Statistical Theory and Method Abstracts (STMA) is now available as a component of Zentralblatt MATH, referred to as STMA-Z. Zentralblatt MATH is the one of the world’s most complete and longest running abstracting and reviewing services in pure and applied mathematics, containing more than 2 million entries drawn from more than 2,300 serials and journals, covering the period from 1868 to the present. STMA-Z contains all entries of ZMATH pertaining to statistics. Former entries of STMA are included to avoid duplication. Entries of Zentralblatt are classified since 1972 according to the Mathematics Subject Classification Scheme. STMA-Z will provide subscribers with specific access to statistical references and related fields.
The Mobile Robot Programming Toolkit (MRPT) is an extensive, cross-platform, and open source C++ library aimed to help robotics researchers to design and implement algorithms (mainly) in the fields of Simultaneous Localization and Mapping (SLAM), computer
Design of Clinical Trials for Treatment of Pain, Development of Clinical Trials, Selected Qualitative Methods, Within-Patient Studies: Cross-over Trials & n-of-1 Studies, Clinical Economics, etc.
Lexical ambiguity is a fundamental problem in Information Retrieval (IR), especially in the medical domain. Many systems use a subset of the words contained in the document to represent the content, but they are faced with the problem of ambiguity.
Abstract
One of the major goals of computational sequence analysis is to find sequence similarities, which could serve as
evidence of structural and functional conservation, as well as of evolutionary relations among the sequences. Since
the degree of similarity is usually assessed by the sequence alignment score, it is necessary to know if a score is high
enough to indicate a biologically interesting alignment. A powerful approach to defining score cutoffs is based on the
evaluation of the statistical significance of alignments. The statistical significance of an alignment score is frequently
assessed by its P-value, which is the probability that this score or a higher one can occur simply by chance, given the
probabilistic models for the sequences. In this review we discuss the general role of P-value estimation in sequence
analysis, and give a description of theoretical methods and computational approaches to the estimation of statistical
signifiance for important classes of sequence analysis problems. In particular, we concentrate on the P-value estimation
techniques for single sequence studies (both score-based and score-free), global and local pairwise sequence
alignments, multiple alignments, sequence-to-profile alignments and alignments built with hidden Markov models.
We anticipate that the review will be useful both to. researchers professionally working in bioinformatics as well as
to biomedical scientists interested in using contemporary methods of DNA and protein sequence analysis.
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