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At least 19 recordsLinked to original sources
PubMed2004

Protein names precisely peeled off free text.

MOTIVATION: Automatically identifying protein names from the scientific literature is a pre-requisite for the increasing demand in data-mining this wealth of information. Existing approaches are based on dictionaries, rules and machine-learning. Here, we introduced a novel system that combines a pre-processing dictionary- and rule-based filtering step with several separately trained support vector machines (SVMs) to identify protein names in the MEDLINE abstracts. RESULTS: Our new tagging-system NLProt is capable of extracting protein names with a precision (accuracy) of 75% at a recall (coverage) of 76% after training on a corpus, which was used before by other groups and contains 200 annotated abstracts. For our estimate of sustained performance, we considered partially identified names as false positives. One important issue frequently ignored in the literature is the redundancy in evaluation sets. We suggested some guidelines for removing overly inadequate overlaps between training and testing sets. Applying these new guidelines, our program appeared to significantly out-perform other methods tagging protein names. NLProt was so successful due to the SVM-building blocks that succeeded in utilizing the local context of protein names in the scientific literature. We challenge that our system may constitute the most general and precise method for tagging protein names. AVAILABILITY: http://cubic.bioc.columbia.edu/services/nlprot/

Abstracting and Indexing↗
PubMed1990

Toxicological information series, II. A survey of toxicology information.

The structure of toxicology information is outlined. Key resources, both print and electronic, are described and the role of organizations is discussed on national and world levels. The section on computerized information covers systems such as TOXNET, CIS, and the ANSWER workstation.

Dictionaries, Chemical as Topic↗
PubMed1999

Dictionary building via unsupervised hierarchical motif discovery in the sequence space of natural proteins.

Using Teiresias, a pattern discovery method that identifies all motifs present in any given set of protein sequences without requiring alignment or explicit enumeration of the solution space, we have explored the GenPept sequence database and built a dictionary of all sequence patterns with two or more instances. The entries of this dictionary, henceforth named seqlets, cover 98.12% of all amino acid positions in the input database and in essence provide a comprehensive finite set of descriptors for protein sequence space. As such, seqlets can be effectively used to describe almost every naturally occurring protein. In fact, seqlets can be thought of as building blocks of protein molecules that are a necessary (but not sufficient) condition for function or family equivalence memberships. Thus, seqlets can either define conserved family signatures or cut across molecular families and previously undetected sequence signals deriving from functional convergence. Moreover, we show that seqlets also can capture structurally conserved motifs. The availability of a dictionary of seqlets that has been derived in such an unsupervised, hierarchical manner is generating new opportunities for addressing problems that range from reliable classification and the correlation of sequence fragments with functional categories to faster and sensitive engines for homology searches, evolutionary studies, and protein structure prediction.

Amino Acid Motifs↗
PubMed1999

Homonyms and synonyms in the Dictionary of Interfaces in Proteins (DIP).

MOTIVATION: Should reports on molecular mimicry in particular cases, e.g. responsible for cross-reactivity, be considered as accidental or as a general principle in protein evolution? To answer this question, two types of similarity have to be considered: those in homologues (synonyms) and resemblance between patches from unrelated proteins (homonyms). RESULTS: All interfaces from known protein structures were collected in a comprehensive data bank [Dictionary of Interfaces in Proteins (DIP)]. A fast, sequence-independent, three-dimensional superposition procedure was developed to search automatically for geometrically similar surface areas. Surprisingly, we found a large number of structurally similar interfaces on the surface of unrelated proteins. Even patches from different types of secondary structure were found resembling each other. The putative functional meaning of homonyms is demonstrated with striking examples.

Algorithms↗
PubMed2007

MinSet: a general approach to derive maximally representative database subsets by using fragment dictionaries and its application to the SCOP database.

MOTIVATION: The size of current protein databases is a challenge for many Bioinformatics applications, both in terms of processing speed and information redundancy. It may be therefore desirable to efficiently reduce the database of interest to a maximally representative subset. RESULTS: The MinSet method employs a combination of a Suffix Tree and a Genetic Algorithm for the generation, selection and assessment of database subsets. The approach is generally applicable to any type of string-encoded data, allowing for a drastic reduction of the database size whilst retaining most of the information contained in the original set. We demonstrate the performance of the method on a database of protein domain structures encoded as strings. We used the SCOP40 domain database by translating protein structures into character strings by means of a structural alphabet and by extracting optimized subsets according to an entropy score that is based on a constant-length fragment dictionary. Therefore, optimized subsets are maximally representative for the distribution and range of local structures. Subsets containing only 10% of the SCOP structure classes show a coverage of >90% for fragments of length 1-4. AVAILABILITY: http://mathbio.nimr.mrc.ac.uk/~jkleinj/MinSet. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Algorithms↗
PubMed2002

Dictionary-driven protein annotation.

Computational methods seeking to automatically determine the properties (functional, structural, physicochemical, etc.) of a protein directly from the sequence have long been the focus of numerous research groups. With the advent of advanced sequencing methods and systems, the number of amino acid sequences that are being deposited in the public databases has been increasing steadily. This has in turn generated a renewed demand for automated approaches that can annotate individual sequences and complete genomes quickly, exhaustively and objectively. In this paper, we present one such approach that is centered around and exploits the Bio-Dictionary, a collection of amino acid patterns that completely covers the natural sequence space and can capture functional and structural signals that have been reused during evolution, within and across protein families. Our annotation approach also makes use of a weighted, position-specific scoring scheme that is unaffected by the over-representation of well-conserved proteins and protein fragments in the databases used. For a given query sequence, the method permits one to determine, in a single pass, the following: local and global similarities between the query and any protein already present in a public database; the likeness of the query to all available archaeal/ bacterial/eukaryotic/viral sequences in the database as a function of amino acid position within the query; the character of secondary structure of the query as a function of amino acid position within the query; the cytoplasmic, transmembrane or extracellular behavior of the query; the nature and position of binding domains, active sites, post-translationally modified sites, signal peptides, etc. In terms of performance, the proposed method is exhaustive, objective and allows for the rapid annotation of individual sequences and full genomes. Annotation examples are presented and discussed in Results, including individual queries and complete genomes that were released publicly after we built the Bio-Dictionary that is used in our experiments. Finally, we have computed the annotations of more than 70 complete genomes and made them available on the World Wide Web at http://cbcsrv.watson.ibm.com/Annotations/.

Algorithms↗
PubMed2004

[The chemical entries of Charles Gerhardt in Dictionnaire universel des Sciences, des Lettres et des Arts by Marie-Nicolas Bouillet].

Charles Gerhardt (1816-1856) is known as the founder of modern organic chemistry. He wrote the chemical entries of the dictionary managed by Marie-Nicolas Bouillet (1798-1864), who was a philosopher. This great deal of work was done between 1849 and 1853. It accounts for didactic and militant purposes of Gerhardt. The whole entries set up a true treatise of chemistry, and reflect the synthetic mind of their author.

Chemistry↗
PubMed2004

[Chemistry dictionary of Pierre-Joseph Macquer].

In 1976, Pierre-Joseph Macquer publishes the first edition of his Chemistry dictionary, the first concerning this science. A second edition with many additions has been printed in 1778, bearing in mind the new concepts borned with the chemistry evolution. All chemists are unanimous to salute this monumental and precious work. These different aspects are presented in this Note, near the examination of the exemplary of this dictionary annotated by Jean Hermann, alsatian doctor and naturalist contemporary of Macquer.

Chemistry↗
PubMedDate not supplied

META. 2. A dictionary model of mammalian xenobiotic metabolism.

META is a new knowledge-based expert system that provides computer simulation of the biotransformation of chemicals. The program is based on the recognition of key functional groups within the complete chemical structure and therefore can predict the metabolites of new xenobiotics. Here, we describe a comprehensive knowledge base built for the purposes of modeling mammalian metabolism with META methodology.

Animals↗
PubMed2004

REFMAC5 dictionary: organization of prior chemical knowledge and guidelines for its use.

One of the most important aspects of macromolecular structure refinement is the use of prior chemical knowledge. Bond lengths, bond angles and other chemical properties are used in restrained refinement as subsidiary conditions. This contribution describes the organization and some aspects of the use of the flexible and human/machine-readable dictionary of prior chemical knowledge used by the maximum-likelihood macromolecular-refinement program REFMAC5. The dictionary stores information about monomers which represent the constitutive building blocks of biological macromolecules (amino acids, nucleic acids and saccharides) and about numerous organic/inorganic compounds commonly found in macromolecular crystallography. It also describes the modifications the building blocks undergo as a result of chemical reactions and the links required for polymer formation. More than 2000 monomer entries, 100 modification entries and 200 link entries are currently available. Algorithms and tools for updating and adding new entries to the dictionary have also been developed and are presented here. In many cases, the REFMAC5 dictionary allows entirely automatic generation of restraints within REFMAC5 refinement runs.

Chemical Phenomena↗
PubMed2004

The U.S. National Library of Medicine's Toxicology and Environmental Health Information Program.

For nearly 40 years, the National Library of Medicine's (NLM) Toxicology and Environmental Health Information Program (TEHIP) has been a significant leader in organizing and providing public access to an extensive storehouse of toxicological information through its online databases. With the advent of the Internet, TEHIP has expanded its role to also serve as a pre-eminent portal to toxicological information worldwide. Its primary databases reside within the web-based TOXNET system, and include the scientifically peer-reviewed Hazardous Substances Data Bank (HSDB), the U.S. Environmental Protection Agency's Integrated Risk Information System (IRIS) and Toxics Release Inventory, the National Cancer Institute's Chemical Carcinogenesis Research Information System (CCRIS) and the TOXLINE file of over 3 million bibliographic references. TEHIP's ChemIDplus is an extensive chemical dictionary that extends beyond simple nomenclature to offer displays of molecular structures and links from particular chemicals to other databases containing more information. Specialty files in occupational safety and health, and household products have recently been added to TEHIP's suite of resources. Additional databases in risk assessment, drugs, toxicology education, and global resources, are under development. "Special Topics" pages lead users to structured summaries and links in areas such as arsenic, chemical warfare agents, biological warfare, and West Nile Virus. A database on alternatives to the use of live animals, a three-module toxicology tutor, and a glossary of terms in toxicology are among TEHIP's other information aids, as well an increasing commitment to serving consumers, as witnessed by the animated ToxTown program. Outside the sphere of TEHIP, NLM offers additional databases, such as PubMed, of significant value to toxicology researchers.

Animals↗
PubMed1994

[Japanese medicines studied by Hepburn, an American missionary, in 1860s].

In the last days of the Tokugawa shogunate when Japan was opened its door to trade, J.C. Hepburn came to Japan, and he contributed to the modernization of Japan as a missionary, a doctor and an instructor. His great academic achievement was publishing a Japanese-English and English-Japanese dictionary. The dictionary contained words which were generally used in those days. It is said that it was way of living and culture in Japan as seen by an American. Words about medicine show the conditions of the business of medicine in those days. Until 1889 a pharmacist was not professionalized yet, nor was the separation of dispensary from medical practice effective. As most of doctors were Chinese herb doctors, there was not the word "MD." As Chinese doctors were the leading ones, lots of words about herb were mentioned. We can find only a few words about chemicals. The common people used patent medicine such as Daranisuke, Mankintan. Less than 100 years, sorts of medicine in Japan have changed to those of medicine in Western countries.

Dictionaries as Topic↗