Predicting the function of a protein from its sequence is a long-standing goal of bioinformatic research. While sequence similarity is the most popular tool used for this purpose, sequence motifs may also subserve this goal. Here we develop a motif-based method consisting of applying an unsupervised motif extraction algorithm (MEX) to all enzyme sequences, and filtering the results by the four-level classification hierarchy of the Enzyme Commission (EC). The resulting motifs serve as specific peptides (SPs), appearing on single branches of the EC. In contrast to previous motif-based methods, the new method does not require any preprocessing by multiple sequence alignment, nor does it rely on over-representation of motifs within EC branches. The SPs obtained comprise on average 8.4 +/- 4.5 amino acids, and specify the functions of 93% of all enzymes, which is much higher than the coverage of 63% provided by ProSite motifs. The SP classification thus compares favorably with previous function annotation methods and successfully demonstrates an added value in extreme cases where sequence similarity fails. Interestingly, SPs cover most of the annotated active and binding site amino acids, and occur in active-site neighboring 3-D pockets in a highly statistically significant manner. The latter are assumed to have strong biological relevance to the activity of the enzyme. Further filtering of SPs by biological functional annotations results in reduced small subsets of SPs that possess very large enzyme coverage. Overall, SPs both form a very useful tool for enzyme functional classification and bear responsibility for the catalytic biological function carried out by enzymes.

We describe a pattern acquisition algorithm that learns, in an unsuper- vised fashion, a streamlined representation of linguistic structures from a plain natural-language corpus. This paper addresses the issues of learn- ing structured knowledge from a large-scale natural language data set, and of generalization to unseen text. The implemented algorithm repre- sents sentences as paths on a graph whose vertices are words (or parts of words). Significant patterns, determined by recursive context-sensitive statistical inference, form new vertices. Linguistic constructions are rep- resented by trees composed of significant patterns and their associated equivalence classes. An input module allows the algorithm to be sub- jected to a standard test of English as a Second Language (ESL) profi- ciency. The results are encouraging: the model attains a level of per- formance considered to be “intermediate” for 9th-grade students, de- spite having been trained on a corpus (CHILDES) containing transcribed speech of parents directed to small children.

The distributional principle according to which morphemes that occur in identical contexts belong, in some sense, to the same category [1] has been advanced as a means for extracting syntactic structures from corpus data. We extend this principle by applying it recursively, and by us- ing mutual information for estimating category coherence. The resulting model learns, in an unsupervised fashion, highly structured, distributed representations of syntactic knowledge from corpora. It also exhibits promising behavior in tasks usually thought to require representations anchored in a grammar, such as systematicity.

Keyword-based search engines are in widespread use today as a popular means for Web-based information retrieval. Although such systems seem deceptively simple, a considerable amount of skill is required in order to satisfy non-trivial information needs. This paper presents a new conceptual paradigm for performing search in context, that largely automates the search process, providing even non-professional users with highly relevant results. This paradigm is implemented in practice in the IntelliZap system, where search is initiated from a text query marked by the user in a document she views, and is guided by the text surrounding the marked query in that document ("the context"). The context-driven information retrieval process involves semantic keyword extraction and clustering to automatically generate new, augmented queries. The latter are submitted to a host of general and domain-specific search engines. Search results are then semantically reranked, using context. Experimental results testify that using context to guide search, effectively offers even inexperienced users an advanced search tool on the Web.

Article Share on Placing search in context: the concept revisited Authors: Lev Finkelstein Zapper Technologies Inc., 3 Azrieli Center, Tel Aviv 67023, Israel Zapper Technologies Inc., 3 Azrieli Center, Tel Aviv 67023, IsraelView Profile , Evgeniy Gabrilovich Zapper Technologies Inc., 3 Azrieli Center, Tel Aviv 67023, Israel Zapper Technologies Inc., 3 Azrieli Center, Tel Aviv 67023, IsraelView Profile , Yossi Matias Zapper Technologies Inc., 3 Azrieli Center, Tel Aviv 67023, Israel Zapper Technologies Inc., 3 Azrieli Center, Tel Aviv 67023, IsraelView Profile , Ehud Rivlin Zapper Technologies Inc., 3 Azrieli Center, Tel Aviv 67023, Israel Zapper Technologies Inc., 3 Azrieli Center, Tel Aviv 67023, IsraelView Profile , Zach Solan Zapper Technologies Inc., 3 Azrieli Center, Tel Aviv 67023, Israel Zapper Technologies Inc., 3 Azrieli Center, Tel Aviv 67023, IsraelView Profile , Gadi Wolfman Zapper Technologies Inc., 3 Azrieli Center, Tel Aviv 67023, Israel Zapper Technologies Inc., 3 Azrieli Center, Tel Aviv 67023, IsraelView Profile , Eytan Ruppin Zapper Technologies Inc., 3 Azrieli Center, Tel Aviv 67023, Israel Zapper Technologies Inc., 3 Azrieli Center, Tel Aviv 67023, IsraelView Profile Authors Info & Claims WWW '01: Proceedings of the 10th international conference on World Wide WebMay 2001 Pages 406–414https://doi.org/10.1145/371920.372094Online:01 April 2001Publication History 319citation2,268DownloadsMetricsTotal Citations319Total Downloads2,268Last 12 Months193Last 6 weeks26 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access