Impact Factor: outdated artefact or stepping-stone to journal certification?

A review of Garfield's journal impact factor and its specific implementation as the Thomson Reuters Impact Factor reveals several weaknesses in this commonly-used indicator of journal standing. Key limitations include the mismatch between citing and cited documents, the deceptive display of three decimals that belies the real precision, and the absence of confidence intervals. These are minor issues that are easily amended and should be corrected, but more substantive improvements are needed. There are indications that the scientific community seeks and needs better certification of journal procedures to improve the quality of published science. Comprehensive certification of editorial and review procedures could help ensure adequate procedures to detect duplicate and fraudulent submissions.Comment: 25 pages, 12 figures, 6 table

Automated functional classification of experimental and predicted protein structures

BACKGROUND: Proteins that are similar in sequence or structure may perform different functions in nature. In such cases, function cannot be inferred from sequence or structural similarity. RESULTS: We analyzed experimental structures belonging to the Structural Classification of Proteins (SCOP) database and showed that about half of them belong to multi-functional fold families for which protein similarity alone is not adequate to assign function. We also analyzed predicted structures from the LiveBench and the PDB-CAFASP experiments and showed that accurate homology-based functional assignments cannot be achieved approximately one third of the time, when the protein is a member of a multi-functional fold family. We then conducted extended performance evaluation and comparisons on both experimental and predicted structures using our Functional Signatures from Structural Alignments (FSSA) algorithm that we previously developed to handle the problem of classifying proteins belonging to multi-functional fold families. CONCLUSION: The results indicate that the FSSA algorithm has better accuracy when compared to homology-based approaches for functional classification of both experimental and predicted protein structures, in part due to its use of local, as opposed to global, information for classifying function. The FSSA algorithm has also been implemented as a webserver and is available at