Abstract Motivation: We present a novel algorithm, MaMF, for identifying transcription factor (TF) binding site motifs. The method is deterministic and depends on an indexing technique to optimize the search process. On common yeast datasets, MaMF performs competitively with other methods. We also present results on a challenging group of eight sets of human genes known to be responsive to a diverse group of TFs. In every case, MaMF finds the annotated motif among the top scoring putative motifs. We compared MaMF against other motif finders on a larger human group of 21 gene sets and found that MaMF performs better than other algorithms. We analyzed the remaining high scoring motifs and show that many correspond to other TFs that are known to co-occur with the annotated TF motifs. The significant and frequent presence of co-occurring transcription factor binding sites explains in part the difficulty of human motif finding. MaMF is a very fast algorithm, suitable for application to large numbers of interesting gene sets. Availability: The software is available for academic research use free of charge by email request. Contact: [email protected] Supplemental information: Data comprising the benchmarks used in the paper may be downloaded from .

MOTIVATION: We present a system, QPACA (Quantitative Pathway Analysis in Cancer) for analysis of biological data in the context of pathways. QPACA supports data visualization and both fine- and coarse-grained specifications, but, more importantly, addresses the problems of pathway recognition and pathway augmentation. RESULTS: Given a set of genes hypothesized to be part of a pathway or a coordinated process, QPACA is able to reliably distinguish true pathways from non-pathways using microarray expression data. Relying on the observation that only some of the experiments within a dataset are relevant to a specific biochemical pathway, QPACA automates selection of this subset using an optimization procedure. We present data on all human and yeast pathways found in the KEGG pathway database. In 117 out of 191 cases (61%), QPACA was able to correctly identify these positive cases as bona fide pathways with p-values measured using rigorous permutation analysis. Success in recognizing pathways was dependent on pathway size, with the largest quartile of pathways yielding 83% success. In cross-validation tests of pathway membership prediction, QPACA was able to yield enrichments for predicted pathway genes over random genes at rates of 2-fold or better the majority of the time, with rates of 10-fold or better 10-20% of the time. AVAILABILITY: The software is available for academic research use free of charge by email request. SUPPLEMENTARY INFORMATION: Data used in the paper may be downloaded from http://www.jainlab.org/downloads.html

MOTIVATION: The last few years have seen the development of DNA microarray technology that allows simultaneous measurement of the expression levels of thousands of genes. While many methods have been developed to analyze such data, most have been visualization-based. Methods that yield quantitative conclusions have been diverse and complex. RESULTS: We present two straightforward methods for identifying specific genes whose expression is linked with a phenotype or outcome variable as well as for systematically predicting sample class membership: (1) a conservative, permutation-based approach to identifying differentially expressed genes; (2) an augmentation of K-nearest-neighbor pattern classification. Our analyses replicate the quantitative conclusions of Golub et al. (1999; Science, 286, 531-537) on leukemia data, with better classification results, using far simpler methods. With the breast tumor data of Perou et al. (2000; Nature, 406, 747-752), the methods lend rigorous quantitative support to the conclusions of the original paper. In the case of the lymphoma data in Alizadeh et al. (2000; Nature, 403, 503-511), our analyses only partially support the conclusions of the original authors. AVAILABILITY: The software and supplementary information are available freely to researchers at academic and non-profit institutions at http://cc.ucsf.edu/jain/public

The authors present PARSEC-a system for generating connectionist parsing networks from example parses. PARSEC is not based on formal grammar systems and has been geared towards spoken language tasks. PARSEC networks exhibit three strengths important for application to speech processing: they learn to parse, and generalize well compared to hand-coded grammars; they tolerate several types of noise; and they can learn to use multimodal input. The authors also present the PARSEC architecture, its training algorithms, and performance analyses along several dimensions that demonstrate PARSEC's features. They compare PARSEC's performance to that of traditional grammar-based parsing systems.>

The authors present JANUS, a speech-to-speech translation system that utilizes diverse processing strategies including dynamic programming, stochastic techniques, connectionist learning, and traditional AI knowledge representation approaches. JANUS translates continuously spoken English utterances into Japanese and German speech utterances. The overall system performance on a corpus of conference registration conversations is 87%. Two versions of JANUS are compared: one using a LR parser (JANUS 1) and one using a connectionist parser (JANUS 2). Performance results were mixed, with JANUS 1 deriving benefit from a tighter language model and JANUS 2 benefitting from greater flexibility.>

A modular, recurrent connectionist network is taught to incrementally parse complex sentences. From input presented one word at a time, the network learns to do semantic role assignment, noun phrase attachment, and clause structure recognition, for sentences with both active and passive constructions and center-embedded clauses. The network makes syntactic and semantic predictions at every step. Previous predictions are revised as expectations are confirmed or violated with the arrival of new information. The network induces its own "grammar rules" for dynamically transforming an input sequence of words into a syntactic/semantic interpretation. The network generalizes well and is tolerant of ill-formed inputs.

A modular, recurrent connectionist network architecture which learns to robustly perform incremental parsing of complex sentences is presented. From sequential input, one word at a time, the networks learn to do semantic role assignment, noun phrase attachment, and clause structure recognition for sentences with passive constructions and center embedded clauses. The networks make syntactic and semantic predictions at every point in time, and previous predictions are revised as expectations are affirmed or violated with the arrival of new information. The networks induce their own grammar rules for dynamically transforming an input sequence of words into a syntactic/semantic interpretation. These networks generalize and display tolerance to input which has been corrupted in ways common in spoken language.>