MOTIVATION: Understanding life cannot be accomplished without making full use of biological data, which are scattered across databases of diverse categories in life sciences. To connect such data seamlessly, identifier (ID) conversion plays a key role. However, existing ID conversion services have disadvantages, such as covering only a limited range of biological categories of databases, not keeping up with the updates of the original databases and outputs being hard to interpret in the context of biological relations, especially when converting IDs in multiple steps. RESULTS: TogoID is an ID conversion service implementing unique features with an intuitive web interface and an application programming interface (API) for programmatic access. TogoID currently supports 65 datasets covering various biological categories. TogoID users can perform exploratory multistep conversions to find a path among IDs. To guide the interpretation of biological meanings in the conversions, we crafted an ontology that defines the semantics of the dataset relations. AVAILABILITY AND IMPLEMENTATION: The TogoID service is freely available on the TogoID website (https://togoid.dbcls.jp/) and the API is also provided to allow programmatic access. To encourage developers to add new dataset pairs, the system stores the configurations of pairs at the GitHub repository (https://github.com/togoid/togoid-config) and accepts the request of additional pairs. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

In the cases when public health authorities confirm patients with highly contagious diseases, they release summaries about patient locations and travel information to alert the public. Due to privacy concerns, these releases do not include the detailed data, and typically comprise the information about commercial facilities and public transportation used by the patients. This paper proposes a framework to represent the information in Resource Description Framework (RDF). The open data would then be used by residents for automated estimation of the potential risks of contacts, combined with the location information stored in their mobile phones.

We propose a prediction method for lane changes in other vehicles. According to previous research, over 90 % of car crashes are caused by human mistakes, and lane changes are the main factor. Therefore, if an intelligent system can predict a lane change and alarm a driver before another vehicle crosses the center line, this can contribute to reducing the accident rate. The main contribution of this work is to propose a new feature describing the relationship of a vehicle to adjacent vehicles. We represent the new feature using a dynamic characteristic potential field that changes the distribution depending on the relative number of adjacent vehicles. The new feature addresses numerous situations in which lane changes are made. Adding the new feature can be expected to improve prediction performance. We trained the prediction model and evaluated the performance using a real traffic dataset with over 900 lane changes, and we confirmed that the proposed method outperforms previous methods in terms of both accuracy and prediction time.

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.

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.

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.

We propose a new subclass of two-dimensional isometric array grammar, called Uniquely Parsable Array Grammar (UPAG). UPAG is a grammar class such that when we parse (i.e. apply rewriting rules reversely to) a given two-dimensional word, the rewritable portions of the word do not overlap each other and the rule that is applicable to each portion is uniquely determined. Thus parsing can be done in a deterministic manner. We study basic properties of UPAG such as unique parsability and parallel parsability. Then we define Monotone Terminating UPAG (MTUPAG), a subclass of UPAG, and show that any two-dimensional word generated by MTUPAG can be parsed in linear time. We also show several examples of MTUPAGs which generate interesting sets of geometrical patterns.

Two kinds of three-way isometric array grammars arc proposed as subclasses of an isometric monotonic array grammar. They are a three-way horizontally context-sensitive array grammar (3HCSAG) and a three-way immediately terminating array grammar (3ITAG). In these three-way grammars, patterns of symbols can grow only in the leftward, rightward and downward directions. We show that their generating abilities of rectangular languages are precisely characterized by some kinds of three-way two-dimensional Turing machines or related acceptors. In this paper. the following results are proved. First, 3HCSAG is characterized by a nondeterministic two-dimensional three-way Turing machine with space-bound n (n is the width of a rectangular input) and a nondeterministic one-way parallel/sequential array acceptor. Second, 3ITAG is characterized by a nondeterministic two-dimensional three-way real-time (or linear-time) restricted Turing machine, a nondeterministic one-dimensional bounded cellular acceptor and a nondeterministic two-dimensional one-line tessellation acceptor.

Regular array grammars (RAGs) are the lowest subclass in the Chomsky-like hierarchy of isometric array grammars. The left-hand side of each rewriting rule of RAGs has one nonterminal symbol and at most one "#" (a blank symbol). Therefore, the rewriting rules cannot sense contexts of non-# symbols. However, they can sense # as a kind of context. In this paper, we investigate this #-sensing ability. and study the language generating power of RAGs. Making good use of this ability, We show a method for RAGs to sense the contexts of local shapes of a host array in a derivation. Using this method, we give RAGs which generate the sets of all solid upright rectangles and all solid squares. On the other hand. it is proved that there is no context-free array grammar (and thus no RAG) which generates the set of all hollow upright rectangles.