InformationTheLanguageofBiology.ppt

Conclusions There are strong parallels between language and biological data affording the development and use of common tools. Some language technology tools have been highly successful Other language technology tools are promising New field of computational sequence analysis (computational biolinguistics, sequence-to-structure technology, …) at intersection? DARPA-style program elements may be appropriate for bioinformatics programs. Annotation standards and tools Community evaluations on common data and tasks Technology transfer intrinsic to the program National challenge problems exist and can unify Federal support * Each class of colored box is extensible (region, content, signal) so signal = gene sequence region is the subset of the signal content is what you are saying about it Under user control: create arbitrary color post-its that you can attach and write on it * * * Sources: John Drake Jerry Hamilton Brian Witten 优秀精品课件文档资料 Information: The Language of Biology Gary Strong NSF, ITR Program Cell Human Language Suggestive Biology-Language Homologies Goals Leading Toward Predictive Biology Gene Sequence Data Gene Identification Protein Circuit Regulatory Network Discovery Biosimulation Structure Prediction Natural Language Processing and Bioinformatics are Already Related Both NL and biology are faced with data mining over massive amounts of data Applying NLP tools to biology Hidden Markov gene finders Protein grammars to predict function from sequence Protein circuit extraction from scientific literature Convergence of biological and language mining PSI Blast homology searching in genome augmented by medical literature Model-based GENSCAN Is Best Among HMM Gene Finders Sn = Sensitivity Sp = Specificity Ac = Approximate Correlation ME = Missing Exons WE = Wrong Exons GENSCAN Performance Data, /Accuracy.html The Chomsky Hierarchy Regular Languages Context- Free Languages Context- Sensitive Languages Recursively Enumerable Languages Language Automaton