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Visual Compression of Workflow Visualizations with Automated Detection of Macro Motifs
Visual Compression of Workflow Visualizations with Automated Detection of Macro Motifs
Visual Compression of Workflow Visualizations with Automated Detection of Macro Motifs
Visual Compression of Workflow Visualizations with Automated Detection of Macro Motifs
Visual Compression of Workflow Visualizations with Automated Detection of Macro Motifs
Visual Compression of Workflow Visualizations with Automated Detection of Macro Motifs
Visual Compression of Workflow Visualizations with Automated Detection of Macro Motifs
Visual Compression of Workflow Visualizations with Automated Detection of Macro Motifs
Visual Compression of Workflow Visualizations with Automated Detection of Macro Motifs
Visual Compression of Workflow Visualizations with Automated Detection of Macro Motifs
Visual Compression of Workflow Visualizations with Automated Detection of Macro Motifs
Visual Compression of Workflow Visualizations with Automated Detection of Macro Motifs
Visual Compression of Workflow Visualizations with Automated Detection of Macro Motifs
Visual Compression of Workflow Visualizations with Automated Detection of Macro Motifs
Visual Compression of Workflow Visualizations with Automated Detection of Macro Motifs
Visual Compression of Workflow Visualizations with Automated Detection of Macro Motifs
Visual Compression of Workflow Visualizations with Automated Detection of Macro Motifs
Visual Compression of Workflow Visualizations with Automated Detection of Macro Motifs
Visual Compression of Workflow Visualizations with Automated Detection of Macro Motifs
Visual Compression of Workflow Visualizations with Automated Detection of Macro Motifs
Visual Compression of Workflow Visualizations with Automated Detection of Macro Motifs
Visual Compression of Workflow Visualizations with Automated Detection of Macro Motifs
Visual Compression of Workflow Visualizations with Automated Detection of Macro Motifs
Visual Compression of Workflow Visualizations with Automated Detection of Macro Motifs
Visual Compression of Workflow Visualizations with Automated Detection of Macro Motifs
Visual Compression of Workflow Visualizations with Automated Detection of Macro Motifs
Visual Compression of Workflow Visualizations with Automated Detection of Macro Motifs
Visual Compression of Workflow Visualizations with Automated Detection of Macro Motifs
Visual Compression of Workflow Visualizations with Automated Detection of Macro Motifs
Visual Compression of Workflow Visualizations with Automated Detection of Macro Motifs
Visual Compression of Workflow Visualizations with Automated Detection of Macro Motifs
Visual Compression of Workflow Visualizations with Automated Detection of Macro Motifs
Visual Compression of Workflow Visualizations with Automated Detection of Macro Motifs
Visual Compression of Workflow Visualizations with Automated Detection of Macro Motifs
Visual Compression of Workflow Visualizations with Automated Detection of Macro Motifs
Visual Compression of Workflow Visualizations with Automated Detection of Macro Motifs
Visual Compression of Workflow Visualizations with Automated Detection of Macro Motifs
Visual Compression of Workflow Visualizations with Automated Detection of Macro Motifs
Visual Compression of Workflow Visualizations with Automated Detection of Macro Motifs
Visual Compression of Workflow Visualizations with Automated Detection of Macro Motifs
Visual Compression of Workflow Visualizations with Automated Detection of Macro Motifs
Visual Compression of Workflow Visualizations with Automated Detection of Macro Motifs
Visual Compression of Workflow Visualizations with Automated Detection of Macro Motifs
Visual Compression of Workflow Visualizations with Automated Detection of Macro Motifs
Visual Compression of Workflow Visualizations with Automated Detection of Macro Motifs
Visual Compression of Workflow Visualizations with Automated Detection of Macro Motifs
Visual Compression of Workflow Visualizations with Automated Detection of Macro Motifs
Visual Compression of Workflow Visualizations with Automated Detection of Macro Motifs
Visual Compression of Workflow Visualizations with Automated Detection of Macro Motifs
Visual Compression of Workflow Visualizations with Automated Detection of Macro Motifs
Visual Compression of Workflow Visualizations with Automated Detection of Macro Motifs
Visual Compression of Workflow Visualizations with Automated Detection of Macro Motifs
Visual Compression of Workflow Visualizations with Automated Detection of Macro Motifs
Visual Compression of Workflow Visualizations with Automated Detection of Macro Motifs
Visual Compression of Workflow Visualizations with Automated Detection of Macro Motifs
Visual Compression of Workflow Visualizations with Automated Detection of Macro Motifs
Visual Compression of Workflow Visualizations with Automated Detection of Macro Motifs
Visual Compression of Workflow Visualizations with Automated Detection of Macro Motifs
Visual Compression of Workflow Visualizations with Automated Detection of Macro Motifs
Visual Compression of Workflow Visualizations with Automated Detection of Macro Motifs
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Visual Compression of Workflow Visualizations with Automated Detection of Macro Motifs

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VIS 2013 Presentation …

VIS 2013 Presentation
Paper is available here: http://www.oerc.ox.ac.uk/personal-pages/emaguire/AutoMacron.pdf
Code is available here: http://github.com/isa-tools/automacron

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  • 1. Visual Compression of Workflow Visualizations with Automated Detection of Macro Motifs Eamonn Maguire, Philippe Rocca-Serra, Susanna-Assunta Sansone, Jim Davies and Min Chen University of Oxford e-Research Centre University of Oxford Department of Computer Science VIS 2013, 13th-18th October 2013
  • 2. Some terminology Workflow Literally a flow of work showing the processes enacted from start to finish in say business processes, software execution, analysis procedures, or in our case, biological experiments. They are used to enable reproducibility. Motif Commonly observed subgraphs Very commonly seen used in: biology - protein-protein interaction, transcription/regulation networks; chemistry; and even visualization (e.g. VisComplete) e.g VisTrails in our VIS community - 40,000 downloads Macro D A single instruction that expands automatically in to a more complex set of instructions. VIS 2013, 13th-18th October 2013 E Q Q D Q E Q
  • 3. Roadmap VIS 2013, 13th-18th October 2013
  • 4. Roadmap Workflow VIS 2013, 13th-18th October 2013 Automatically Detect Motifs Substitute motifs with ‘macros’
  • 5. Blockades VIS 2013, 13th-18th October 2013
  • 6. Blockades No semantics Current Motif Detection Algorithm Limitations VIS 2013, 13th-18th October 2013 Limited motif sizes (Max 10)
  • 7. Blockades Macros in electronic circuit diagrams are the product of years of refinement. No semantics Current Motif Detection Algorithm Limitations VIS 2013, 13th-18th October 2013 Limited motif sizes (Max 10) Deciding what should be a Macro Macros in biological workflows for instance is new...how do we determine what should be a macro?
  • 8. Example case Biology VIS 2013, 13th-18th October 2013
  • 9. Extension on Previous Work Taxonomy-based Glyph Design Visualizing (ISA based) workflows of biological experiments Maguire et al, 2012 IEEE TVCG VIS 2013, 13th-18th October 2013
  • 10. A Typical Biological Experiment Hypothesis VIS 2013, 13th-18th October 2013 Experiment Analysis Results & Paper
  • 11. Representing an Experiment - Workflows! Source name Sampling Protocol Sample name Chemical Label Labeling Protocol Describe the flow of work from a biological sample to the data file. Workflow varies between technologies, but there is a large commonality in steps. Labeled Extract Hybridisation Protocol Assay Name Scanning Protocol Raw Data File Feature Extraction Protocol For example, the labeling step is very common in DNA microarray experiments. Processed Data File VIS 2013, 13th-18th October 2013 u od pr e R bi ci y! lit
  • 12. Our Process 1 A s0 A s1 B E s2 B s3 C D H E E G s4 OCCURRENCE WORKFLOWS 476 3276 F n E MOTIF EXTRACTION ALGORITHM VIS 2013, 13th-18th October 2013 ... MOTIFS DOMAIN EXPERT COMPRESSION 1092 C BIOLOGICAL WORKFLOW REPOSITORY 2.87 ... 2.87 DOMAIN EXPERT 2.4 OCCURRENCE WORKFLOWS COMPRESSION OCCURRENCE WORKFLOWS COMPRESSION 1092 -2.43 Branch & Merge 476 3276 600 240 2400 Branch & Merge OCCURRENCE WORKFLOWS COMPRESSION 20 10 200 RANKING ALGORITHM Branch & Merge SELECTED MACROS Branch & Merge MACRO SELECTION VIA UI MACRO SELECTION GLYPH DESIGN MACRO ANNOTATION MACRO INSERTION IN GRAPH
  • 13. Our Process 1 A s0 A s1 B E s2 B s3 C D H E E G s4 OCCURRENCE WORKFLOWS 476 3276 F n E MOTIF EXTRACTION ALGORITHM VIS 2013, 13th-18th October 2013 ... MOTIFS DOMAIN EXPERT COMPRESSION 1092 C BIOLOGICAL WORKFLOW REPOSITORY 2.87 ... 2.87 DOMAIN EXPERT 2.4 OCCURRENCE WORKFLOWS COMPRESSION OCCURRENCE WORKFLOWS COMPRESSION 1092 -2.43 Branch & Merge 476 3276 600 240 2400 Branch & Merge OCCURRENCE WORKFLOWS COMPRESSION 20 10 200 RANKING ALGORITHM Branch & Merge SELECTED MACROS Branch & Merge MACRO SELECTION VIA UI MACRO SELECTION GLYPH DESIGN MACRO ANNOTATION MACRO INSERTION IN GRAPH
  • 14. Workflow Repository 9,670 Biological Experiment Workflows Why such a large number? We can statistically make suggestions to users about what motifs can be macros based on a number of metrics (detailed later) + we can robustly test our algorithm performance across a huge cross section of experiments... VIS 2013, 13th-18th October 2013
  • 15. 1 A s0 A s1 B E s2 B s3 C D H E E G s4 OCCURRENCE WORKFLOWS 476 3276 F n E MOTIF EXTRACTION ALGORITHM VIS 2013, 13th-18th October 2013 ... MOTIFS DOMAIN EXPERT COMPRESSION 1092 C BIOLOGICAL WORKFLOW REPOSITORY 2.87 ... 2.87 DOMAIN EXPERT 2.4 OCCURRENCE WORKFLOWS COMPRESSION OCCURRENCE WORKFLOWS COMPRESSION 1092 -2.43 Branch & Merge 476 3276 600 240 2400 Branch & Merge OCCURRENCE WORKFLOWS COMPRESSION 20 10 200 RANKING ALGORITHM Branch & Merge SELECTED MACROS Branch & Merge MACRO SELECTION VIA UI MACRO SELECTION GLYPH DESIGN MACRO ANNOTATION MACRO INSERTION IN GRAPH
  • 16. A A s0 1 A s0 A s1 B E s2 B s3 C D H E E G s4 2.87 WORKFLOWS 476 3276 F n E ... ... B DOMAIN EXPERT COMPRESSION 1092 C B E OCCURRENCE s1 2.87 2.4 OCCURRENCE -2.43 OCCURRENCE WORKFLOWS COMPRESSION 20 10 200 WORKFLOWS COMPRESSION OCCURRENCE WORKFLOWS COMPRESSION 1092 476 3276 600 240 2400 s2 C SELECTED MACROS s3 D H E E G DOMAIN EXPERT s4 F Branch & Merge Branch & Merge Branch & Merge Branch & Merge C BIOLOGICAL WORKFLOW REPOSITORY MOTIF EXTRACTION ALGORITHM MOTIFS RANKING ALGORITHM MACRO SELECTION VIA UI E MACRO SELECTION GLYPH DESIGN Motif Extraction Algorithm VIS 2013, 13th-18th October 2013 MACRO ANNOTATION MACRO INSERTION IN GRAPH
  • 17. The Current Weaknesses FANMOD, mFinder etc. No semantics (edge or node) Small node limit normally <10 Imagine n-grams with no information other than topology e.g. bi-grams of DNA ‘motifs’ where instead of A-T, T-C, T-G > x-x, x-x, x-x VIS 2013, 13th-18th October 2013
  • 18. The Problem...Current Motif Extraction Algorithms Unable to infer function Unable to produce a macro What’s up? We can’t infer function from these results Ah, and you can’t have macros without function... Exactly! VIS 2013, 13th-18th October 2013
  • 19. Solution A s0 A s1 B E s2 B s3 C D H E E C E G s4 a holding state, with a pseudo- F a star state a tr that generates a a tr does not generate a a normal state, with a ‘’legal’’ G s4 a normal state, with a ‘’legal’’ a holding state, with a pseudo- F a star state a tr that generates a a tr does not generate a VIS 2013, 13th-18th October 2013 More detail about each individual case, A-H available in paper.
  • 20. Solution A s0 A s1 B E s2 B s3 C D H E E C E G s4 a holding state, with a pseudo- F a star state a tr that generates a a tr does not generate a a normal state, with a ‘’legal’’ G s4 a normal state, with a ‘’legal’’ a holding state, with a pseudo- F a star state 3 a tr that generates a a tr does not generate a VIS 2013, 13th-18th October 2013 More detail about each individual case, A-H available in paper.
  • 21. Resulting In... VIS 2013, 13th-18th October 2013 From our algorithm, running over 9,670 workflows, we retrieved ~12,000 motifs up to depth 12
  • 22. Resulting In... From our algorithm, running over 9,670 workflows, we retrieved ~12,000 motifs up to depth 12 Semantically aware Limited by depth, not node count - we have motifs with > 80 nodes Essentially, more complicated topologically sensitive n-grams VIS 2013, 13th-18th October 2013
  • 23. 1 A s0 A s1 B E s2 B s3 C D H E E G s4 OCCURRENCE WORKFLOWS 476 3276 F n E MOTIF EXTRACTION ALGORITHM VIS 2013, 13th-18th October 2013 ... MOTIFS DOMAIN EXPERT COMPRESSION 1092 C BIOLOGICAL WORKFLOW REPOSITORY 2.87 ... 2.87 DOMAIN EXPERT 2.4 OCCURRENCE WORKFLOWS COMPRESSION OCCURRENCE WORKFLOWS COMPRESSION 1092 -2.43 Branch & Merge 476 3276 600 240 2400 Branch & Merge OCCURRENCE WORKFLOWS COMPRESSION 20 10 200 RANKING ALGORITHM Branch & Merge SELECTED MACROS Branch & Merge MACRO SELECTION VIA UI MACRO SELECTION GLYPH DESIGN MACRO ANNOTATION MACRO INSERTION IN GRAPH
  • 24. 1 2.87 OCCURRENCE 1 s0 A s1 B E s2 B s3 C D H E E G s4 WORKFLOWS 476 3276 F E ... 3276 ... DOMAIN EXPERT COMPRESSION 1092 n 476 2.87 OCCURRENCE C COMPRESSION 1092 A WORKFLOWS ... -2.43 n OCCURRENCE WORKFLOWS COMPRESSION 20 10 200 2.87 MOTIF EXTRACTION ALGORITHM MOTIFS RANKING ALGORITHM OCCURRENCE WORKFLOWS COMPRESSION OCCURRENCE WORKFLOWS COMPRESSION 1092 476 3276 600 240 2400 -2.43 Branch & Merge Branch & Merge SELECTED MACROS Branch & Merge WORKFLOWS 20 10 MACRO SELECTION VIA UI COMPRESSION 200 MACRO SELECTION GLYPH DESIGN Ranking Algorithm ...because 12,000 is just too much. VIS 2013, 13th-18th October 2013 Branch & Merge 2.4 OCCURRENCE BIOLOGICAL WORKFLOW REPOSITORY DOMAIN EXPERT MACRO ANNOTATION MACRO INSERTION IN GRAPH
  • 25. Ranking Algorithm 1,043 M1 - Occurrences in data repository VIS 2013, 13th-18th October 2013 ... 640 M2 -Workflow Presence M3 -Compression Potention
  • 26. Ranking Algorithm 1,043 M1 - Occurrences in data repository VIS 2013, 13th-18th October 2013 ... 640 M2 -Workflow Presence M3 -Compression Potention
  • 27. Ranking Algorithm 1,043 M1 - Occurrences in data repository VIS 2013, 13th-18th October 2013 ... 640 M2 -Workflow Presence M3 -Compression Potention
  • 28. Ranking Algorithm 1,043 M1 - Occurrences in data repository ... 640 M2 -Workflow Presence M3 -Compression Potention For At, Aw and Ac, we map it to a fixed range [−1, 1] using a linear mapping based on the min-max range of each indicator, yielding three normalized metrics M1 , M2 and M3 No algorithm would be complete without a weighting element. So each metric can be weighted. We use a default weight of 1. VIS 2013, 13th-18th October 2013
  • 29. Ranking Algorithm Motifs arranged by depth 3 Normalized metrics Filter by min/max depth Filter by pattern presence Linear, branching and merging Motif subgraph 3 Glyph representations VIS 2013, 13th-18th October 2013 Depth 6 motifs with magnified view in B and detailed popup of selected motif in D
  • 30. Ranking Algorithm Motifs arranged by depth 3 Normalized metrics Filter by min/max depth Filter by pattern presence Linear, branching and merging Motif subgraph 3 Glyph representations VIS 2013, 13th-18th October 2013 Depth 6 motifs with magnified view in B and detailed popup of selected motif in D Score Occurrences Workflow Compression presence Potential
  • 31. Ranking Algorithm Motifs arranged by depth Filter by min/max depth Filter by pattern presence Linear, branching and merging Depth 6 motifs with magnified view in B and detailed popup of selected motif in D Score Occurrences Workflow Compression presence Potential Downgrade Icon Adjusted Score 3 Normalized metrics Motif subgraph 3 Glyph representations VIS 2013, 13th-18th October 2013
  • 32. Ranking Algorithm Motifs arranged by depth Filter by min/max depth Filter by pattern presence Linear, branching and merging Depth 6 motifs with magnified view in B and detailed popup of selected motif in D Score Occurrences Workflow Compression presence Potential 1000 Downgrade Icon Adjusted Score 3 Normalized metrics Motif subgraph 3 Glyph representations VIS 2013, 13th-18th October 2013
  • 33. Ranking Algorithm Motifs arranged by depth Filter by min/max depth Filter by pattern presence Linear, branching and merging Depth 6 motifs with magnified view in B and detailed popup of selected motif in D Score Occurrences Workflow Compression presence Potential 1000 Subset of 1200 3 Normalized metrics Motif subgraph 3 Glyph representations VIS 2013, 13th-18th October 2013 Downgrade Icon Adjusted Score
  • 34. Ranking Algorithm Motifs arranged by depth Filter by min/max depth Filter by pattern presence Linear, branching and merging Depth 6 motifs with magnified view in B and detailed popup of selected motif in D Score Occurrences Workflow Compression presence Potential 1000 Subset of 1200 3 Normalized metrics 200 Motif subgraph 3 Glyph representations VIS 2013, 13th-18th October 2013 Downgrade Icon Adjusted Score
  • 35. 1 A s0 A s1 B E s2 B s3 C D H E E G s4 OCCURRENCE WORKFLOWS 476 3276 F n E MOTIF EXTRACTION ALGORITHM VIS 2013, 13th-18th October 2013 ... MOTIFS DOMAIN EXPERT COMPRESSION 1092 C BIOLOGICAL WORKFLOW REPOSITORY 2.87 ... 2.87 DOMAIN EXPERT 2.4 OCCURRENCE WORKFLOWS COMPRESSION OCCURRENCE WORKFLOWS COMPRESSION 1092 -2.43 Branch & Merge 476 3276 600 240 2400 Branch & Merge OCCURRENCE WORKFLOWS COMPRESSION 20 10 200 RANKING ALGORITHM Branch & Merge SELECTED MACROS Branch & Merge MACRO SELECTION VIA UI MACRO SELECTION GLYPH DESIGN MACRO ANNOTATION MACRO INSERTION IN GRAPH
  • 36. 1 A s0 A s1 B E s2 B s3 C D H E E G s4 OCCURRENCE WORKFLOWS 476 3276 F n E MOTIF EXTRACTION ALGORITHM ... MOTIFS DOMAIN EXPERT COMPRESSION 1092 C BIOLOGICAL WORKFLOW REPOSITORY 2.87 ... 2.87 DOMAIN EXPERT 2.4 OCCURRENCE WORKFLOWS COMPRESSION OCCURRENCE WORKFLOWS COMPRESSION 1092 -2.43 476 3276 600 240 2400 Branch & Merge OCCURRENCE WORKFLOWS COMPRESSION 20 10 200 RANKING ALGORITHM Branch & Merge SELECTED MACROS Branch & Merge MACRO SELECTION VIA UI MACRO SELECTION Glyph Design VIS 2013, 13th-18th October 2013 Branch & Merge GLYPH DESIGN MACRO ANNOTATION MACRO INSERTION IN GRAPH
  • 37. Glyph Design Density Annotation Topology/structure within a macro Node type Things we’d like to see... VIS 2013, 13th-18th October 2013
  • 38. Glyph Design Breadth Topology overall Length Node type annotation colour Breadth Node type Length colour/shape annotation Topology arrangement Breadth Length Node type colour/shape annotation VIS 2013, 13th-18th October 2013 Topology arrangement
  • 39. STATE-TRANSITION MODEL EXAMPLES A s0 A s1 B E s2 s0 s B s3 C D H E E G F s4 C E Breadth Topology overall Length Node type annotation colour Breadth Node type Length colour/shape annotation Topology arrangement Breadth Length Node type colour/shape annotation Topology arrangement s0 A s1 A s1 s1 B s3 C s3 A s1 s1 E s4 F s4 s4 G s1
  • 40. STATE-TRANSITION MODEL EXAMPLES A s0 A s1 B E s2 s0 s B s3 C D H E E G F s4 C E Breadth Topology overall Length Node type annotation colour Breadth Node type Length colour/shape annotation Topology arrangement Breadth Length Node type colour/shape annotation Topology arrangement s0 A s1 A s1 s1 B s3 C s3 A s1 s1 E s4 F s4 s4 G s1
  • 41. 1 A s0 A s1 B E s2 B s3 C D H E E G s4 OCCURRENCE WORKFLOWS 476 3276 F n E MOTIF EXTRACTION ALGORITHM VIS 2013, 13th-18th October 2013 ... MOTIFS DOMAIN EXPERT COMPRESSION 1092 C BIOLOGICAL WORKFLOW REPOSITORY 2.87 ... 2.87 DOMAIN EXPERT 2.4 OCCURRENCE WORKFLOWS COMPRESSION OCCURRENCE WORKFLOWS COMPRESSION 1092 -2.43 Branch & Merge 476 3276 600 240 2400 Branch & Merge OCCURRENCE WORKFLOWS COMPRESSION 20 10 200 RANKING ALGORITHM Branch & Merge SELECTED MACROS Branch & Merge MACRO SELECTION VIA UI MACRO SELECTION GLYPH DESIGN MACRO ANNOTATION MACRO INSERTION IN GRAPH
  • 42. Branch & Merge 1 A s0 A s1 B E s2 B s3 C D H E E G s4 OCCURRENCE WORKFLOWS 476 3276 F n E ... MOTIF EXTRACTION ALGORITHM MOTIFS DOMAIN EXPERT COMPRESSION 1092 C BIOLOGICAL WORKFLOW REPOSITORY 2.87 ... 2.87 DOMAIN EXPERT 2.4 OCCURRENCE WORKFLOWS COMPRESSION OCCURRENCE WORKFLOWS COMPRESSION 1092 -2.43 476 3276 600 240 2400 Branch & Merge OCCURRENCE WORKFLOWS COMPRESSION 20 10 200 RANKING ALGORITHM Branch & Merge SELECTED MACROS Branch & Merge MACRO SELECTION VIA UI MACRO SELECTION Branch & Merge GLYPH DESIGN MACRO ANNOTATION Macro Insertion for Workflow Compression VIS 2013, 13th-18th October 2013 Branch & Merge MACRO INSERTION IN GRAPH
  • 43. Macro Insertion for Workflow Compression VIS 2013, 13th-18th October 2013
  • 44. Macro Insertion for Workflow Compression A VIS 2013, 13th-18th October 2013
  • 45. Macro Insertion for Workflow Compression B A VIS 2013, 13th-18th October 2013
  • 46. Macro Insertion for Workflow Compression C B A VIS 2013, 13th-18th October 2013
  • 47. Macro Insertion for Workflow Compression C B A VIS 2013, 13th-18th October 2013 D
  • 48. Evaluation User Testing VIS 2013, 13th-18th October 2013 Performance
  • 49. Evaluation VIS 2013, 13th-18th October 2013
  • 50. Evaluation VIS 2013, 13th-18th October 2013
  • 51. Evaluation VIS 2013, 13th-18th October 2013
  • 52. Evaluation VIS 2013, 13th-18th October 2013
  • 53. Community Dissemination VIS 2013, 13th-18th October 2013
  • 54. Dissemination of macros to community B Automacron API available as an OSGi plugin for ISAcreator VIS 2013, 13th-18th October 2013
  • 55. Roadmap Workflow VIS 2013, 13th-18th October 2013 Automatically Detect Motifs Substitute motifs with ‘macros’
  • 56. Overcoming the blockades Macros in electronic circuit diagrams are the product of years of refinement. No semantics Current Motif Detection Algorithm Limitations VIS 2013, 13th-18th October 2013 Limited motif sizes (Max 10) Deciding what should be a Macro Macros in biological workflows for instance is new...how do we determine what should be a macro?
  • 57. Overcoming the blockades Macros in electronic circuit diagrams are the product of years of refinement. hm ri t al t ic en ly an Current Motif Detection em s ew Algorithm Limitations N VIS 2013, 13th-18th October 2013 go al semantics d eNo bl a Limited motif sizes (Max 10) Deciding what should be a Macro Macros in biological workflows for instance is new...how do we determine what should be a macro?
  • 58. Overcoming the blockades hm ri t al t ic en ly an Current Motif Detection em s ew Algorithm Limitations N go al semantics d eNo bl a Limited motif sizes (Max 10) om s Macros n fr w in electronic circuit tio arelo product of c kf the diagramsor e le w s f dyearsoof refinement. e rm rpus fo co in e l l y rg ica a la Macros in biological ist f t t a is o Swhatsshould Deciding workflows for instance is y be a nal a Macro new...how do we determine what should be a macro? VIS 2013, 13th-18th October 2013
  • 59. Summary A s0 A s1 B E s2 B s3 C C D H E E G s4 F New semantically enabled motif discovery algorithm E Statistically informed selection of macro candidates for use in biological workflow visualizations Automated macro image generation from inferred from algorithm states Integration of final selections and utility to compress in ISAcreator tool for curators and biologists alike Open source - we want you to extend! VIS 2013, 13th-18th October 2013
  • 60. And yes. Co-authors Philippe Rocca-Serra Susanna-Assunta Sansone Jim Davies Min Chen It is open source! Bye. You can download this software now! Also Alejandra Gonzalez Beltran for many useful discussions VIS 2013, 13th-18th October 2013 github.com/isa-tools/automacron

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