how to make a presentation (oral and poster) dr. bernard chen ph.d. university of central arkansas...
TRANSCRIPT
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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
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Outline
Presentation Overview Poster Presentation Oral Presentation My presentation in conference
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Important things to look for conferences
CFP: call for papersExample: http://www.cs.gsu.edu/BIBM2011/?q=node/6
Relevant Topics
Important Dates
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Presentation Overview
It all start with:
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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/
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Presentation Overview
Dress Code: Business Casual
Not necessary wear a suit Shirt, pant, with a tie would be
perfect
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Outline
Presentation Overview Poster Presentation Oral Presentation My presentation in conference
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Poster Presentation
So what then makes for an effective poster?
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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
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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
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Third, “sections” Clearly separate each section
Introduction (This part should include the main research purpose)
Methods Results Conclusion
Not everyone will read all sections
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Fourth, easy reading sections
the poster should NOT contain large blocks of text.
Nor the long sentences
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Making Poster
Here is one poster template in power point format
Use “file => page setup” to change the size of your poster
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Outline
Presentation Overview Poster Presentation Oral Presentation My presentation in an actual
conference
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Oral Presentation
Understand the background of your audience
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Oral Presentation Presentation style:
Never read word to word in your slides
Short sentences in your slides
Eye contact
Enthusiastic in your presentation
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Oral Presentation
Contents: Most important three pages:
First page: Title page + introduction Second page: Outlines Last page: Thank you and Question page
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Oral Presentation
Contents: Main Presentation Body
The main purpose of your research Methods Data Results Conclusion and future works
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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
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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
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Outline
Presentation Overview Poster Presentation Oral Presentation My presentation in conference
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Clustering on Protein Sequence Motifs using SCAN
and Positional Association Rule Algorithms
Dr. Bernard Chen Ph.D.Assistant Professor
Department of Computer ScienceUniversity of Central Arkansas
USA
July 18-21, Las Vegas, NV
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Outline
Introduction Methods
Positional Association Rule SCAN
Dataset Results Conclusion
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Protein Primary, Secondary, and Tertiary Structure
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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.
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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
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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
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mismatches and shifted by several bases problem
In this paper, we deal with “mismatches” problem
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Outline
Introduction Methods
Positional Association Rule SCAN
Dataset Results Conclusion
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Association Rules
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Association Rules
support, s, probability that a transaction contains X Y
confidence, c, conditional probability that a transaction having X also contains Y
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Association Rules
Support (A=>B) = 3/5 Confidence (A=>B) = 3/3
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Positional Association Rules Example
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Positional Association Rules
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Positional Association Rules A=>D minimum distance assurance = 60%
1. = 3/4
2. = 1/4
)(5
DA
)(28
DA
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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
)(0
DA
)(0
DA
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Directed graph generated from positional association rules with distance =0
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Outline
Introduction Methods
Positional Association Rule SCAN
Dataset Results Conclusion
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Structural Clustering Algorithm for Networks (SCAN)
SCAN was originally designed for Network clustering
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Structural Clustering Algorithm for Networks (SCAN)
SCAN has two parameters: ε: Similarity threshold μ: Minimum number of members in a
cluster
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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
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Outline
Introduction Methods
Positional Association Rule SCAN
Dataset Results Conclusion
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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
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Dataset
Therefore, the dataset we work on equals to 2710 transactions and 343 data items
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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.
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Outline
Introduction Methods
Positional Association Rule SCAN
Dataset Results Conclusion
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Distance Assurance effects most ε=0.3 seems generating good results
EPS
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μ’s effect on the results
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Outline
Introduction Methods
Positional Association Rule SCAN
Dataset Results Conclusion
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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
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Future work
Find the optimal parameters
Improve SCAN into directed graph
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Thanks!!
Questions??