In recent years, biological web resources such as databases and tools have become more complex because of the enormous amounts of data generated in the field of life sciences. Traditional methods of distributing tutorials include publishing textbooks and posting web documents, but these static contents cannot adequately describe recent dynamic web services. Due to improvements in computer technology, it is now possible to create dynamic content such as video with minimal effort and low cost on most modern computers. The ease of creating and distributing video tutorials instead of static content improves accessibility for researchers, annotators and curators. This article focuses on online video repositories for educational and tutorial videos provided by resource developers and users. It also describes a project in Japan named TogoTV (http://togotv.dbcls.jp/en/) and discusses the production and distribution of high-quality tutorial videos, which would be useful to viewer, with examples. This article intends to stimulate and encourage researchers who develop and use databases and tools to distribute how-to videos as a tool to enhance product usability.

BACKGROUND: The Genia task, when it was introduced in 2009, was the first community-wide effort to address a fine-grained, structural information extraction from biomedical literature. Arranged for the second time as one of the main tasks of BioNLP Shared Task 2011, it aimed to measure the progress of the community since 2009, and to evaluate generalization of the technology to full text papers. The Protein Coreference task was arranged as one of the supporting tasks, motivated from one of the lessons of the 2009 task that the abundance of coreference structures in natural language text hinders further improvement with the Genia task. RESULTS: The Genia task received final submissions from 15 teams. The results show that the community has made a significant progress, marking 74% of the best F-score in extracting bio-molecular events of simple structure, e.g., gene expressions, and 45% ~ 48% in extracting those of complex structure, e.g., regulations. The Protein Coreference task received 6 final submissions. The results show that the coreference resolution performance in biomedical domain is lagging behind that in newswire domain, cf. 50% vs. 66% in MUC score. Particularly, in terms of protein coreference resolution the best system achieved 34% in F-score. CONCLUSIONS: Detailed analysis performed on the results improves our insight into the problem and suggests the directions for further improvements.

Term clustering by measuring the string similarities between terms is known to be an effective method to improve the quality of texts and dictionaries. However, based on our observations, chemical names are difficult to cluster using string similarity measures such as the edit distance. To demonstrate this difficulty clearly, we compared the string similarities determined using the edit distance, the Monge-Elkan score, SoftTFIDF, and the bigram Dice coefficient for chemical names with those for other terms. The experimental results show that the discriminative application of string similarity methods to chemical and non-chemical names may be a simple but effective way to improve the performance of term clustering.

MOTIVATION: Many types of omics data are compiled as lists of connections between elements and visualized as networks or graphs where the nodes and edges correspond to the elements and the connections, respectively. However, these networks often appear as 'hair-balls'-with a large number of extremely tangled edges-and cannot be visually interpreted. RESULTS: We present an interactive, multiscale navigation method for biological networks. Our approach can automatically and rapidly abstract any portion of a large network of interest to an immediately interpretable extent. The method is based on an ultrafast graph clustering technique that abstracts networks of about 100 000 nodes in a second by iteratively grouping densely connected portions and a biological-property-based clustering technique that takes advantage of biological information often provided for biological entities (e.g. Gene Ontology terms). It was confirmed to be effective by applying it to real yeast protein network data, and would greatly help modern biologists faced with large, complicated networks in a similar manner to how Web mapping services enable interactive multiscale navigation of geographical maps (e.g. Google Maps). AVAILABILITY: Java implementation of our method, named NaviCluster, is available at http://navicluster.cb.k.u-tokyo.ac.jp/. CONTACT: [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

This article presents a novel approach to event extraction from biological text using Markov Logic. It can be described by three design decisions: (1) instead of building a pipeline using local classifiers, we design and learn a joint probabilistic model over events in a sentence; (2) instead of developing specific inference and learning algorithms for our joint model, we apply Markov Logic, a general purpose Statistical Relation Learning language, for this task; (3) we represent events as relations over the token indices of a sentence, as opposed to structures that relate event entities to gene or protein mentions. In this article, we extend our original work by providing an error analysis for binding events. Moreover, we investigate the impact of different loss functions to precision, recall and F-measure. Finally, we show how to extract events of different types that share the same event clue. This extension allowed us to improve our performance our performance even further, leading to the third best scores for task 1 (in close range to the second place) and the best results for task 2 with a 14% point margin.

MOTIVATION: The identification of putative ligand-binding sites on proteins is important for the prediction of protein function. Knowledge-based approaches using structure databases have become interesting, because of the recent increase in structural information. Approaches using binding motif information are particularly effective. However, they can only be applied to well-known ligands that frequently appear in the structure databases. RESULTS: We have developed a new method for predicting the binding sites of chemically diverse ligands, by using information about the interactions between fragments. The selection of the fragment size is important. If the fragments are too small, then the patterns derived from the binding motifs cannot be used, since they are many-body interactions, while using larger fragments limits the application to well-known ligands. In our method, we used the main and side chains for proteins, and three successive atoms for ligands, as fragments. After superposition of the fragments, our method builds the conformations of ligands and predicts the binding sites. As a result, our method could accurately predict the binding sites of chemically diverse ligands, even though the Protein Data Bank currently contains a large number of nucleotides. Moreover, a further evaluation for the unbound forms of proteins revealed that our building up procedure was robust to conformational changes induced by ligand binding. AVAILABILITY: Our method, named 'BUMBLE', is available at http://bumble.hgc.jp/. SUPPLEMENTARY INFORMATION: Supplementary Material is available at Bioinformatics online.

Figures in full science papers contain much information not described in the main text. The use of these figures is an important subject. In this study, we developed a method to classify these biomedical figures into categories using a combination of a bag of keypoints approach and a bag of words approach for the legends. For bag of keypoints, the descriptors of detected interest points are quantized by k-means clustering and are converted into a feature vector element. The figure classification is carried out using a one-against-all multi-class support vector machine. When the Harris-affine and extended scale-invariant feature transform method are used for interest point detection and feature description, respectively, the classification accuracy of bag of keypoints is about 20% better than that of field-level image descriptors which are similar to descriptors used in previous studies. Further, when bag of words for legends is combined with this method, the prediction performance achieved 75.7% classification accuracy. Introducing bag of keypoints not only increases the classification performance but also enables the figures to be treated as text words. This method is expected to be useful for figure similarity search and information retrieval across figures and text.

The network information on biological phenomena, such as incurable diseases, which were seldom known until now, was collected from more than 18,000,000 biomedical articles by a hybrid curation method which consisted of a machine curation by an information extraction system, GENPAC, and a manual curation by biologists. So far, 64 kinds of disease networks which comprise nodes of genes, proteins, chemicals, and biological phenomena, and edges of the interaction types, such as ldquoActivaterdquo and ldquoBindrdquo have been curated by the method. The information extraction of the networks shows high recall and precision rate, and furthermore, the disease networks information can be collected by the hybrid curation more efficiently than by only expert manual curation. It can be considered that the interaction and network information which is collected by the hybrid curation will be utilized for the functional annotation of gene clusters and discovering new subpathways on biological pathway maps.

Prevention of dementia is a crucial issue in this aged society. We propose coimagination method for prevention of dementia through supporting interactive communication with images. Coimagination method aims to activate three cognitive functions: episode memory, division of attention, and planning function, which decline at mild cognitive impairment (MCI). Participants of the coimagination program bring images according to the theme and communicate with them. The objective of the program is to make participants to focus on present and future rather than past, which is major difference between coimagination and reminiscence. We measure frequency of comments by others for each participant in order to evaluate interactivity of conversation. They take memory task whether they remember the owner or theme of images after the series of sessions. We held coimagination program successfully at the welfare institution for elderly people in Kashiwa city, Japan. Each session was held one hour per week for five times. The result of the task indicates that the participants showed empathy with each other. The effectiveness of the proposed method was validated through the experiment.

Biomedical papers contain large amounts of figures. Since they provide important information about research outcomes, mining techniques targeting them have attracted a great deal of attention. Our final goal is to develop a figure finding system, FigFinder, to retrieve figures relevant to a userpsilas query by mining information contained in figures, their legends, and the main text in an integrative manner. In this study, we worked on figure classification to choose those representing signaling or metabolic pathways, based on textual information contained in biomedical papers, as the first step to develop FigFinder. We took several supervised machine learning methods, and could confirm that the use of main text combined with figure legends was quite effective. Although many groups have considered figure legends, this is the first attempt to address figure classification task by utilizing figure legends together with main text to our knowledge.

We present an algorithm for on-line, incremental discovery of temporal-difference (TD) networks. The key contribution is the establishment of three criteria to expand a node in TD network: a node is expanded when the node is well-known, independent, and has a prediction error that requires further explanation. Since none of these criteria requires centralized calculation operations, they are easily computed in a parallel and distributed manner, and scalable for bigger problems compared to other discovery methods of predictive state representations. Through computer experiments, we demonstrate the empirical effectiveness of our algorithm.

This paper presents open brain simulator, which estimates the neural state of human through external measurement for the purpose of improving motor and social skills. Macroscopic anatomical nervous systems model was built which can be connected to the musculoskeletal model. Microscopic anatomical and physiological neural models were interfaced to the macroscopic model. Neural activities of somatosensory area and Purkinje cell were calculated from motion capture data. The simulator provides technical infrastructure for human biomechatronics, which is promising for the novel diagnosis of neurological disorders and their treatments through medication and movement therapy, and for motor learning support system supporting acquisition of motor skill considering neural mechanism.

BACKGROUND: Many online resources for the life sciences have been developed and introduced in peer-reviewed papers recently, ranging from databases and web applications to data-analysis software. Some have been introduced in special journal issues or websites with a search function, but others remain scattered throughout the Internet and in the published literature. The searchable resources on these sites are collected and maintained manually and are therefore of higher quality than automatically updated sites, but also require more time and effort. DESCRIPTION: We developed an online resource search system called OReFiL to address these issues. We developed a crawler to gather all of the web pages whose URLs appear in MEDLINE abstracts and full-text papers on the BioMed Central open-access journals. The URLs were extracted using regular expressions and rules based on our heuristic knowledge. We then indexed the online resources to facilitate their retrieval and comparison by researchers. Because every online resource has at least one PubMed ID, we can easily acquire its summary with Medical Subject Headings (MeSH) terms and confirm its credibility through reference to the corresponding PubMed entry. In addition, because OReFiL automatically extracts URLs and updates the index, minimal time and effort is needed to maintain the system. CONCLUSION: We developed OReFiL, a search system for online life science resources, which is freely available. The system's distinctive features include the ability to return up-to-date query-relevant online resources introduced in peer-reviewed papers; the ability to search using free words, MeSH terms, or author names; easy verification of each hit following links to the corresponding PubMed entry or to papers citing the URL through the search systems of BioMed Central, Scirus, HighWire Press, or Google Scholar; and quick confirmation of the existence of an online resource web page.

Abstract The amount of knowledge accumulated in published scientific papers has increased due to the continuing progress being made in scientific research. Since numerous papers have only reported fragments of scientific facts, there are possibilities for discovering new knowledge by connecting these facts. We therefore developed a system called BioTermNet to draft a conceptual network with hybrid methods of information extraction and information retrieval. Two concepts are regarded as related in this system if (a) their relationship is clearly described in MEDLINE abstracts or (b) they have distinctively co‐occurred in abstracts. PRIME data, including protein interactions and functions extracted by NLP techniques, are used in the former, and the Singhal‐measure for information retrieval is used in the latter. Relationships that are not clearly or directly described in an abstract can be extracted by connecting multiple concepts. To evaluate how well this system performs, Swanson's association between Raynaud's disease and fish oil and that between migraine and magnesium were tested with abstracts that had been published before the discovery of these associations. The result was that when start and end concepts were given, plausible and understandable intermediate concepts connecting them could be detected. When only the start concept was given, not only the focused concept (magnesium and fish oil) but also other probable concepts could be detected as related concept candidates. Finally, this system was applied to find diseases related to the BRCA1 gene. Some other new potentially related diseases were detected along with diseases whose relations to BRCA1 were already known. The BioTermNet is available at http://btn.ontology.ims.u‐tokyo.ac.jp.

MOTIVATION: With the rapid advancement of biomedical science and the development of high-throughput analysis methods, the extraction of various types of information from biomedical text has become critical. Since automatic functional annotations of genes are quite useful for interpreting large amounts of high-throughput data efficiently, the demand for automatic extraction of information related to gene functions from text has been increasing. RESULTS: We have developed a method for automatically extracting the biological process functions of genes/protein/families based on Gene Ontology (GO) from text using a shallow parser and sentence structure analysis techniques. When the gene/protein/family names and their functions are described in ACTOR (doer of action) and OBJECT (receiver of action) relationships, the corresponding GO-IDs are assigned to the genes/proteins/families. The gene/protein/family names are recognized using the gene/protein/family name dictionaries developed by our group. To achieve wide recognition of the gene/protein/family functions, we semi-automatically gather functional terms based on GO using co-occurrence, collocation similarities and rule-based techniques. A preliminary experiment demonstrated that our method has an estimated recall of 54-64% with a precision of 91-94% for actually described functions in abstracts. When applied to the PUBMED, it extracted over 190 000 gene-GO relationships and 150 000 family-GO relationships for major eukaryotes.

OBJECTIVE: To help biomedical researchers recognize dynamically introduced abbreviations in biomedical literature, such as gene and protein names, we have constructed a support system called ALICE (Abbreviation LIfter using Corpus-based Extraction). ALICE aims to extract all types of abbreviations with their expansions from a target paper on the fly. METHODS: ALICE extracts an abbreviation and its expansion from the literature by using heuristic pattern-matching rules. This system consists of three phases and potentially identifies valid 320 abbreviation-expansion patterns as combinations of the rules. RESULTS: It achieved 95% recall and 97% precision on randomly selected titles and abstracts from the MEDLINE database. CONCLUSION: ALICE extracted abbreviations and their expansions from the literature efficiently. The subtly compiled heuristics enabled it to extract abbreviations with high recall without significantly reducing precision. ALICE does not only facilitate recognition of an undefined abbreviation in a paper by constructing an abbreviation database or dictionary, but also makes biomedical literature retrieval more accurate. This system is freely available at http://uvdb3.hgc.jp/ALICE/ALICE_index.html.

Protein-protein interactions play a crucial role in the cellular process. Although recent studies have elucidated a huge amount of protein-protein interactions within Saccharomyces cerevisiae, many still remain to be identified. This paper presents a new interaction prediction method that associates domains and other protein features by using support vector machines (SVMs), and it reports the results of investigating the effect of those protein features on the prediction accuracy. Cross-validation tests revealed that the highest F-measure of 79%, was obtained by combining the features "domain, " "amino acid composition, " and "subcellular localization. " These prediction results were more accurate than the predictions reported previously. Furthermore, predicting the interaction of unknown protein pairs revealed that high-scoring protein pairs tend to share similar GO annotations in the biological process hierarchy. This method can be applied across species.

MOTIVATION: In eukaryotes, rules regarding organization of cis-regulatory elements are complex. They sometimes govern multiple kinds of elements and positional restrictions on elements. RESULTS: We propose a method for detecting rules, by which the order of elements is restricted. The order restriction is expressed as element patterns. We extract all the element patterns that occur in promoter regions of at least the specified number of genes. Then, we find significant patterns based on the expression similarity of genes with promoter regions containing each of the extracted patterns. When we applied our method to Saccharomyces cerevisiae, we detected significant patterns overlooked by previous methods, thus demonstrating the utility of our method for analyses of eukaryotic gene regulation. We also suggest that several types of element organization exist: (i) those in which only the order of elements is important, (ii) order and distance both are important and (iii) only the combination of elements is important. AVAILABILITY: The program for extracting element patterns is available upon request.

Abstract Summary: ‘Melina’ assists users to compare the results of four public softwares for DNA motif extraction in order to both confirm the reliability of each finding and avoid missing important motifs. It is also useful to optimize the sensitivity of software with a series of different parameter settings. Availability: Melina is available at http://www.hgc.ims.u-tokyo.ac.jp/Melina/ Contact: [email protected] * To whom correspondence should be addressed.

Abstract Motivations: Signal transduction is the common term used to define a diverse topic that encompasses a large body of knowledge about the biochemical mechanisms. Since most of the knowledge of signal transduction resides in scientific articles and is represented by texts in natural language or by diagrams, there is the need of a knowledge representation model for signal transduction pathways that can be as readily processed by a computer as it is easily understood by humans. Results: A signal transduction pathway representation model is presented. It is based on a compound graph structure and is designed to handle the diversity and hierarchical structure of pathways. A prototype knowledge base was implemented on a deductive database and a number of biological queries are demonstrated on it. Contact: [email protected]; [email protected] 2 Present address: Computational Biology Research Center (CBRC), NationalInstitute of Advanced Industrial Science and Technology (AIST) 2-41-6 Aomi, Koutou-ku, Tokyo 135-0064, Japan.

Abstract Motivation: To understand biological process, we must clarify how proteins interact with each other. However, since information about protein–protein interactions still exists primarily in the scientific literature, it is not accessible in a computer-readable format. Efficient processing of large amounts of interactions therefore needs an intelligent information extraction method. Our aim is to develop an efficient method for extracting information on protein–protein interaction from scientific literature. Results: We present a method for extracting information on protein–protein interactions from the scientific literature. This method, which employs only a protein name dictionary, surface clues on word patterns and simple part-of-speech rules, achieved high recall and precision rates for yeast (recall \batchmode \documentclass[fleqn,10pt,legalpaper]{article} \usepackage{amssymb} \usepackage{amsfonts} \usepackage{amsmath} \pagestyle{empty} \begin{document} \(=\) \end{document}86.8% and precision \batchmode \documentclass[fleqn,10pt,legalpaper]{article} \usepackage{amssymb} \usepackage{amsfonts} \usepackage{amsmath} \pagestyle{empty} \begin{document} \(=\) \end{document}94.3%) and Escherichia coli (recall \batchmode \documentclass[fleqn,10pt,legalpaper]{article} \usepackage{amssymb} \usepackage{amsfonts} \usepackage{amsmath} \pagestyle{empty} \begin{document} \(=\) \end{document}82.5% and precision \batchmode \documentclass[fleqn,10pt,legalpaper]{article} \usepackage{amssymb} \usepackage{amsfonts} \usepackage{amsmath} \pagestyle{empty} \begin{document} \(=\) \end{document}93.5%). The result of extraction suggests that our method should be applicable to any species for which a protein name dictionary is constructed. Availability: The program is available on request from the authors. Contact: [email protected] * To whom correspondence should be addressed.

MOTIVATION: Since their initial development, integration and construction of databases for molecular-level data have progressed. Though biological molecules are related to each other and form a complex system, the information is stored in the vast archives of the literature or in diverse databases. There is no unified naming convention for biological object, and biological terms may be ambiguous or polysemic. This makes the integration and interaction of databases difficult. In order to eliminate these problems, machine-readable natural language resources appear to be quite promising. We have developed a workbench for protein name abbreviation dictionary (PNAD) building. RESULTS: We have developed PNAD Construction Support System (PNAD-CSS), which offers various convenient facilities to decrease the construction costs of a protein name abbreviation dictionary of which entries are collected from abstracts in biomedical papers. The system allows the users to concentrate on higher level interpretation by removing some troublesome tasks, e.g. management of abstracts, extracting protein names and their abbreviations, and so on. To extract a pair of protein names and abbreviations, we have developed a hybrid system composed of the PROPER System and the PNAD System. The PNAD System can extract the pairs from parenthetical-paraphrases involved in protein names, the PROPER System identified these paris, with 98.95% precision, 95.56% recall and 97.58% complete precision. AVAILABILITY: PROPER System is freely available from http://www.hgc.inc.u-tokyo.ac.jp/service/tooldoc /KeX/intro.html. The other software are also available on request. Contact the authors. CONTACT: [email protected]

MOTIVATION: Precise analysis of the genetic network, gene function and transcription regulation requires accurate prediction of transcription factor (TF) bindability on DNA. For calculating the matching score between an input sequence and a set of known TF binding sites, we use positional weight matrices (PWMs) and Bucher's calculating method (Bucher, J. Mol. Biol., 212, 563-578, 1990). Since estimating TF binding sites requires cut-off values, we propose a robust cut-off value determining algorithm. RESULTS: We generalize the concept of local overrepresentation with statistics, and propose a new algorithm for determining the cut-off value using the background rate estimated on non-promoters. The algorithm iteratively determines parameters separating instances into phenomena-dependent and phenomena-independent subsets. Our system includes the method of re-estimating cut-off values of TFs that mis-recognize other TF preferred regions. Our data source comprised 433 non-redundant vertebrate promoters including viral promoters, from Eukaryotic Promoter Database (EPD) R.50. The method is applied to 205 vertebrate TFs that have frequency matrices in TRANSFAC Ver.3. 4 and the cut-off values of all of them can be determined. AVAILABILITY: The cut-off values and TF binding site predicting tool are available at http://www.hgc.ims.u-tokyo.ac. jp/service/tooldoc/TFBIND. We also provide the cut-off value estimating programs.

A deductive database system PACADE (Protein Atomic Coordinate Analyzer with Deductive Engine) has been developed for protein structure analysis. With this system, super-secondary structures described in logical and declarative rules can be retrieved effectively. For protein structure analysis, comparison of local structures in different proteins is a necessary mean. A function to search for similar structures has, therefore, been added to the PACADE system. We describe herein the result of searches for the same topological structures and three-dimensionally similar ones. A user of PACADE can select these two levels of similarity by changing parameters. This function enables the inference system to retrieve similar structures, according to the restraints of variables defined by the user. Similar super-secondary structures among proteins can be searched for automatically, which is useful for protein structure analysis. The retrieved similar super-secondary structures can serve as criteria for protein spatial alignment.

We have developed ODS (Overlapping Oligonucleotide Database for Signal Sequence Search)--the first relational database that integrates information on biological features into the search for signal sequences. In existing biological sequence databases, even relational ones, retrieving nucleotide sequences based on their biological features involves much labour and time or even the development of a new program. GenBank sequence data, including FEATURES records, are organized into three relational tables in ODS. Nucleotide sequences are transformed into overlapping oligonucleotides in order to facilitate the signal sequence search rapidly without the need for specific alignment programs. This transformation leads to a one-to-one correspondence between the nucleotide sequence and its biological feature. The signal sequence search by ODS is done in SQL queries and ODS obviates the need for molecular biologists to write computer programs. The application of ODS to searches of promoter regions revealed putative cis-acting elements and basic statistical analyses of occurrences of oligonucleotides showed interesting findings concerning the 'cg' dinucleotide.

We have developed a deductive database system PACADE for analyzing 3-D and secondary structures of protein. The PACADE system consists of a relational database created from Protein Data Bank and a deductive engine DEE based on logic programming. It has the following features: (1) The system has an inference mechanism. This means by which users can easily write and check biological hypotheses using logical and declarative rules instead of procedural programs. (2) The relational database of the PACADE system stores data on both 3-D and secondary structures of protein. The integration of this two level structure makes feasible an abstract representation of the protein structure. We describe herein the design, functions, and implementation of this PACADE system.