Ppt on evolution and classification of computers

Introduction to Information Technology Turban, Rainer and Potter

John Wiley & Sons, Inc. Copyright 2005 Chapter 2 1 Information Technologies: Concepts and Management Chapter2 Information Technologies: Concepts and Management Chapter 2 1 Chapter Outline Information System: Concepts and Definition Evolution of Information System Classification of Information Systems Examples of Information Systems The Modern Computing Environment Web-based Systems Emerging Computing Environments Managing Information Resources Chapter 2 Learning Objectives Differentiate between information/


Adventures in Computational Enzymology John Mitchell.

is largely nucleophilic Protontransfer Ad N 2 E1 SN2SN2SN2SN2E2 RadicalreactionTautom.Others Repertoire of Enzyme Catalysis Residue Catalytic Propensities Evolution of Enzyme Function D.E. Almonacid et al., to be published Work with domains - evolutionary & structural units of proteins. Map enzyme catalytic mechanisms to domains to quantify convergent and divergent functional evolution of enzymes. Domains Functional Classification: EC Enzyme Commission (EC) Nomenclature, 1992, Academic Press, San Diego, 6/


OverviewKindergartenEvidence:First GradeEvidence:Second GradeEvidence: Counting and Cardinality Know number names and the count sequence. (K.CC.A) 1.Count.

years of evolution that has filled every available niche with life forms. Principles that Underlie the Concept and/or Skill: Relations to common ancestor: Current diverse species are related by descent from common ancestors. The millions of different species of plants, animals, and microorganisms that live on Earth today are related by descent from common ancestors. Principles that Underlie the Concept and/or Skill: Biological classification Biological classification is/


Computing & Information Sciences Kansas State University Wednesday, 02 Apr 2008 CIS 732 / 830: Machine Learning / Advanced Topics in AI Lecture 27 of 42.

mining tasks  Multi-dimensional on-line analysis of streams  Mining outliers and unusual patterns in stream data  Clustering data streams  Classification of stream data Computing & Information Sciences Kansas State University Wednesday, 02/2003)  Use tilted time window frame  Mining evolution and dramatic changes of frequent patterns Space-saving computation of frequent and top-k elements (Metwally, Agrawal, and El Abbadi, ICDT05) Computing & Information Sciences Kansas State University Wednesday, 02 /


UNIT-1 Introduction  Motivation: Why data mining?  What is data mining?  Data Mining: On what kind of data?  Data mining functionality  Classification.

or exception but is quite useful in fraud detection, rare events analysis Trend and evolution analysis – Trend and deviation: regression analysis – Sequential pattern mining, periodicity analysis – Similarity-based analysis 8/25/2015P.PramodKumar, Sr.Asst.Prof., Data Mining: Classification Schemes 8/25/2015P.PramodKumar, Sr.Asst.Prof., Data Mining: Confluence of Multiple Disciplines Data Mining Database Technology Statistics Other Disciplines Visualization Information Science MachineLearning 8/


Division of Population Health Sciences 1 Royal College of Surgeons in Ireland Coláiste Ríoga na Máinleá in Éirinn Computer-Based Clinical Decision Support.

–Comparative clinical data Division of Population Health Sciences 5 III) DSSs: definition and aspects - 1 Decision Support Systems (DSSs) - a class of computer-based information systems including knowledge-based systems that support decision-making activities: - parts/blocks: database/KB, model, user interface - components: inputs, user knowledge/expertise, outputs decisions - classifications: scope, relationships to user, mode of assistance, evolution, orientation Division of Population Health Sciences 6/


1 Unit – I Data Warehouse and Business Analysis What is Data Warehouse? Defined in many different ways, but not rigorously. A decision support database.

that does not comply with the general behavior of the data Noise or exception? Useful in fraud detection, rare events analysis Trend and evolution analysis Trend and deviation: e.g., regression analysis Sequential pattern mining/: update W multiplicatively 256 Classification by Backpropagation Backpropagation: A neural network learning algorithm Started by psychologists and neurobiologists to develop and test computational analogues of neurons A neural network: A set of connected input/output units where/


1 Chapter 1. Introduction Motivation: Why data mining? What is data mining? Data Mining: On what kind of data? Data mining functionality Major issues in.

that does not comply with the general behavior of the data Noise or exception? Useful in fraud detection, rare events analysis Trend and evolution analysis Trend and deviation: e.g., regression analysis Sequential pattern mining/: update W multiplicatively 256 Classification by Backpropagation Backpropagation: A neural network learning algorithm Started by psychologists and neurobiologists to develop and test computational analogues of neurons A neural network: A set of connected input/output units where/


Soft Computing Applications Pavel Krömer IT4 Knowledge Management.

(A. E. Villa, W. Duch, P. Érdi, F. Masulli, and G. Palm, eds.), vol. 7553 of Lecture Notes in Computer Science, pp. 132–139, Springer, 2012. + submitted/invited papers to journal special issues (Neurocomputing) Evolution of fuzzy predictors and classifiers for data mining Application of fuzzy IR principles and GP in data mining An evolution of query optimization algorithms Used for classification, function approximation, time series analysis Steel products quality estimation/


1 PDE Methods are Not Necessarily Level Set Methods Allen Tannenbaum Georgia Institute of Technology Emory University.

of Curve Evolutions 12 Classification of Curve Evolutions Kass, Witkin, Terzopoulos, "Snakes: Active Contour Models," International Journal of Computer Vision, pp. 321-331, 1988. 13 Classification of Curve Evolutions Terzopoulos, Szeliski, Active Vision, chapter Tracking with Kalman Snakes, pp. 3-20, MIT Press, 1992. 14 Classification of Curve Evolutions Kichenassamy, Kumar, Olver, Tannenbaum, Yezzi, "Conformal curvature flows: From phase transitions to active vision," Archive for Rational Mechanics and/


Biologically Inspired Computing: Introduction This is a lecture one of `Biologically Inspired Computing’ Contents: Course structure, Motivation for BIC,

it’s about 1—2 Intro to BIC The differences between BIC and `ordinary’ computing, the kinds of problems we need BIC for (including basics of classification, optimisation, and problem complexity), motivation for BIC, and a broad overview of many BIC techniques and the kinds of problems they can solve. 3—8 Evolutionary Algorithms Algorithms based on natural evolution, for solving real-world problems; various different algorithms based on this idea/


© Keith G Jeffery & Anne AssersonCERIF Course: Evolution 20021024 1 CERIF COURSE Session 6: Evolution Keith G Jeffery, Director, IT CLRC

, 1991 Ortelius Thesaurus on Higher Education, 1988 UNESCO International Standard Classification of Education ISCED, 1997 Swiss University Information System, Technical Handbook, 2001 Swiss National Science Foundation and ProClim Classification, 1996 © Keith G Jeffery & Anne AssersonCERIF Course: Evolution 20021024 57 Beat’s classification scheme CERIF91 Uneven distribution Sub-disciplines and specializations in History, Linguistics, Law and Medicine are over-represented Leads to hugh pick lists which are/


CENTER FOR BIOLOGICAL SEQUENCE ANALYSIS Multiple Alignment Anders Gorm Pedersen Molecular Evolution Group Center for Biological Sequence Analysis

performance to benchmark data sets for which 3D structures and structural alignments are available (BALiBASE, PREfab, SABmark, SMART). –Advantage: real, biological data with real characteristics –Problem: we only have good benchmark data for core regions, no good knowledge of how gappy regions really look Option 2: Construct synthetic alignments by letting a computer simulate evolution of a sequence along a phylogenetic treeOption 2: Construct synthetic/


UNIT - I Data Mining. UNIT - I Introduction : Fundamentals of data mining, Data Mining Functionalities, Classification of Data Mining systems, Major issues.

Patterns 3.Classification and Prediction 4.Cluster Analysis 5.Outlier Analysis 6.Evolution Analysis Data Mining Functionalities: Characterization and Discrimination Data can be associated with classes or concepts, it can be useful to describe individual classes or concepts in summarized, concise, and yet precise terms. For example, in the AllElectronics store, classes of items for sale include computers and printers, and concepts of customers include bigSpenders and budgetSpenders. Such descriptions of a/


Features and Algorithms Paper by: XIAOGUANG QI and BRIAN D. DAVISON Presentation by: Jason Bender.

information to build a more accurate classifier Evolution of Websites  Apple in 1998 Evolution of Websites  Apple 2008 Evolution of Websites  Nike in 2000 Evolution of Websites  Nike in 2008 Evolution of Websites  Yahoo in 1996 Evolution of Websites  Yahoo in 2008 Evolution of Websites  Microsoft in 1998 Evolution of Websites  Microsoft in 2008 Evolution of Websites  MTV in 1998 Evolution of Websites  MTV in 2008 Sources  Web Page Classification: Features and Algorithms by Xiaoguang Qi & Brian D/


Learning with Hypergraphs: Discovery of Higher-Order Interaction Patterns from High-Dimensional Data Moscow State University, Faculty of Computational.

Ai)Pg(Ai) [Zhang, CEC-99] Evolution as a Bayesian inference process Evolutionary computation (EC) is viewed as an iterative process of generating the individuals of ever higher posterior probabilities from the priors and the observed data. © 2007, SNU Biointelligence/ hsa-miR-183260877.3 hsa-miR-184260116.7 hsa-let-7a256313.8 Non-Biological Applications Digit Recognition Face Classification Text Classification Movie Title Prediction © 2007, SNU Biointelligence Lab, http://bi.snu.ac.kr/ 43 Digit Recognition: /


Neural and Evolutionary Computing - Lecture 1 1 Neural and Evolutionary Computing What is this course about ? Computational Intelligence Neural Computing.

the network adaptive parameters Learning variants: Supervised (with a teacher) Unsupervised (without a teacher) Reinforcement Neural and Evolutionary Computing - Lecture 1 21 ANN applications Classification –Supervised and unsupervised classification of data –Character/image/speech recognition Approximation –Estimate the relationship between different variables Prediction –Extract time series models from data Control –Nonlinear systems modelling Optimization –Electronic circuits design Signal analysis/


Classification and Novel Class Detection in Data Streams Classification and Novel Class Detection in Data Streams Mehedy Masud 1, Latifur Khan 1, Jing.

Detection in Data Streams Classification and Novel Class Detection in Data Streams Mehedy Masud 1, Latifur Khan 1, Jing Gao 2, Jiawei Han 2, and Bhavani Thuraisingham 1 1 Department of Computer Science, University of Texas at Dallas 2 Department of Computer Science, University of Illinois at Urbana Champaign This work was funded in part by Presentation Overview Stream Mining Background Novel Class Detection– Concept Evolution Data Streams Data streams/


Improving Text Classification by Shrinkage in a Hierarchy of Classes Andrew McCallum Just Research & CMU Tom Mitchell CMU Roni Rosenfeld CMU Andrew Y.

d is a document, w d i is the i th word of document d 5 “Shrinkage” / “Deleted Interpolation” [James and Stein, 1961] / [Jelinek and Mercer, 1980] (Uniform) MagnetismRelativity Physics EvolutionBotany Irrigation Crops BiologyAgriculture Science/botany evolution cell magnetism relativity courses agriculturebiologyphysicsCSspace 264 classes, 14k documents, 3 million words, 76k vocabulary... … (30) www.yahoo.com/Science... 15 Yahoo Science Classification Accuracy 16 Pruning the tree for computational efficiency/


Anastasia Nikolskaya PIR (Protein Information Resource), Georgetown University Medical Center FUNCTIONAL ANALYSIS OF PROTEIN SEQUENCES: ANNOTATION AND.

-length protein family classification based on evolution Highly annotated, optimized for annotation propagation Functional predictions for uncharacterized proteins Used to facilitate and standardize annotations in UniProt PIRSF Protein Classification System Functional Analysis of Protein Sequences: Homology-based (sequence analysis, structure analysis) Non-homology (genome context, phylogenetic distribution) 3 Proteomics and Bioinformatics Bioinformatics Computational analysis and integration of these data/


Giorgos FlourisOpen Data Tutorials, May 2013 1 Data and Knowledge Evolution Slides available at: Giorgos.

is the semantics of evolution and change?  How can I efficiently compute the ideal evolution result? Giorgos FlourisOpen Data Tutorials, May 2013 20 Evolution: Visualization Dataset Real World Evolution Algorithm Delete_Class(…) Pull_Up_Class(…) Rename_Class(…) … D0D0 D1D1 Giorgos FlourisOpen Data Tutorials, May 2013 21 Evolution: Summary  Evolution topics  Understanding the evolution challenges  Understanding the process of change — Balancing between philosophical and practical considerations  Cross/


Grammatical Evolution Neural Networks for Genetic Epidemiology Alison Motsinger-Reif, PhD Bioinformatics Research Center Department of Statistics North.

studies improves the power of Grammatical Evolution Neural Networks. Proceedings of the IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology 2007 pp. 1-8. Has been favorably compared to other methods in the field in a range of genetic models –Random Forests, Focused Interaction Testing Framework, Multifactor Dimensionality Reduction, Logistic Regression –Motsinger-Reif AA, Reif DM, Fanelli TJ, Ritchie MD. Comparison of computational approaches for genetic association/


January 14, 2016Data Mining: Concepts and Techniques 1 Chapter 4: Data Mining Primitives, Languages, and System Architectures Data mining primitives: What.

be mined, as this determines the data mining functions to be performed. The kinds of knowledge include concept description (characterization and discrimination), association, classification, predication, clustering, and evolution analysis. In addition to specifying the kind of knowledge to be mined for a given data mining task, the user can be more specific and provide pattern templates that all discovered patterns must match January 14, 2016Data Mining: Concepts/


1 樹木學及實習 Dendrology and Practice 植物型態 Plant Morphology 國立臺灣大學 森林環境暨資源學系 鍾國芳 (Kuo-Fang Chung) School of Forestry and Resource Conservation, National Taiwan.

F- X. elliptica A- X. alba B- X. lutea 0.00.51.01.52.0 34 Advantages and pitfalls of phenetic approaches Easy to operate Computationally fast  Most alike ≠ most closely related (parallel and convergent evolution; e.g. Euphorbia vs. cacti)  Aiming for objective, but difficult to achieve ‒ Selection of characters Phenetic approaches are now widely used in ecology 35 36 Phylogenetic Systematics/Cladistics ( 支序學 ) Willi Hennig/


صفحه قبلصفحه بعد نام درس : داده کاوي نام منبع : Data Mining: Concepts and Techniques نام مولفان : Jiawei Han, Micheline Kamber انتشارات : Morgan Kaufmann.

methods for classification and analysis of multivariate observations, in Proc. 5th Berkeley Symp. Mathematical Statistics and Probability, 1967. #12. BIRCH: Zhang, T., Ramakrishnan, R., and Livny, M. 1996. BIRCH: an efficient data clustering method for very large databases. In SIGMOD 96. Bagging and Boosting #13. AdaBoost: Freund, Y. and Schapire, R. E. 1997. A decision-theoretic generalization of on- line learning and an application to boosting. J. Comput. Syst. Sci/


February 13, 2016 Data Mining: Concepts and Techniques 1 1 Data Mining: Concepts and Techniques These slides have been adapted from Han, J., Kamber, M.,

Be Mined? Data Mining: On What Kind of data? Time and Ordering: Sequential Pattern, Trend and Evolution Analysis Structure and Network Analysis Evaluation of Knowledge Applications of Data Mining Major Challenges in Data Mining A Brief History of Data Mining and Data Mining Society Summary February 13, 2016 Data Mining: Concepts and Techniques 31 Applications of Data Mining Web page analysis: from web page classification, clustering to PageRank & HITS algorithms Collaborative analysis/


© Worboys and Duckham (2004) GIS: A Computing Perspective, Second Edition, CRC Press Chapter 1 Introduction.

hierarchy or taxonomic hierarchy? What is its level of detail? What is a GIS Data and databases Hardware support Functionality © Worboys and Duckham (2004) GIS: A Computing Perspective, Second Edition, CRC Press Classifications of events and processes: Mourelatos What is a GIS Data and databases Hardware support Functionality © Worboys and Duckham (2004) GIS: A Computing Perspective, Second Edition, CRC Press Classifications of events and processes: count nouns/mass nouns ContinuantsOccurrent Count noun/


Human Molecular Evolution Lecture 2 Molecular phylogenies and molecular clocks You can download a copy of these slides from www.stats.ox.ac.uk/~harding.

Human Molecular Evolution Lecture 2 Molecular phylogenies and molecular clocks You can download a copy of these slides from www.stats.ox.ac.uk/~harding Concepts and topics to be covered in this lecture Classification and Phylogeny Controversies in the phylogenetic systematics of primates Constructing phylogenetic trees Molecular clocks Neutral theory basis for a constant evolutionary rate given by the mutation rate. From classification to phylogeny Classification: grouping of taxa based/


New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U.

clinical value (classification concept or an administrative definition) 6 active /cut] Finding (finding)00195700000000000000 Clinical history and observation findings (finding)0016540 46 Clinical/compute the relevant information content of a version as the sum of the information contents of all SCs in that version. New York State Center of Excellence in Bioinformatics & Life Sciences R T U New York State Center of Excellence in Bioinformatics & Life Sciences R T U Hypothesis 1.the evolution of the information content of/


Estimating and using phylogenies

more slowly than birds since lineages separated Birds as separate class recognizes their rapid evolution = major unique derived traits Systematicists Still many polyphylectic groups Detect convergent evol. ==> change classification BUT favour retaining paraphyletic groups to underscore rapid evolution STABILITY of taxonomic system Future of Systematics Molecular genetics & powerful computers Fossil history - dating and derived vs ancestral traits Molecular = more traits than ever before Combining two lines/


Finding Order in Diversity

Diversity Classification Why do we need to classify? Imagine a store…..how do you know where to find the milk or the cereal? Are they in the same aisle? How is the store “organized”? Are all stores similar? Imagine your computer or mp3 player…..are all of your songs and files in a single folder or do you have them grouped in some way? Evolution/


Physics of stars Syllabus week topics

by watching a single tree for decades to see how it grew and aged. For stars, the observations are compared with computer models of how stars evolve, and used to refine those models. Interactive animation of star evolution i H-R diagram http://sunshine.chpc.utah.edu/labs/star_life/hr_interactive.html HR diagram – stellar evolution When stars with different mass but the same age as each other/


Visiting Scholar, Harvard University Technical Advisor, AFRL

where Now we show some equations describing what I just talked about. Each data sample consists of two items, the signature position in the focal plane ( eta_nm), and the corresponding vector of classification features computed during preprocessing (f_nm). The pdf, conditional on target k, consists of the product of the conditional pdfs for beta and f, which we model as Gaussian functions. Note that the function describing the/


Genesis of a theoretical framework

.orgs-evolution-knowledge.net/ Cognition (terms are meaningful in relation to autopoietic or artificially intelligent systems) Observation: Initial change induced within the autopoietic system by a perturbation Classification (/ decision): Process by which an induced change results in the system settling into one of alternative attractor basins on a landscape of potential gradients Meaning: The net result in the system due to the initial propagation and classification of/


Physics of stars Syllabus week theme

by watching a single tree for decades to see how it grew and aged. For stars, the observations are compared with computer models of how stars evolve, and used to refine those models. Interactive animation of star evolution i H-R diagram http://sunshine.chpc.utah.edu/labs/star_life/hr_interactive.html HR diagram – stellar evolution When stars with different mass but the same age as each other/


Biologically Inspired Computing: Introduction This is lecture one of `Biologically Inspired Computing’ Contents: Course structure, Motivation for BIC,

done in biology – i.e. (usually) how computation is done by biological machines Basic notes on pattern recognition and optimisation Pattern recognition is often called classification Formally, a classification problem is like this: We have a set of things: S (e.g. images, videos, / nature, this problem seems to be solved wonderfully well, again and again and again, by evolution Nature has designed millions of extremely complex machines, each almost ideal for their tasks (assuming an environment that doesn’t /


Data Mining: Concepts and Techniques — Chapter 1 — — Introduction —

Data Mining: Concepts and Techniques Evolution of Database Technology Data collection, database creation, IMS and network DBMS 1970s: /and computer graphics, etc. April 13, 2017 Data Mining: Concepts and Techniques Recommended Reference Books S. Chakrabarti. Mining the Web: Statistical Analysis of Hypertex and Semi-Structured Data. Morgan Kaufmann, 2002 R. O. Duda, P. E. Hart, and D. G. Stork, Pattern Classification, 2ed., Wiley-Interscience, 2000 T. Dasu and T. Johnson. Exploratory Data Mining and/


1: Introduction. An overview of molecular evolution, or what is molecular evolution?

are based on morphological characters However, morphological characters are not enough to solve all the morphological classification 1.The evolution of organisms and biological complexes by using molecular data. (e.g., species, higher taxa, coevolutionary systems, and migratory patterns) 2.The evolution of molecular entities (e.g., genes, proteins, chromosomal arrangements) “Molecular Evolution” deals with two subjects: How molecular characters are obtained Species Tissue sample DNA extraction Amplification/


Gene Families and Functional Annotation Once genes have been id.ed they need to be functionally annotated A computational first step is to group genes.

certain genes are missing or overrepresented in the given genome - possibly reflecting the niche of the organism As w/ earlier computational analyses, functional annotation based solely on in silico analyses is only a first step 17:17 Gene Families and Functional Annotation Sequence-similarity searches are a first pass in classification BLAST - Basic Local Alignment Search Tool BLASTn - nucleotide BLASTp- protein BLASTx - translates a/


Enzyme Evolution John Mitchell, February 2010. Theories of Enzyme Evolution.

available structures. As more become available, we will find more functions for existing folds, and more folds with existing functions. So these are underestimates! Convergent Divergent Caveat: Our working definition of “Convergent Evolution” is dependent on the EC classification, which is not a perfect gold standard. MACiE Mechanism, Annotation and Classification in Enzymes. http://www.ebi.ac.uk/thornton-srv/databases/MACiE/ The MACiE Database G/


Hidden Markov Models and Graphical Models [slides prises du cours cs294-10 UC Berkeley (2006 / 2009)]

Markov models Discrete-State HMMs  Inference: Filtering, smoothing, Viterbi, classification  Learning: EM algorithm Continuous-State HMMs  Linear state space /Evolution D. Blei, 2007 Bioinformatics Protein Folding (Yanover & Weiss 2003) Computational Genomics (Xing & Sohn 2007) Learning in Graphical Models Tree-Structured Graphs There are direct, efficient extensions of HMM learning and inference algorithms Junction Tree: Cluster nodes to remove cycles (exact, but computation exponential in “distance” of/


Computer Systems Lab TJHSST Current Projects 2004-2005 Second Period.

, S., Warnow, T. A Fast Algorithm for the Computation and Dnumeration of Perfect Phylogenies. 1996. Warnow, T., Nakhleh, L., Ringe, D., Evans, S. A Comparison of Phylogenetic Reconstruction Methods on an IE Dataset. Warnow, T., Nakhleh, L., Ringe, D., Evans, S. Stochastic Models of Language Evolution and an Application to the Indo-European Family of Languages. Developing Algorithms for Computational Comparative Diachronic Historical Linguistics Dan Wright Appendix A My/


Introduction to Data Mining

, used to organi- ze attribute values into different levels of abstraction. Data mining engine: essential to the data mining system; ideally consists of a set of functional modules for tasks such as associa- tion, classification, cluster analysis, and evolution and deviation analysis. Architecture of a Data Mining System (3) Pattern evaluation module: This component typically employs interestingness measures and interacts with the data mining so as to focus the/


Finding Order in Diversity Classification. Why do we need to classify? Imagine a store…..how do you know where to find the milk or the cereal? Are they.

Think about it! The development and safety of all modern medicines, cosmetic products, etc. is based on the Theory of Evolution and Modern Phylogenetics The development of new Crops is based on the Theory of Evolution and Modern Phylogenetics Conservation Biology is based on the Theory of Evolution and Modern Phylogenetics Evolution is an inseparable part of ALL Life Science – Medicine -Agriculture The Kingdoms There are currently 6 kingdoms Classification into a kingdom is based/


From Evolutionary Computation to Ensemble Learning Xin Yao CERCIA, School of Computer Science University of Birmingham UK.

to Ensemble Learning Xin Yao CERCIA, School of Computer Science University of Birmingham UK Overview Introduction (Evolutionary Computation) Multi-objective learning and ensembles Online learning with concept drifts Class imbalance learning Concluding remarks Why Evolution? Learning and evolution are two fundamental forms of adaptation. It is interesting to study both, especially the integration of the two. Simulated evolution makes few assumptions of what’s being evolved. It can be introduced into/


diameter, degree distribution local features such as occurence of certain subgraphs choice of relevant subgraphs based on domain knowledge domain expert based on frequency pattern mining algorithm [Huan et al 04] SFU, CMPT 741, Fall 2009, Martin Ester Graph Classification Kernel-based Graph Classification kernel-based map two graphs x and x‘ into feature space via function compute similarity (inner product) in feature space kernel k/


Core-CT Assessment Final Report

and distinct Core-CT position classifications that effectively outline the unique team-member skill-sets, abilities, compensation, and career path. 4: Prioritize the critical line agency enhancement requests, and enhance the development/ deployment and config/release management capabilities of the Core-CT team. 5: Create State specific training material, develop training technology (computer/ stakeholders of the Core-CT application. Line agencies have a vested interest in the evolution of Core-CT and should /


CENTER FOR BIOLOGICAL SEQUENCE ANALYSIS Multiple Alignment Anders Gorm Pedersen Molecular Evolution Group Center for Biological Sequence Analysis

!!! Clustalx Possible solutions: (1) Cut out conserved regions of interest and THEN align them (2) Use method that deals with local similarity (e.g. DIALIGN) CENTER FOR BIOLOGICAL SEQUENCE ANALYSIS Brief Introduction to the Theory of Evolution Anders Gorm Pedersen Molecular Evolution Group Center for Biological Sequence Analysis gorm@cbs.dtu.dk CENTER FOR BIOLOGICAL SEQUENCE ANALYSIS Classification: Linnaeus Carl Linnaeus 1707-1778 CENTER FOR BIOLOGICAL/


CENTER FOR BIOLOGICAL SEQUENCE ANALYSIS Brief Introduction to the Theory of Evolution Anders Gorm Pedersen Molecular Evolution Group Center for Biological.

differences.Some differences among individuals are based on genetic differences. Individuals with favorable characteristics have higher rates of survival and reproduction.Individuals with favorable characteristics have higher rates of survival and reproduction. Evolution by means of natural selectionEvolution by means of natural selection Presence of ”design-like” features in organisms:Presence of ”design-like” features in organisms: quite often features are there “for a reason”quite often features are/


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