How to make a presentation (Oral and Poster)

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How to make a presentation (Oral and Poster)

• Dr. Bernard Chen Ph.D.
• University of Central Arkansas
• July 5th
• HIT_at_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 papers
• Example http//www.cs.gsu.edu/BIBM2011/?qnode/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 1520 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 gt 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 Science
• University 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 (AgtB) 3/5
• Confidence (AgtB) 3/3

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Positional Association Rules Example
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Positional Association Rules
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Positional Association Rules AgtD minimum
distance assurance 60

• 1. 3/4
• 2. 1/4

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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

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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
• e Similarity threshold
• µ Minimum number of members in a cluster

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Structural Clustering Algorithm for Networks
(SCAN)

• Similarity is calculated by
• G(E)E,B
• G(B)E,B,A,C,D
• which is the example of
• s(E,B)G(E) n G(B) / sqrt(num(G(E) )num(G(B)))
• 2/ sqrt(25) 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 e0.3 seems
generating good results
EPS
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µs 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??