MScanner: a classifier for retrieving Medline citations

Background  

Keyword searching through PubMed and other systems is the standard means of retrieving information from Medline. However, ad-hoc retrieval systems do not meet all of the needs of databases that curate information from literature, or of text miners developing a corpus on a topic that has many terms indicative of relevance. Several databases have developed supervised learning methods that operate on a filtered subset of Medline, to classify Medline records so that fewer articles have to be manually reviewed for relevance. A few studies have considered generalisation of Medline classification to operate on the entire Medline database in a non-domain-specific manner, but existing applications lack speed, available implementations, or a means to measure performance in new domains.     

BioContrasts: extracting and exploiting protein-protein contrastive relations from biomedical literature

MOTIVATION: Contrasts are useful conceptual vehicles for learning processes and exploratory research of the unknown. For example, contrastive information between proteins can reveal what similarities, divergences and relations there are of the two proteins, leading to invaluable insights for better understanding about the proteins. Such contrastive information are found to be reported in the biomedical literature. However, there have been no reported attempts in current biomedical text mining work that systematically extract and present such useful contrastive information from the literature for exploitation. RESULTS: Our BioContrasts system extracts protein-protein contrastive information from MEDLINE abstracts and presents the information to biologists in a web-application for exploitation. Contrastive information are identified in the text abstracts with contrastive negation patterns such as 'A but not B'. A total of 799 169 pairs of contrastive expressions were successfully extracted from 2.5 million MEDLINE abstracts. Using grounding of contrastive protein names to Swiss-Prot entries, we were able to produce 41 471 pieces of contrasts between Swiss-Prot protein entries. These contrastive pieces of information are then presented via a user-friendly interactive web portal that can be exploited for applications such as the refinement of biological pathways. AVAILABILITY: BioContrasts can be accessed at http://biocontrasts.i2r.a-star.edu.sg. It is also mirrored at http://biocontrasts.biopathway.org. SUPPLEMENTARY INFORMATION: Supplementary materials are available at Bioinformatics online.     

Competition between three Chihuahuan desert species: evidence from plant size-distance relations and root distribution

Abstract. Using size-distance data we tested the intensity and importance of competition between Hilaria mutica (a tussock grass), Larrea tridentata (a microphyllous shrub) and Opuntia rastrera (a succulent) in the Chihuahuan desert. We also compared the vertical and horizontal distribution of roots to assess the potential degree of overlap in the use of soil resources. The relationships between sizes and distances of nearest-neighbour plants suggested that intraspecific competition is generally more important than interspecific competition. However, evidence of stronger inter than intraspecific competition was found in some cases. Species combinations showing significant interspecific competition involved always Opuntia, whereas Larrea and Hilaria did not influence each other. The analysis of the symmetry of competition showed that Opuntia was adversely affected by the presence of Hilaria or Larrea. Although differences were found in the distribution of roots, the results of the size-distance study support the idea that, (particularly) Opuntia, below-ground niche differentiation is not sufficiently important to totally avoid the negative effects of plant competition.     

Concept recognition for extracting protein interaction relations from biomedical text

Background:

Reliable information extraction applications have been a long sought goal of the biomedical text mining community, a goal that if reached would provide valuable tools to benchside biologists in their increasingly difficult task of assimilating the knowledge contained in the biomedical literature. We present an integrated approach to concept recognition in biomedical text. Concept recognition provides key information that has been largely missing from previous biomedical information extraction efforts, namely direct links to well defined knowledge resources that explicitly cement the concept's semantics. The BioCreative II tasks discussed in this special issue have provided a unique opportunity to demonstrate the effectiveness of concept recognition in the field of biomedical language processing.

Results:

Through the modular construction of a protein interaction relation extraction system, we present several use cases of concept recognition in biomedical text, and relate these use cases to potential uses by the benchside biologist.

Conclusion:

Current information extraction technologies are approaching performance standards at which concept recognition can begin to deliver high quality data to the benchside biologist. Our system is available as part of the BioCreative Meta-Server project and on the internet http://bionlp.sourceforge.net.

     

Extraction of semantic biomedical relations from text using conditional random fields

Background  

The increasing amount of published literature in biomedicine represents an immense source of knowledge, which can only efficiently be accessed by a new generation of automated information extraction tools. Named entity recognition of well-defined objects, such as genes or proteins, has achieved a sufficient level of maturity such that it can form the basis for the next step: the extraction of relations that exist between the recognized entities. Whereas most early work focused on the mere detection of relations, the classification of the type of relation is also of great importance and this is the focus of this work. In this paper we describe an approach that extracts both the existence of a relation and its type. Our work is based on Conditional Random Fields, which have been applied with much success to the task of named entity recognition.     

A re-evaluation of biomedical named entity-term relations

Text mining can support the interpretation of the enormous quantity of textual data produced in biomedical field. Recent developments in biomedical text mining include advances in the reliability of the recognition of named entities (NEs) such as specific genes and proteins, as well as movement toward richer representations of the associations of NEs. We argue that this shift in representation should be accompanied by the adoption of a more detailed model of the relations holding between NEs and other relevant domain terms. As a step toward this goal, we study NE-term relations with the aim of defining a detailed, broadly applicable set of relation types based on accepted domain standard concepts for use in corpus annotation and domain information extraction approaches.     

Yale Image Finder (YIF): a new search engine for retrieving biomedical images

Yale Image Finder (YIF) is a publicly accessible search engine featuring a new way of retrieving biomedical images and associated papers based on the text carried inside the images. Image queries can also be issued against the image caption, as well as words in the associated paper abstract and title. A typical search scenario using YIF is as follows: a user provides few search keywords and the most relevant images are returned and presented in the form of thumbnails. Users can click on the image of interest to retrieve the high resolution image. In addition, the search engine will provide two types of related images: those that appear in the same paper, and those from other papers with similar image content. Retrieved images link back to their source papers, allowing users to find related papers starting with an image of interest. Currently, YIF has indexed over 140 000 images from over 34 000 open access biomedical journal papers. AVAILABILITY: http://krauthammerlab.med.yale.edu/imagefinder/     

CROssBAR: comprehensive resource of biomedical relations with knowledge graph representations

Systemic analysis of available large-scale biological/biomedical data is critical for studying biological mechanisms, and developing novel and effective treatment approaches against diseases. However, different layers of the available data are produced using different technologies and scattered across individual computational resources without any explicit connections to each other, which hinders extensive and integrative multi-omics-based analysis. We aimed to address this issue by developing a new data integration/representation methodology and its application by constructing a biological data resource. CROssBAR is a comprehensive system that integrates large-scale biological/biomedical data from various resources and stores them in a NoSQL database. CROssBAR is enriched with the deep-learning-based prediction of relationships between numerous data entries, which is followed by the rigorous analysis of the enriched data to obtain biologically meaningful modules. These complex sets of entities and relationships are displayed to users via easy-to-interpret, interactive knowledge graphs within an open-access service. CROssBAR knowledge graphs incorporate relevant genes-proteins, molecular interactions, pathways, phenotypes, diseases, as well as known/predicted drugs and bioactive compounds, and they are constructed on-the-fly based on simple non-programmatic user queries. These intensely processed heterogeneous networks are expected to aid systems-level research, especially to infer biological mechanisms in relation to genes, proteins, their ligands, and diseases.     

FACTA: a text search engine for finding associated biomedical concepts

FACTA is a text search engine for MEDLINE abstracts, which is designed particularly to help users browse biomedical concepts (e.g. genes/proteins, diseases, enzymes and chemical compounds) appearing in the documents retrieved by the query. The concepts are presented to the user in a tabular format and ranked based on the co-occurrence statistics. Unlike existing systems that provide similar functionality, FACTA pre-indexes not only the words but also the concepts mentioned in the documents, which enables the user to issue a flexible query (e.g. free keywords or Boolean combinations of keywords/concepts) and receive the results immediately even when the number of the documents that match the query is very large. The user can also view snippets from MEDLINE to get textual evidence of associations between the query terms and the concepts. The concept IDs and their names/synonyms for building the indexes were collected from several biomedical databases and thesauri, such as UniProt, BioThesaurus, UMLS, KEGG and DrugBank. AVAILABILITY: The system is available at http://www.nactem.ac.uk/software/facta/     

Fecundity selection theory: concepts and evidence

Fitness results from an optimal balance between survival, mating success and fecundity. The interactions between these three components of fitness vary depending on the selective context, from positive covariation between them, to antagonistic pleiotropic relationships when fitness increases in one reduce the fitness of others. Therefore, elucidating the routes through which selection shapes life history and phenotypic adaptations via these fitness components is of primary significance to understanding ecological and evolutionary dynamics. However, while the fitness components mediated by natural (survival) and sexual (mating success) selection have been debated extensively from most possible perspectives, fecundity selection remains considerably less studied. Here, we review the theoretical basis, evidence and implications of fecundity selection as a driver of sex‐specific adaptive evolution. Based on accumulating literature on the life‐history, phenotypic and ecological aspects of fecundity, we (i) suggest a re‐arrangement of the concepts of fecundity, whereby we coin the term ‘transient fecundity’ to refer to brood size per reproductive episode, while ‘annual’ and ‘lifetime fecundity’ should not be used interchangeably with ‘transient fecundity’ as they represent different life‐history parameters; (ii) provide a generalized re‐definition of the concept of fecundity selection as a mechanism that encompasses any traits that influence fecundity in any direction (from high to low) and in either sex; (iii) review the (macro)ecological basis of fecundity selection (e.g. ecological pressures that influence predictable spatial variation in fecundity); (iv) suggest that most ecological theories of fecundity selection should be tested in organisms other than birds; (v) argue that the longstanding fecundity selection hypothesis of female‐biased sexual size dimorphism (SSD) has gained inconsistent support, that strong fecundity selection does not necessarily drive female‐biased SSD, and that this form of SSD can be driven by other selective pressures; and (vi) discuss cases in which fecundity selection operates on males. This conceptual analysis of the theory of fecundity selection promises to help illuminate one of the central components of fitness and its contribution to adaptive evolution.     

Agricultural sustainability: concepts, principles and evidence

Concerns about sustainability in agricultural systems centre on the need to develop technologies and practices that do not have adverse effects on environmental goods and services, are accessible to and effective for farmers, and lead to improvements in food productivity. Despite great progress in agricultural productivity in the past half-century, with crop and livestock productivity strongly driven by increased use of fertilizers, irrigation water, agricultural machinery, pesticides and land, it would be over-optimistic to assume that these relationships will remain linear in the future. New approaches are needed that will integrate biological and ecological processes into food production, minimize the use of those non-renewable inputs that cause harm to the environment or to the health of farmers and consumers, make productive use of the knowledge and skills of farmers, so substituting human capital for costly external inputs, and make productive use of people's collective capacities to work together to solve common agricultural and natural resource problems, such as for pest, watershed, irrigation, forest and credit management. These principles help to build important capital assets for agricultural systems: natural; social; human; physical; and financial capital. Improving natural capital is a central aim, and dividends can come from making the best use of the genotypes of crops and animals and the ecological conditions under which they are grown or raised. Agricultural sustainability suggests a focus on both genotype improvements through the full range of modern biological approaches and improved understanding of the benefits of ecological and agronomic management, manipulation and redesign. The ecological management of agroecosystems that addresses energy flows, nutrient cycling, population-regulating mechanisms and system resilience can lead to the redesign of agriculture at a landscape scale. Sustainable agriculture outcomes can be positive for food productivity, reduced pesticide use and carbon balances. Significant challenges, however, remain to develop national and international policies to support the wider emergence of more sustainable forms of agricultural production across both industrialized and developing countries.     

Inertial vestibular coding of motion: concepts and evidence

Central processing of inertial sensory information about head attitude and motion in space is crucial for motor control. Vestibular signals are coded relative to a non-inertial system, the head, that is virtually continuously in motion. Evidence for transformation of vestibular signals from head-fixed sensory coordinates to gravity-centered coordinates have been provided by studies of the vestibulo-ocular reflex. The underlying central processing depends on otolith afferent information that needs to be resolved in terms of head translation related inertial forces and head attitude dependent pull of gravity. Theoretical solutions have been suggested, but experimental evidence is still scarce. It appears, along these lines, that gaze control systems are intimately linked to motor control of head attitude and posture.     

Co-occurrence based meta-analysis of scientific texts: retrieving biological relationships between genes

MOTIVATION: The advent of high-throughput experiments in molecular biology creates a need for methods to efficiently extract and use information for large numbers of genes. Recently, the associative concept space (ACS) has been developed for the representation of information extracted from biomedical literature. The ACS is a Euclidean space in which thesaurus concepts are positioned and the distances between concepts indicates their relatedness. The ACS uses co-occurrence of concepts as a source of information. In this paper we evaluate how well the system can retrieve functionally related genes and we compare its performance with a simple gene co-occurrence method. RESULTS: To assess the performance of the ACS we composed a test set of five groups of functionally related genes. With the ACS good scores were obtained for four of the five groups. When compared to the gene co-occurrence method, the ACS is capable of revealing more functional biological relations and can achieve results with less literature available per gene. Hierarchical clustering was performed on the ACS output, as a potential aid to users, and was found to provide useful clusters. Our results suggest that the algorithm can be of value for researchers studying large numbers of genes. AVAILABILITY: The ACS program is available upon request from the authors.     

Developmental studies of twins from birth on: heredity, environment, biomedical variables, and co-twin relations.

The reports in this section demonstrate several ways in which longitudinal research with twins is informative for the study of development. All of these twins have been followed for long time periods. These results are the latest stage of study for each. By virtue of their long-term nature, together these studies provide information on patterns and changes in several developmental areas from infancy to adulthood. They also document when specific variables no longer exert any influence on development. The first report, by Alin Akerman and Suurvee ("The Cognitive and Identity Development of Twins at 16 Years of Age: A Follow-up Study of 32 Twin Pairs") is a study of twins followed from birth to 16 years of age. The second report, by Ebeling, Porkka, Penninkilampi-Kerola, Berg, Jarvi, and Moilanen ("Inter-twin Relationships and Mental Health"), is a study of twins followed from pregnancy to 22-30 years of age. The third report, by Lange ("Coping Ability at Midlife in Relation to Genetic and Environmental Influences at Adolescence"), is a study of twins and singletons followed from 10 to 16 years of age to 35 years of age. The section attests to the perseverance of these authors as researchers, and to the strength of the personal relationships of these authors with the individuals in their projects.     

Communication between neurones: Current concepts

Neurotransmitters are chemicals which have the specific function of transferring information from one neurone to another at specific sites called synapses. This concept is discussed in relation to the experimental evidence which suggests that neurotransmitters may be released non synaptically and that certain neurones may utilise more than one transmitter substance. The term ‘modulator’ is also discussed and compared with what is understood to be a ‘neurotransmitter’.     

Extraction of regulatory gene/protein networks from Medline

MOTIVATION: We have previously developed a rule-based approach for extracting information on the regulation of gene expression in yeast. The biomedical literature, however, contains information on several other equally important regulatory mechanisms, in particular phosphorylation, which we now expanded for our rule-based system also to extract. RESULTS: This paper presents new results for extraction of relational information from biomedical text. We have improved our system, STRING-IE, to capture both new types of linguistic constructs as well as new types of biological information [i.e. (de-)phosphorylation]. The precision remains stable with a slight increase in recall. From almost one million PubMed abstracts related to four model organisms, we manage to extract regulatory networks and binary phosphorylations comprising 3,319 relation chunks. The accuracy is 83-90% and 86-95% for gene expression and (de-)phosphorylation relations, respectively. To achieve this, we made use of an organism-specific resource of gene/protein names considerably larger than those used in most other biology related information extraction approaches. These names were included in the lexicon when retraining the part-of-speech (POS) tagger on the GENIA corpus. For the domain in question, an accuracy of 96.4% was attained on POS tags. It should be noted that the rules were developed for yeast and successfully applied to both abstracts and full-text articles related to other organisms with comparable accuracy. AVAILABILITY: The revised GENIA corpus, the POS tagger, the extraction rules and the full sets of extracted relations are available from http://www.bork.embl.de/Docu/STRING-IE     

miRSel: Automated extraction of associations between microRNAs and genes from the biomedical literature

Background  

MicroRNAs have been discovered as important regulators of gene expression. To identify the target genes of microRNAs, several databases and prediction algorithms have been developed. Only few experimentally confirmed microRNA targets are available in databases. Many of the microRNA targets stored in databases were derived from large-scale experiments that are considered not very reliable. We propose to use text mining of publication abstracts for extracting microRNA-gene associations including microRNA-target relations to complement current repositories.     

The qualitative and time-dependent character of spatial relations in biomedical ontologies

MOTIVATION: The formal representation of mereological aspects of canonical anatomy (parthood relations) is relatively well understood. The formal representation of other aspects of canonical anatomy, such as connectedness and adjacency relations between anatomical parts, their shape and size as well as the spatial arrangement of anatomical parts within larger anatomical structures are, however, much less well understood and represented in existing computational anatomical and bio-medical ontologies only insufficiently. RESULTS: In this article, we provide a methodology of how to incorporate this kind of information into anatomical and bio-medical ontologies by applying techniques of representing qualitative spatial information from Artificial Intelligence. In particular, we focus on how to explicitly take into account the qualitative and time-dependent character of these relations. As a running example, we use the human temporomandibular joint (TMJ). AVAILABILITY: Using the presented methodology, a formal ontology was developed which is accessible on http://www.ifomis.org/bfo/fol. This ontology may help to improve the logical and ontological rigor of bio-medical ontologies such as the OBO relation ontology.