1,721,150 research outputs found
A software framework for microarray and gene expression object model (MAGE-OM) array design annotation
Full text linkAbstract Background The MIAME and MAGE-OM standards defined by the MGED society provide a specification and implementation of a software infrastructure to facilitate the submission and sharing of data from microarray studies via public repositories. However, although the MAGE object model is flexible enough to support different annotation strategies, the annotation of array descriptions can be complex. Results We have developed a graphical Java-based application (Adamant) to assist with submission of Microarray designs to public repositories. Output of the application is fully compliant with the standards prescribed by the various public data repositories. Conclusion Adamant will allow researchers to annotate and submit their own array designs to public repositories without requiring programming expertise, knowledge of the MAGE-OM or XML. The application has been used to submit a number of ArrayDesigns to the Array Express database.</p
Faculty Opinions recommendation of Tempo and mode of genome evolution in a 50,000-generation experiment.
No full textFaculty Opinions recommendation of A model for the generation and transmission of variations in evolution.
No full textMinimum Information About a Microarray Experiment (MIAME) – Successes, Failures, Challenges
No full textThe Minimum Information About a Microarray Experiment (known as MIAME) guidelines describe information that needs to be provided to enable the interpretation of the results of a microarray-based experiment unambiguously. The MIAME guidelines were developed by the Microarray Gene Expression Data (MGED) Society. Since the MIAME position paper was published in 2001, it has been cited in the scientific literature well over a thousand times. MIAME has been replicated for many other technologies, the major data repositories are supporting MIAME, and most scientific journals have adopted MIAME guidelines as a requirement for publishing. With the advent of new-generation sequencing technology, MIAME faces new challenges. To address this, the MGED Society has proposed new guidelines, i.e., Minimum Information about a high-throughput SeQuencing Experiment (MINSEQE). Here we present analysis of the reasons for the success of MIAME, as well as discuss where it has failed, and the challenges it faces
Learning Regular Expressions by Pattern Matching
No full textWe are considering the problem of restoring regular expressions from representative examples. We describe a natural learning algorithm for obtaining a "plausible" regular expression from one example. The algorithm is based on finding the longest substring which can be matched by some part of the so far obtained expression. We believe that the algorithm to a certain extent mimics humans guessing regular expressions from the same sort of examples. We show that for regular expressions of bounded length successful learning takes time linear in the length of the example, provided that the example is "good". Under certain natural restrictions the run-time of the learning algorithm is polynomial also in unsuccessful cases. In the end we discuss the computer experiment of learning regular expressions via the described algorithm showing that the proposed learning method is quite practical. 1 Introduction Research into algorithms for recognizing periodic repetitions is an important part of mac..
Faculty Opinions recommendation of Local regulation of gene expression by lncRNA promoters, transcription and splicing.
No full textFaculty Opinions recommendation of Gene-specific correlation of RNA and protein levels in human cells and tissues.
No full text
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
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