1,721,132 research outputs found
Comment on "Protein Sequences from Mastodon and Tyrannosaurus rex Revealed by Mass Spectrometry"
No full textPavel A. Pevzner, Sangtae Kim, Julio Ng (2008): Comment on "Protein sequences from mastodon and Tyrannosaurus rex revealed by mass spectrometry". Science 321: 1040, DOI: 10.1126/science.115500
HORmon
No full textData folder for manuscript:"HORmon: automated annotation of human centromeres"Olga Kunyavskaya, Tatiana Dvorkina, Andrey V. Bzikadze, Ivan Alexandrov, Pavel A. Pevzner</div
Research in Computational Molecular Biology, 10th Annual International Conference, RECOMB 2006, Venice, Italy, April 2-5, 2006, Proceedings Springer 2006
No full text
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe 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
Variations on the Author
Get PDF“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Get PDFWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Tyrannosaurus rex
No full textImagine a monkey typing random keys on a typewriter and let us assume that the monkey is given 100,000 attempts to generate six-letter words. One would be surprised if the monkey typed a six-letter word from Webster’ s dictionary on the first attempt; indeed, the probability of this is rather low. However, nobody would be surprised if some of the 100,000 words turned out to be correctly spelled English words. Now imagine a boy who watches the monkey and discovers that 7 out of 100,000 words are actually spelled correctly. The boy is so surprised that he writes a paper called “My monkey can spell!” and publishes it in a scientific journal. Some scientists are not convinced, and they request the list of all words the monkey generated in addition to the seven correctly spelled words. The boy does not understand the reason for such requests; indeed, if all other words are just junk, what is the point of asking for them? One often feels like a monkey (and a boy) when trying to interpret peptide mass spectra. Indeed, a randomly chosen spectrum can easily match a word in Webster (if English letters are interpreted as amino acids) or in any protein database. Scientists fail to interpret the lion’ s share of mass spectra generated worldwide, resulting in billions of uninterpreted or “junk” spectra. If we matched these junk spectra against Webster we would surely find that some of them spell English words. Unfortunately, we would not be able to publish a paper called “Mass spectrometers can spell!” because false protein identifications are unavoidable in the field of proteomics. Scientists learned how to cope with them by establishing the Proteomics Publication Guidelines that require authors to provide the error rates of their identifications. Asara et al. (1) reported the sequencing of proteins from 68-million-year-old T. rex fossils and established similarities between dinosaur and chicken genomes. The authors generated seven T. rex peptides by matching mass spectra against collagen proteins. They did not reveal all generated spectra and never specified exactly how many spectra were generated. Because there are false identifications in every mass spectrometry experiment, without addressing the statistical significance problem, the results of (1) are no more convincing than the first sensational report of dinosaur DNA published in Science more than a decade ago (2). In the spring of 2007, we notified Asara and Science of concerns about the statistical significance of some of the peptides. In a subsequent clarification letter (3), Asara et al. acknowledged some of the problems with their analysis in (1). In particular, they stated, “We have determined that one of the reported T. rex spectra for the peptide GLVGAPGLRGLPGK is statistically insignificant when searched against large protein databases....” (3, 4). By admitting this point, Asara et al. implicitly (and probably unknowingly) acknowledged a much bigger problem with their original study (1). Indeed, the statistical significance (e.g., false positive rate or FPR) is a number that needs to be computed, but Asara et al. (3) never described how they computed statistical significance, and it is not clear whether they tried. If they computed the statistical significance, they would discern that other T. rex peptides do not fare much better. For example, it turns out that there are thousands of peptides that match the fifth T. rex spectrum reported in (1, 3) even better than the alleged T. rex peptide GVVGLP*GQR [FPR or spectral probability equal to 1.3 × 10 −6 (5)]. This implies that if one tries to match this spectrum against a small database of 10 6 amino acids, there is a good chance of matching this spectrum simply by chance. Or, equivalently, if one tries to match 1000 arbitrary spectra of similar quality against an arbitrary database of 1000 amino acids, there is a good chance to find an interpretation that is even better than the alleged T. rex peptide GVVGLP*GQR. Department of Computer Science and Engineering, University of California, San Diego, 9500 Gilman Drive, Mail Code 0404, La Jolla, CA 92093–0404, USA. *To whom correspondence should be addressed. E-mail: [email protected] Asara et al. (3) must have generated at least hundreds of thousands of spectra, and their database is much larger than 1000 amino acids. This immediately characterizes the peptide GVVGLP*GQR as a statistical artifact,in addition to GLVGAPGLRGLPGK, which the authors acknowledge in (3). If Asara et al. (1) stand by the statistical significance argument given in (3), they should question all of the T. rex peptides identified in (1). Only one of these peptides was supported by chemical synthesis with a spectral correlation coefficient of 0.71, which although borderline significant, may also represent homeometric (6), but not identical, peptides. We argue that most of the peptides with GVVGLP*GQR-like spectra (e.g., 10,919 peptides with better InsPecT scores or 10,294 peptides with better X!Tandem scores than GVVGLP*GQR) would have produced spectra that are somewhat similar to the spectrum of GVVGLP*GQR, thus calling for more extensive synthesis-based verification of the results in (1). For example, one could potentially synthesize GVVGLP*GQR and discover that the resulting spectrum “looks like” one of the T. rex spectra, thus “proving” that GVVGLP*GQR is indeed a T. rex peptide. In this case, it is puzzling how Asara et al. selected the “correct” statistically insignificant peptide among hundreds of other statistically insignificant peptides. For example, peptides RVGLRAAR, RVGLPTKK, RVGP*PTKK, and thousands of others represent better InsPecTand X!Tandem spectral interpretations than GVVGLP*GQR (table S1) (supporting online material). If one is willing to argue that GVVGLP*GQR is a valid identification based on peptide synthesis, the peptides RVGLRAAR, RVGLPTKK, and RVGP*PTKK should also be synthesized and compared to the T. rex spectrum. Extraordinary science requires extraordinary proofs. Since the publication of their report (1), Asara et al. have reinterpreted (3) four out of seven of the T. rex peptides originally reported. The most likely outcome of further criticism is that Asara and colleagues will continue changing their original interpretations until the critics give up. So far, five out of six of the remaining significant T. rex peptides have already emerged as identical to chicken peptides. Maybe T. rex was a chicken after all? Recently, a group of 27 mass spectrometrists, bioinformaticians, and dinosaur experts published an insightful criticism of the T. rex protein analysis (7). Still, Asara and Schweitzer (8), refused to acknowledge the problems with their analysis. It is now the turn of the mass spectrometry community to question whether the monkey can actually spell. It is very easy to check; just ask the boy how many words (e.g., spectra) the monkey has generated and what tests of statistical significance were used to compute FPR. With this information in hand, the scientists can finally match all dinosaur proteins against Webster’ s dictionary to see whether mass spectrometers can spell and whether T. rex was a chicken.Published as part of Pavel A. Pevzner, Sangtae Kim & Julio Ng, 2008, Comment on " Protein sequences from mastodon and Tyrannosaurus rex revealed by mass spectrometry ", pp. 1040 in Science 321 on page 1040, DOI: 10.1126/science.1155006, http://zenodo.org/record/374604
Dispelling the Myths Behind First-author Citation Counts
Get PDFWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
- …
