexical representation and grammatical structure. These differences lead to problems in the Chinese NLP, such as word segmentation and flexible syntactic structure. Many conventional methods and approaches in Natural Language Processing (NLP) based on English text are shown to be ineffective when attending to these language specific problems in late-started Chinese NLP. Relation Extraction is an area under NLP, looking to identify semantic relationships between entities in the text. The term “Effect Relation” is introduced in this research to refer to a specific content type of relationship between two entities, where one entity has a certain “effect” on the other entity. In this research project, a case study on Chinese text from Traditional Chinese Medicine (TCM) journal publications is built, to closely examine the forms of Effect Relation in this text domain. This case study targets the effect of a prescription or herb, in treatment of a disease, symptom or body part. A rule-based methodology is introduced in this thesis. It utilises predetermined rules and templates, derived from the characteristics and pattern observed in the dataset. This methodology achieves the F-score of 0.85 in its Named Entity Recognition (NER) module; 0.79 in its Semantic Relationship Extraction (SRE) module; and the overall performance of 0.46. A second methodology taking a feature-based approach is also introduced in this thesis. It views the RE task as a classification problem and utilises mathematical classification model and features consisting of contextual information and rules. It achieves the F-scores of: 0.73 (NER), 0.88 (SRE) and overall performance of 0.41. The role of functional words in the contemporary Chinese language and in relation to the ERs in this research is explored. Functional words have been found to be effective in detecting the complex structure ER entities as rules in the rule-based methodology