Artificial Intelligence DNA Hypernetworks Biointelligence Lab School of Computer Sci. & Eng. Seoul National University

From Simple Graphs to Hypergraphs © 2009, SNU Biointelligence Lab, 2 v5v5 v5v5 v1v1 v1v1 v3v3 v3v3 v7v7 v7v7 v2v2 v2v2 v6v6 v6v6 v4v4 v4v4 G = (V, E) V = {v 1, v 2, v 3, …, v 7 } E = {E 1, E 2, E 3, E 4, E 5 } E 1 = {v 1, v 3, v 4 } E 2 = {v 1, v 4 } E 3 = {v 2, v 3, v 6 } E 4 = {v 3, v 4, v 6, v 7 } E 5 = {v 4, v 5, v 7 } E1E1 E4E4 E5E5 E2E2 E3E3 v1v1 v1v1 v3v3 v3v3 v4v4 v4v4 v2v2 v2v2 Simple Graph: Bi-connectionHypergraph: Multi-connection

x1 x2 x3 x4 x5 x6 x7 x8x9 x10 x11 x12 x13 x14 x15 From Hypergraphs to Hypernetworks Vertices: Genes Proteins Chemicals Words Hyperedges: Interactions Genetic Signaling Relations Associations 33 © 2009, SNU Biointelligence Lab,

Hypernetworks A hypernetwork is a hypergraph of weighted edges. It is defined as a triple H = (V, E, W), where V = {v 1, v 2, …, v n }, E = {E 1, E 2, …, E n }, and W = {w 1, w 2, …, w n }. An m-hypernetwork consists of a set V of vertices and a subset E of V [m], i.e. H = (V, V [m], W) where V [m] is a set of subsets of V whose elements have precisely m members and W is the set of weights associated with the hyperedges. A hypernetwork H is said to be k-uniform if every edge E i in E has cardinality (or order) k. A hypernetwork H is k-regular if every vertex has degree k. Note: An ordinary graph is a 2-uniform hypergraph with w i =1. 44 © 2009, SNU Biointelligence Lab,

Generating Hyperedges by Random Sampling © 2009, SNU CSE Biointelligence Lab, 5 x 0 =0 x 1 =0 x 2 =1 x 3 =1 x 4 =0 x 5 =1 x 6 =1 x 7 =0 y=1 x 0 =0x 1 =0y=1 x 1 =0x 5 =1x 6 =1y=1 x 0 =0x 3 =1y=1 x 2 =0x 4 =0x 7 =0y=1 x 0 =0x 2 =1x 7 =0y=1 A Data Sample Hyperedges A hypernetwork label

Basic Elements of Hypernetworks A hypernetwork can be interpreted as a library of weighted hyperedges The cardinality (or order) of each hyperedge in the library may be uniform or hybrid Weights of hyperedges are updated during random matching process based on training instances © 2009, SNU Biointelligence Lab, 6

7 Properties of the Hypernetwork Model Hypernetwork learning addresses both  Structural learning (size and contents of hyperedges) and  Parameter learning (weights of hyperedges) Probabilistic  Higher-order probabilistic relationship  Overcoming the weakness of the ordinary Bayesian networks Descriptive  Can discover the building blocks  Higher-order description  Useful for Discovery  Self-organizing random graphs Cognitive  Memory chunk-like storage mechanism (hyperedges as chunks)  Associative recall of the memory  Useful for Modeling  A random association graph of chunk-like memory fragments

8 Note: Pattern Recognition by DNA Computer [Zhang, DNA-2006]

9 Note: Molecular Self-Assembly of Hypernetworks xixi xjxj y X7 X6 X5 X8 X1 X2 X3 X4 Hypernetwork Representation Molecular Encoding DNA

10 Note: Learning the Hypernetwork (by Evolution) Library of combinatorial molecules + LibraryExample Select the library elements matching the example Amplify the matched library elements by PCR Next generation Hybridize [Zhang, DNA11]

© 2009, SNU Biointelligence Lab, 11 Memory, Learning, and Mining Memory Raw Data Rules Learning Mining

© 2009, SNU Biointelligence Lab, 12 Machine Learning and Human Learning Machine Learning  Recognition  Short-lived  Local or global  Batch learning  Repetition  Statistical  Connectionist Human Learning  Recall  Long-lasting  Local and global  Incremental  Sequential  Categorical  Compositional

DNA hypernetworks as Cognitive Recall Memory

© 2009, SNU Biointelligence Lab, 14 x1 x2 x3 x4 x5 x6 x7 x8x9 x10 x11 x12 x13 x14 x15 Hypergraph Models of Recall Memory Linguistic Memory Vertex: Word Edge: Semantic link Visual Memory Vertex: Object (Pixel) Edge: Spatial link

© 2009, SNU Biointelligence Lab, 15 Visual Memory Classes 0-9, Random Sampling of Features of Order 5 “Mental Chemistry”

© 2009, SNU Biointelligence Lab, 16 Subsampled Features for Two Classes Visual Memory: Image Completion “Mental Chemistry”

© 2009, SNU Biointelligence Lab, 17 Linguistic Memory [Park & Zhang, 2007]

© 2009, SNU Biointelligence Lab, 18  Completion (Generation) & Classification (Recognition) Examples QueryCompletionClassification who are youCorpus: Friends, 24, Prison Break ? are you who ? you who are ? what are you who are you Friends you need to wear itCorpus: 24, Prison Break, House ? need to wear it you ? to wear it you need ? wear it you need to ? it you need to wear ? i need to wear it you want to wear it you need to wear it you need to do it you need to wear a 24 House 24 Linguistic Memory: Sentence Completion “Mental Chemistry”

© 2009, SNU Biointelligence Lab, 19 Applications of Cognitive Learning Biomedical Diagnosis and Treatments  Modeling and Treatment of Memory Deficits  Diagnosis of Language Disorder Human Computer Interaction (HCI)  Cognitively-Friendly User Interface  Tracking User Interests (Games, Web Applications) Memory and Learning Research  Data Mining for Learning and Memory Research  Modeling Cognitive Behaviors Linguistic/Visual Data Processing  Learning and Mining from Text  Learning from Pictures

© 2009, SNU Biointelligence Lab, 20 Conclusion The DNA hypernetworks are a useful tool for modeling cognitive learning and memory processes:  Hypergraphs as memory organization  Random graph process as learning DNA hypernetworks as recognition memories:  Digit recognition  Face recognition  Movie identification DNA hypernetworks as recall memories:  Visual memory  Linguistic memory The multimodal memory game is a scalable platform for studying cognitive learning architectures and algorithms.