How to make a presentation (Oral and Poster) - PowerPoint PPT Presentation

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How to make a presentation (Oral and Poster)

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  1. How to make a presentation (Oral and Poster) Dr. Bernard Chen Ph.D. University of Central Arkansas July 5th HIT@UCA Applied Research in Healthy Information

  2. Outline • Presentation Overview • Poster Presentation • Oral Presentation • My presentation in conference

  3. Important things to look for conferences • CFP: call for papers Example: http://www.cs.gsu.edu/BIBM2011/?q=node/6 • Relevant Topics • Important Dates

  4. Presentation Overview • It all start with:

  5. Presentation Overview • Accepted paper type: • Oral Presentation (usually 15~20 minutes presentation, 5 minutes for questions) • Regular research paper • Short research paper • Poster Presentation • Poster paper Example: http://acmbcb.org/accepted-regular-papers/

  6. Presentation Overview • Dress Code: Business Casual • Not necessary wear a suit • Shirt, pant, with a tie would be perfect

  7. Outline Presentation Overview Poster Presentation Oral Presentation My presentation in conference

  8. Poster Presentation • So what then makes for an effective poster?

  9. First of all “title” • Title is the most important thing to attract audience • Do NOTtypeset the title in all capital letters (Hard to read) • Put key words in Title

  10. Second “the purpose” • “10 seconds” is about the time that a person can spend to recognize the work • Clearly define the purpose of the paper • The type is large enough to read

  11. Third, “sections” • Clearly separate each section • Introduction (This part should include the main research purpose) • Methods • Results • Conclusion • Not everyone will read all sections

  12. Fourth, easy reading sections • the poster should NOT contain large blocks of text. • Nor the long sentences

  13. Making Poster • Here is one poster template in power point format • Use “file => page setup” to change the size of your poster

  14. Outline Presentation Overview Poster Presentation Oral Presentation My presentation in an actual conference

  15. Oral Presentation • Understand the background of your audience

  16. Oral Presentation • Presentation style: • Never read word to word in your slides • Short sentences in your slides • Eye contact • Enthusiastic in your presentation

  17. Oral Presentation • Contents: • Most important three pages: • First page: Title page + introduction • Second page: Outlines • Last page: Thank you and Question page

  18. Oral Presentation • Contents: • Main Presentation Body • The main purpose of your research • Methods • Data • Results • Conclusion and future works

  19. Oral Presentation • Practice makes it perfect • Finish the presentation slides two weeks before the D-day • Rehearse at least two times with your advisor • Practice at least once/day, start one week before the D-day

  20. Oral Presentation • Arrive the room at least 15 minutes prior to the start of the session • Bring your laptop is always safe • Make two copy of your presentation in your jump drive and in your email

  21. Outline Presentation Overview Poster Presentation Oral Presentation My presentation in conference

  22. Clustering on Protein Sequence Motifs using SCAN and Positional Association Rule Algorithms Dr. Bernard Chen Ph.D. Assistant Professor Department of Computer Science University of Central Arkansas USA July 18-21, Las Vegas, NV

  23. Outline • Introduction • Methods • Positional Association Rule • SCAN • Dataset • Results • Conclusion

  24. Protein Primary, Secondary, and Tertiary Structure

  25. Protein Sequence Motif • Although there are 20 amino acids, the construction of protein primary structure is not randomly choose among those amino acids • Sequence Motif: A relatively small number of functionally or structurally conserved sequence patterns that occurs repeatedly in a group of related proteins.

  26. Goal of the our group • The main purpose is trying to obtain and extract protein sequence motifs which are universally conserved and across protein family boundaries. • Discuss the hidden relation between protein Primary sequences and their Tertiary structure

  27. The Main purpose of this paper • In order to obtain the DNA/protein sequence motifs information, fixing the length of sequence segments is usually necessary. • Due to the fixed size, they might deliver a number of similar motifs simply shifted by several bases or including mismatches

  28. mismatches and shifted by several bases problem • In this paper, we deal with “mismatches” problem

  29. Outline • Introduction • Methods • Positional Association Rule • SCAN • Dataset • Results • Conclusion

  30. Association Rules

  31. Association Rules • support, s, probability that a transaction contains X  Y • confidence, c,conditional probability that a transaction having X also contains Y

  32. Association Rules • Support (A=>B) = 3/5 • Confidence (A=>B) = 3/3

  33. Positional Association Rules Example

  34. Positional Association Rules

  35. Positional Association Rules A=>Dminimum distance assurance = 60% 1.= 3/4 2. = 1/4

  36. Positional Association Rules • By applying positional association rules into our data set, we obtain two type of rules: • Rules with distance =0 , and • Rules with distance not =0

  37. Directed graph generated from positional association rules with distance =0

  38. Outline • Introduction • Methods • Positional Association Rule • SCAN • Dataset • Results • Conclusion

  39. Structural Clustering Algorithm for Networks (SCAN) • SCAN was originally designed for Network clustering

  40. Structural Clustering Algorithm for Networks (SCAN) • SCAN has two parameters: • ε: Similarity threshold • μ: Minimum number of members in a cluster

  41. Structural Clustering Algorithm for Networks (SCAN) • Similarity is calculated by • Γ(E)={E,B} • Γ(B)={E,B,A,C,D} • which is the example of σ(E,B)=Γ(E) ∩ Γ(B) / sqrt(num(Γ(E) )*num(Γ(B))) = 2/ sqrt(2*5) = 0.63

  42. Outline • Introduction • Methods • Positional Association Rule • SCAN • Dataset • Results • Conclusion

  43. Dataset • In our previous work, we discovered 343 protein sequence motifs from 2710 protein sequences • So we mapped those sequences back to those protein sequences

  44. Dataset • Therefore, the dataset we work on equals to 2710 transactions and 343 data items

  45. Evaluation of the cluster • The quality of the cluster is evaluated by secondary structural similarity • If the structural homology for a cluster exceeds 70%, the cluster can be considered structurally identical.

  46. Outline • Introduction • Methods • Positional Association Rule • SCAN • Dataset • Results • Conclusion

  47. Distance Assurance effects most ε=0.3 seems generating good results EPS