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

Outline Presentation Overview Poster Presentation Oral Presentation My presentation in conference

Important things to look for conferences CFP: call for papers Example: Relevant Topics Important Dates

Presentation Overview It all start with:

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:

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

Outline Presentation Overview Poster Presentation Oral Presentation My presentation in conference

Poster Presentation So what then makes for an effective poster?

First of all “title” Title is the most important thing to attract audience Do NOT typeset the title in all capital letters (Hard to read) Put key words in Title

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

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

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

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

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

Oral Presentation Understand the background of your audience

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

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

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

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

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

Outline Presentation Overview Poster Presentation Oral Presentation My presentation in conference

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

Outline Introduction Methods Positional Association Rule SCAN Dataset Results Conclusion

Protein Primary, Secondary, and Tertiary Structure

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.

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

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

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

Outline Introduction Methods Positional Association Rule SCAN Dataset Results Conclusion

Association Rules

support, s, probability that a transaction contains X  Y confidence, c, conditional probability that a transaction having X also contains Y

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

Positional Association Rules Example

Positional Association Rules

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

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

Directed graph generated from positional association rules with distance =0

Outline Introduction Methods Positional Association Rule SCAN Dataset Results Conclusion

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

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

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

Outline Introduction Methods Positional Association Rule SCAN Dataset Results Conclusion

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

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

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.

Outline Introduction Methods Positional Association Rule SCAN Dataset Results Conclusion

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

μ’s effect on the results

Outline Introduction Methods Positional Association Rule SCAN Dataset Results Conclusion

In this paper, we combine positional association rule and SCAN algorithm to alleviate the mismatch problem caused by fixed window size approach. We show that the positional association rule algorithm can also be used as clustering manner

Future work Find the optimal parameters Improve SCAN into directed graph

Thanks!! Questions??