Knowledge based expert systems in Bioinformatics

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  • Myphdworkisgenerallyplaced in a context of information management and knowledgediscoverysincewe are living in a world whereuseful or unsueful information comesfromeverywhereeitherfrom books, journals, magazines, audio and video sources and especiallysince the nternetrevolutionthroughunexpectable information sources like social networks, informationalwebsitesetcwhich are onlymeans by whichhumans exchange their data and information. All of thisuniverseconstitutes the infospherewhere the man istrapped in hisowngame and istodayfacing lot of problems and challenges for thisamount of data management
  • Biology as manyotherfieldsisclearly a good illustration of this data management challenges, especiallywith the computational and infrastructure rapidgrowth and evolutionwhich has a direct effect on the creation of new biological data types and concepts
  • Entre le KB : apprentissageToolchoices plus compliqué : plusieurs outils pour la meme chose : tout integrer
  • Knowledge based expert systems in Bioinformatics

    1. 1. Knowledge Based Expert System development in Bioinformatics applied to Multiple Sequence Alignment<br />Mohamed RadhouaneAniba<br />Laboratoire de Bioinformatique et de GénomiqueIntégratives<br />Supervisors<br />Julie Thompson<br />AronMarchler-Bauer<br />Mohamed RadhouaneAniba<br />15/09/2010<br />1<br />
    2. 2. Outline<br />INTRODUCTION<br />Infosphere and KnowledgeDiscovery<br />BiologicalKnowledgeDiscovery<br /> Data Integration<br />KnowledgeBased Expert Systems in Bioinformatics<br /> KBS : Application to Multiple SequenceAlignment<br />ALIGNMENT EXPERT SYSTEM : AlexSys<br /> Design<br />Implementation<br /> Evaluation<br />CONCLUSIONS AND PERSPECTIVES<br />When The Information Age meets the PostgenomicEra<br />Data Storage, warehousing and Quality<br />From Data Integration to KnowledgeDiscovery… Challenges<br />Why do weneedthem ?<br />Ideal case study ? Data/Textmining, machine learning, knowledgeware<br />Not a software, Not a workflow …<br />BioinformaticsMash-up : Unstructured Information + Apps + Artificial Intelligence<br />Benchmarking, Training data, Test data, performance<br />Mohamed RadhouaneAniba<br />15/09/2010<br />2<br />
    3. 3. Infosphere and KnowledgeDiscovery<br />Mohamed RadhouaneAniba<br />15/09/2010<br />3<br />
    4. 4. BiologicalInfosphere<br />http://genomics.energy.gov<br />Mohamed RadhouaneAniba<br />15/09/2010<br />4<br />
    5. 5. KnowledgeDiscovery Cycle<br />List of simple facts / observations WITHOUTcontext or meaning<br />Whatwelearnafter Information absorption<br />Organized data generatingmeaning ( relationshipbetweenpieces of data )<br />Knowledge extraction is a complexprocess<br />Mohamed RadhouaneAniba<br />15/09/2010<br />5<br />
    6. 6. BiologicalKnowledgeDiscovery<br />Integration<br />Integration<br />Data Warehouse<br />Integration<br />Integration<br />Integration<br />Integration<br />Integration<br />Raw Data<br />Knowledge<br />Raw Data<br />Knowledge<br />Knowledge<br />Raw Data<br />Knowledge<br />Raw Data<br />Knowledge<br />Raw Data<br />Knowledge<br />Raw Data<br />Raw Data<br />Knowledge<br />Distributed Data<br />Access<br />Patterns / Rules<br />Patterns / Rules<br />Patterns / Rules<br />Patterns / Rules<br />Patterns / Rules<br />Patterns / Rules<br />Patterns / Rules<br />SRS<br />Transformed Data<br />Transformed Data<br />Transformed Data<br />Transformed Data<br />Transformed Data<br />Transformed Data<br />Transformed Data<br />Target Data<br />Target Data<br />Target Data<br />Target Data<br />Target Data<br />Target Data<br />Target Data<br />Data Access<br />Data Access<br />Data Access<br />Data Access<br />Data Access<br />Data Access<br />Data Warehouse<br />ENTREZ<br />Understanding<br />Understanding<br />Understanding<br />Understanding<br />Understanding<br />Understanding<br />Understanding<br />Data Mining<br />TextMining<br />Interpretation &<br />Evaluation<br />Data Mining<br />TextMining<br />Interpretation &<br />Evaluation<br />Data Mining<br />TextMining<br />Interpretation &<br />Evaluation<br />Data Mining<br />TextMining<br />Interpretation &<br />Evaluation<br />Data Mining<br />TextMining<br />Interpretation &<br />Evaluation<br />Data Mining<br />TextMining<br />Interpretation &<br />Evaluation<br />Selection & <br />Cleaning<br />Selection & <br />Cleaning<br />ATLAS<br />Selection & <br />Cleaning<br />Selection & <br />Cleaning<br />Selection & <br />Cleaning<br />Selection & <br />Cleaning<br />Data Mining<br />TextMining<br />Interpretation &<br />Evaluation<br />Selection & <br />Cleaning<br />Mohamed RadhouaneAniba<br />15/09/2010<br />6<br />
    7. 7. Towards «  Knowledgeware »<br />Bioinformaticsresourcesintegration<br />Reasoning and decisionmaking<br />Artificial Intelligence and Machine Learning<br />Pipelines / Workflows<br />Metadatabased system<br />Human expertise<br />Mohamed RadhouaneAniba<br />15/09/2010<br />7<br />
    8. 8. Towards «  Knowledgeware »<br />Bioinformaticsresourcesintegration<br />Reasoning and decisionmaking<br />Artificial Intelligence and Machine Learning<br />Pipelines / Workflows<br />Metadatabased system<br />Human expertise<br />Software<br />Knowledgeware<br />(expert system)<br />ProblemSolver<br />atSingleLevel<br />ProblemSolver<br />atSystem Level<br />Mohamed RadhouaneAniba<br />15/09/2010<br />8<br />
    9. 9. Expert Systems in Bioinformatics, Why ?<br /><ul><li> Expert System : A veryold concept
    10. 10. Stop talking about AMOUNT OF DATA !!
    11. 11. Bioinformatics : the real problemis data dynamics and complexity
    12. 12. Data origins and evolution
    13. 13. Tons of answers to one single question
    14. 14. Necessity to rely on models and gold standards => Comparison
    15. 15. Comparison, Conservation, Differences
    16. 16. Weneed standards ! Weneed to learnfrom the past and to make the right predictions</li></ul>Large amount of data<br /><ul><li>Necessity to rely on models and gold standards => Comparison
    17. 17. Comparison = Conservation + Differences
    18. 18. Weneed to learnfrom the past and to make the right predictions
    19. 19. Intelligent decisions</li></ul>+<br />Data complexity and dynamics<br />Bioinformatics<br />+<br />Large number of softwares and algorithms<br />Mohamed RadhouaneAniba<br />15/09/2010<br />9<br />
    20. 20. Expert System for MSA<br /><ul><li> MSA plays a central role in the biologicalinfosphere
    21. 21. Strategic application: impact on otherfields</li></ul>Mohamed RadhouaneAniba<br />15/09/2010<br />10<br />
    22. 22. MSA complexity<br /><ul><li> MSA : more and more complex
    23. 23. Sequencenumber forces process automation
    24. 24. Noisy data (error propagation)
    25. 25. Data complexity</li></ul>Multidomainproteins<br />P53/P63/P73<br />Toomanysequences (> 10 000)<br />Errors (Sequencingerrors , poorpredictions .. ) <br />40 ~ 50 % <br />more and more long and complex<br />proteinsequences<br />Complicating the construction and analysis of MSA<br />Mohamed RadhouaneAniba<br />15/09/2010<br />11<br />
    26. 26. MSA Algorithm Evolution<br /><ul><li>Algorithmicdiversity
    27. 27. MSA : a mature field
    28. 28. MSA construction stages and validation (Expertise exists)</li></ul>Co-operativealgorithms : non redundantand important approach<br />Thompson et al. J. Mol. Biol 2001<br />Thompson and Poch, Current Bioinformatics, 2006<br />Mohamed RadhouaneAniba<br />15/09/2010<br />12<br />
    29. 29. MSA state of the art<br />Complexproteinfamilies : programs behavedifferently<br />CONSERVED VS DIVERGENT<br />No single algorithm to solve all problems : cooperativeapproaches<br />Mohamed RadhouaneAniba<br />15/09/2010<br />13<br />
    30. 30. Thesis Objectives: AlexSys<br />Specification<br /><ul><li> Develop an integrated expert system to test, evaluate and optimize all stages of the construction, analysis and exploitation of a multiple sequence alignment</li></ul>Objectives<br /><ul><li>Milestone 1Developa modularplatform, incorporatingdifferent, complementaryalgorithms and heterogeneous data (sequence, structure, function, evolution…)
    31. 31. Milestone 2Automaticallydefinealgorithms to use in eachstep of multiple sequencealignment construction based on intelligent decisions
    32. 32. Milestone 3Understandrelationshipsbetweensequencecharacteristics and algorithmicstrengths and weaknesses
    33. 33. Milestone 4Developdifferentanalysisprotocolsdedicated to different applications (Bio-Scenarios : comparative genomics, functional annotation, 3D modeling, evolutionarystudies … )</li></ul>Mohamed RadhouaneAniba<br />15/09/2010<br />14<br />
    34. 34. Expert System Development<br />Specifications<br />Design<br />ProblemDefinition<br />Development<br />Maintenance<br />Evolution<br />Knowledge Base<br />Tools Choice<br />Data access<br />Analysis<br />modules<br />Bug Reporting<br />Code optimization<br />Testing<br />Results<br />Modules Extensions<br />Cross platform<br />Deployment<br />Exploitation<br />Mohamed RadhouaneAniba<br />15/09/2010<br />15<br />
    35. 35. KnowledgeBased Expert System Design<br />Users<br />Domain Expertise, factsused by ES<br />To makedeterminations<br />2<br />1<br />User Interface<br />Databasecontaining data specific to a problembeingsolved<br />5<br />6<br />2<br />Analysis Modules<br />Aquisition<br />3<br />InferenceEngine : Code at the core of the system thatderivesrecommendations<br />6<br />4<br />3<br />4<br />Knowledge base<br />Working Storage<br />Update or expand the knowledge base<br />4<br />1<br />UI : dialogbetween the user and the ES<br />5<br />Experts<br />Mohamed RadhouaneAniba<br />15/09/2010<br />16<br />
    36. 36. AlexSys developmentplatform<br />Development alternatives<br />UIMAUnstructured Information Management Architecture.<br />Scalable and extensible platform <br />Deployment of unstructured information management solutions<br /><ul><li>Develop a dedicated system from scratch:</li></ul>Time consuming, not easy to maintain(C, prolog …)<br /><ul><li>Use an existing ES shell: (CLIPS, JESS, UIMA …)</li></ul>UIMA Advantages:<br />Ready to use architecture<br />Modulesoriented<br />Services and developmenttools<br />Data-Drivenflows<br />XML basedcomponents<br />Active community<br />Apache incubatorproject<br />Wide support<br />Javaprogramminglanguage<br />Mohamed RadhouaneAniba<br />15/09/2010<br />17<br />
    37. 37. AlexSys developmentplatform<br />UIMA Expert System architecture<br />Type System<br />(Data Containers)<br />ExampleSequence<br />ID : String<br />Sequence : String<br />Length : Integer<br />CrossReference : String<br />Etc …<br />Type System<br />(Data Containers)<br />Example Blast<br />Query : String<br />Result : String<br />Evalue : float<br />Hits : Integer ….<br />Analysis Module<br />(1 module = 1 task)<br />Example Blast/Alignment<br />Structured<br />Data<br />Unstructured<br />Data<br />Mohamed RadhouaneAniba<br />15/09/2010<br />18<br />
    38. 38. AlexSys Core System: Milestone 1<br />Development of Bio-scenarios<br />Data access and standardization<br />Metadataretrieval and integration (structure, function, literature, clinicalstudies, …)<br />Data curation and validation (predictionerrors, sequencequality …)<br />Data classification according to the analysis scenario<br />Alignment construction (combination of differentalgorithms)<br />Alignment validation, refinement and qualitymeasurement<br />Alignmentautomatic annotation <br />Mohamed RadhouaneAniba<br />15/09/2010<br />19<br />
    39. 39. AlexSys : MSA Construction<br />Choose a suitable MSA program to align input sequences<br />Input/Output management module<br />(API : Biojava)<br />SequenceFeature Extraction modules: <br />Number, length, %ID, helix, strands, hydrophobicity, composition etc …<br />BIRD, MACSIMS, Interproscan<br />Type System (CAS)<br />Sequences<br />Multiple Alignment modules: Incorporation of differentalgorithms<br />Type System (CAS)<br />NewFormat<br />Type System (CAS)<br />Features<br />Type System (CAS)<br />Alignment<br />Alignment Program = f ( feature1, feature2, feature3…featureN ) ?<br />Whichfeaturecombinationis « dangerous » for a given program ?<br />Whatmakes a given program sensitive to a givenfeaturecombination ?<br />Mohamed RadhouaneAniba<br />15/09/2010<br />20<br />
    40. 40. AlexSys intelligent decision<br />Incorporation of machine learningstep<br />Alignment Program = f ( feature1, feature2, feature3…featureN ) ?<br />Collect Data<br />Choose Features<br />Choose Model<br />Train Classifier<br />Evaluate Classifier <br />Mohamed RadhouaneAniba<br />15/09/2010<br />21<br />
    41. 41. AlexSys intelligent decision<br />Incorporation of machine learningstep<br />Alignment Program = f( feature1, feature2, feature3…featureN ) ?<br />Collect Data<br />Choose Features<br />Choose Model<br />Train Classifier<br />Evaluate Classifier <br />Mohamed RadhouaneAniba<br />15/09/2010<br />22<br />
    42. 42. AlexSys intelligent decision<br />Incorporation of machine learningstep<br />Training/Test set: BAliBase 3.0<br />Reference 1 equi-distant sequences with various levels of conservation<br />Reference 2 families aligned with a highly divergent "orphan" sequence <br />Reference 3 subgroups with <25% residue identity between groups <br />Reference 4 sequences with N/C-terminal extensions <br />Reference 5 internal insertions<br />Reference 6 repeats<br />Reference 7 transmembrane regions<br />Reference 8 circular permutations<br />218<br />Alignments<br />6222<br />Sequences<br />Thompson et al. Bioinformatics 1999<br />Bahr et al., Nucl Acids Res, 2001<br />Thompson et al. Proteins 2005 <br />http://www-bio3d-igbmc.u-strasbg.fr/balibase/<br />Mohamed RadhouaneAniba<br />15/09/2010<br />23<br />
    43. 43. AlexSys intelligent decision<br />Incorporation of machine learningstep<br />Alignment Program = f ( feature1, feature2, feature3…featureN ) ?<br />Collect Data<br />Choose Features<br />Choose Model<br />Train Classifier<br />Evaluate Classifier <br />Mohamed RadhouaneAniba<br />15/09/2010<br />24<br />
    44. 44. AlexSys intelligent decision<br />Incorporation of machine learningstep<br /><ul><li> Brainstorming
    45. 45. ExperiencewithAlignment benchmarks
    46. 46. Featureselection</li></ul>Mohamed RadhouaneAniba<br />15/09/2010<br />25<br />
    47. 47. AlexSys intelligent decision<br />Incorporation of machine learningstep<br />Alignment Program = f ( feature1, feature2, feature3…featureN ) ?<br />Collect Data<br />Choose Features<br />Choose Model<br />Train Classifier<br />Evaluate Classifier <br />Mohamed RadhouaneAniba<br />15/09/2010<br />26<br />
    48. 48. AlexSys intelligent decision<br />Incorporation of machine learningstep<br />Sum of Pairs Probcons<br />0 1<br />ProbCons<br />UnalignedSequences<br />Sum of Pairs Mafft<br />0 1<br />Mafft<br />Reference (BAliBase)<br />Sum of Pairs Muscle<br />0 1<br />Muscle<br />All in one model<br />Instances<br />Class<br />Mafft<br />Attributes<br />Probcons<br />Muscle<br />175 sets (80%) x 6 alignment programs = 1050 operations<br />Mohamed RadhouaneAniba<br />15/09/2010<br />27<br />
    49. 49. AlexSys intelligent decision<br />Incorporation of machine learningstep<br />All in one model<br />Class<br />Mafft<br />Probcons<br />Muscle<br />Machine Learning<br />Model<br />UnknownSequences<br />Which Class ?<br />Mohamed RadhouaneAniba<br />15/09/2010<br />28<br />
    50. 50. AlexSys intelligent decision<br />Incorporation of machine learningstep<br /><ul><li>Widelyused in Bioinformatics
    51. 51. Decisiontrees are understandable by humans
    52. 52. Trees are easilyconverted to rules
    53. 53. Simple learning procedure, fast evaluation.
    54. 54. Can be applied to metric, nominal, or mixed data.</li></ul>DecisionTrees<br />BayesianMethods<br />Hidden Markov Models<br />Support Vector Machines<br />Neural Networks<br />Clustering<br />GeneticAlgorithms<br />Association Rules<br />Reinforcement Learning<br />Fuzzy Sets<br />DecisionTrees<br />Mohamed RadhouaneAniba<br />15/09/2010<br />29<br />
    55. 55. AlexSys intelligent decision<br />Incorporation of machine learningstep<br />Alignment Program = f ( feature1, feature2, feature3…featureN ) ?<br />Collect Data<br />Choose Features<br />Choose Model<br />J48 / RandomTree / Random Forest<br />Train Classifier<br />Train Set / 10 fold Cross Validation<br />Evaluate Classifier <br />Test Set / Performance <br />Mohamed RadhouaneAniba<br />15/09/2010<br />30<br />
    56. 56. AlexSys intelligent decision<br />Incorporation of machine learningstep<br />All in one model<br />2x(PxR)/(P+R)<br />TP/(TP+FN)<br />TP/(TP+FP)<br />TP<br />C4.5 (J48)<br />FN<br />Correctlyclassifiedalignments<br />~ 42 %<br />FP<br />Mohamed RadhouaneAniba<br />15/09/2010<br />31<br />
    57. 57. AlexSys intelligent decision<br />Incorporation of machine learningstep<br />All in one model<br />RandomTree<br />Correctlyclassifiedalignments<br />~ 41 %<br />Mohamed RadhouaneAniba<br />15/09/2010<br />32<br />
    58. 58. AlexSys intelligent decision<br />Incorporation of machine learningstep<br />All in one model<br />Random Forest<br />Correctlyclassifiedalignments<br />~ 52 %<br />Mohamed RadhouaneAniba<br />15/09/2010<br />33<br />
    59. 59. AlexSys intelligent decision<br />Incorporation of machine learningstep<br />Not Accurate<br />Training set toosmall, not representative ?<br />Not enoughfeatures ?<br />Complex multi-dimensional model ?<br />Alignment programs are difficult to distinguish in some cases ?<br />Mohamed RadhouaneAniba<br />15/09/2010<br />34<br />
    60. 60. AlexSys intelligent decision<br />Incorporation of machine learningstep<br />Alignment Program = f ( feature1, feature2, feature3…featureN ) ?<br />Oxbench<br />605 Alignments<br />3656 Sequences<br />BAliBase 4.0 (New)<br />240 Alignments<br />19806 Sequences<br />ADD MORE Data<br />1063 Alignments<br />29684 Sequences<br />Choose Features<br />Choose Model<br />Train Classifier<br />Not YetAccurate<br />Evaluate Classifier <br />Mohamed RadhouaneAniba<br />15/09/2010<br />35<br />
    61. 61. AlexSys intelligent decision<br />Incorporation of machine learningstep<br />Alignment Program = f ( feature1, feature2, feature3…featureN ) ?<br />AlignmentQuality = f ( feature1, feature2, feature3…featureN ) ?<br />Collect Data<br />Choose Features<br />CHANGE Model<br />Train Classifier<br />Binary Classification<br />Models for AlignmentStrength<br />Evaluate Classifier <br />Mohamed RadhouaneAniba<br />15/09/2010<br />36<br />
    62. 62. AlexSys intelligent decision<br />Incorporation of machine learningstep<br />Mohamed RadhouaneAniba<br />15/09/2010<br />37<br />
    63. 63. AlexSys intelligent decision<br />Incorporation of machine learningstep<br />Mohamed RadhouaneAniba<br />15/09/2010<br />38<br />
    64. 64. AlexSys intelligent decision<br />ClustalW<br />Mafft<br />Dialign<br />Mode 1<br />Muscle<br />Predictive<br />Based on Probability<br />UnalignedSequences<br />+<br />Kalign<br />Mode 2<br />Probcons<br />Intuitive<br />Based on rules<br />Pr(Probcons) = Strong<br />Pr(Mafft) = Strong<br />Pr(Dialign) = Weak<br />Pr(ClustalW) = Weak<br />Pr(Kalign) = Strong<br />Pr(Muscle) = Weak<br />If<br />Decision Inside AlexSys<br />Combine seperatepredictionsinto a single decision<br />Mohamed RadhouaneAniba<br />15/09/2010<br />39<br />
    65. 65. AlexSys Intelligent System: Milestone 2<br />AnalysisEngine to predictsuitable program <br />for an unknown set of sequences<br />Aligner Predictor<br />Mohamed RadhouaneAniba<br />15/09/2010<br />40<br />
    66. 66. AlexSys evaluation: MSA Accuracy<br />Mohamed RadhouaneAniba<br />15/09/2010<br />41<br />
    67. 67. Exploitation : Milestone 3<br />(Sequence/Algorithmrelationship) RadViz (Radial Visualization)<br />ClustalW<br />Mohamed RadhouaneAniba<br />15/09/2010<br />42<br />
    68. 68. (Sequence/Algorithmrelationship) RadViz (Radial Visualization)<br />Exploitation : Milestone 3<br />Mafft<br />ClustalW<br />Probcons<br />Dialign<br />Mohamed RadhouaneAniba<br />15/09/2010<br />43<br />
    69. 69. Exploitation : Milestone 3<br />Detectingunalignablesequence sets<br />High SP Scores<br />Alignmentswithlow SP scores<br />All programs fail<br />Low SP Scores<br />Mohamed RadhouaneAniba<br />15/09/2010<br />44<br />
    70. 70. Exploitation : High throughputproject<br />EvolHHuPro (ANR, collaboration P. Pontarotti) <br /><ul><li>Reconstruction of evolutionary histories of the humanproteome
    71. 71. For eachhumanprotein (~18,000):
    72. 72. Search for homologs in 20 vertebratespecies
    73. 73. Construct MSA using MAFFT
    74. 74. Reconstructphylogenetictrees and geneticevents</li></ul>Domain organisation<br />Referencegenome<br />Gene order<br />Exon<br />shuffling<br />duplication<br />insertion<br />Mohamed RadhouaneAniba<br />15/09/2010<br />45<br />
    75. 75. Exploitation : High throughputproject<br />16/800 (2%) <br />Predicted to be « weak »<br />AlexSys[Mafft] ~ 0.4<br />800 random MSA <br />(EvolHHuPro)<br />Examplequery ANR60_HUMAN<br />=> Eitherchooseanother program, or warning « unalignable »<br />Ankyrinrepeats<br />ZU5<br />Death<br />Mohamed RadhouaneAniba<br />15/09/2010<br />46<br />
    76. 76. Conclusions<br /><ul><li> To extractknowledgefrom large biological data sets, weneed expert systems
    77. 77. Wedeveloped a novel system for MSA construction and validation
    78. 78. Objectives achieved:</li></ul>Core system, incorporatingdifferent, complementaryalgorithms<br />Understanding of relationshipsbetweensequencecharacteristics and algorithmicstrengths and weaknesses<br />Development of a system thatcanautomaticallydefinewhichalgorithm to use depending on the sequencefeaturesusing an Intelligent Engine<br /> Application in a highthroughputproject<br />Mohamed RadhouaneAniba<br />15/09/2010<br />47<br />
    79. 79. Perspectives<br /><ul><li>Detection of unalignablealignments: Preventingerror propagation
    80. 80. Use knowledgegained to improvealgorithms for alignment construction (ClustalW/X, …)
    81. 81. Integration of additionalalgorithms (transmembrane, repeats, disorderedregions, motif detection, …)
    82. 82. Integration of additional data (domains, 3D structures, function, mutation, …)
    83. 83. Integration of information from the literature (exploitation of UIMA)
    84. 84. Extendknowledge base (BAliBASE, feature investigation)
    85. 85. Develop Bio-Scenarios for specifictasks/projects</li></ul>Mohamed RadhouaneAniba<br />15/09/2010<br />48<br />
    86. 86. Perspectives: ES<br /><ul><li> ES are suitable for most applications in bioinformaticstoday
    87. 87. Dedicated system design : Bioinformaticsproblems
    88. 88. Human expertise needs to beformalized (ontologies, logicprogramming …)
    89. 89. Dynamic, evolving: integration of new, useful data and algorithms as they are developed
    90. 90. Evaluation of the quality of input data and results (Objective functions)
    91. 91. Multicore distribution (Gridcomputing)
    92. 92. Cloud Computing : Amazon EC2, IBM, Google, Sun … (BlastReduce, Biodoop, CloudBrust, CloudBlast, …)
    93. 93. Create a community for ES in bioinformatics (Standards development and open projects)</li></ul>Mohamed RadhouaneAniba<br />15/09/2010<br />49<br />
    94. 94. Acknowledgement<br />Julie Thompson<br />Aron Marchler-Bauer<br />Mohamed RadhouaneAniba<br />15/09/2010<br />50<br />
    95. 95. Infosphere and KnowledgeDiscovery<br />Mohamed RadhouaneAniba<br />15/09/2010<br />51<br />
    96. 96. Information hierarchical classification<br />Luciano Floridi<br />Unstructured Data<br />Primary Information<br />(Info in Databases …)<br />Data<br />(Structured)<br />Secondary Information<br />(Presence / Absence …)<br />Environmental<br />Semantic<br />(Content)<br />Meta Information<br />(Copyright …)<br />Instructional<br />factual<br />Untrue<br />True<br />(Information)<br />Operational Information<br />(Info about IS dynamics)<br />Derivative Information<br />(comparative/quantitative analyses)<br />Unintentional<br />(Misinformation)<br />Intentional<br />(Disinformation)<br />Knowledge<br />Mohamed RadhouaneAniba<br />15/09/2010<br />52<br />
    97. 97. AlexSys Prototype Testing<br />Mohamed RadhouaneAniba<br />15/09/2010<br />53<br />
    98. 98. Transmembrane<br />Repeats<br />Mafft<br />Dialign<br />Probcons<br />Mohamed RadhouaneAniba<br />15/09/2010<br />54<br />
    99. 99. Sequence/Algorithmrelationship: Milestone 3<br />Mohamed RadhouaneAniba<br />15/09/2010<br />55<br />

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