The six month progress report summarizes what the author has learned, gaps in their understanding, outputs so far, and plans until December 2008. The author provides details on settling into a new environment and learning about regulations, as well as gaining knowledge in thesis writing, LaTeX, databases, and biological text mining theory and hands-on work. The report also outlines continuing to develop skills in writing, coding, and presenting results.

1. Six Month Progress Report
Farzaneh Sarafraz
14 August 2008

2. In this report
What I have learnt
●
What are the gaps in my understanding
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Outputs so far
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Reflection on supervision mode
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Plan outline until December 2008
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3. 1. What I have learnt – general
General
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Settled down in a new environment
–
Learnt some of the regulations and how things
–
work in
The country
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The city
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The university
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The faculty
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The school
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4. What I have learnt – less
general
Less general
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Thesis and paper writing theory and practice
–
Specifically through the CS7100 seminar
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LaTeX
–
Coding infrastruction
–
Warmed up!
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Database handling
–
Administration / web applications
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Specific
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Biological text mining theory
–

5. Biological text mining
Biological text mining theory
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Main problems
–
Main challenges
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Main approaches
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Communities
–
Events, papers, journals, competitions, etc.
–
40+ papers in my CiteULike account
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Biological text mining hands on
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Tools, techniques, and resources
–
i2b2
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HIV
–

6. Biological text mining theory
Main problems
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Information retrieval
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Information extraction
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Relation extraction
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Shallow parsing / chunking
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POS tagging
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Word sense disambiguation
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Term variation
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7. Biological text mining theory
(cont.)
Main problems (cont.)
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Named entity recognition
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Dictionary based
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Rule based
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Machine learning (HMM: Zhou et al.)
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Hybrid
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Evaluation
–
Precision, recall, F­Score
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Sensitivity and specificity
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Not always possible due to the lack of
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Test Corpora
–
Common domains, techniques, goals
–

8. Biological text mining theory
(cont.)
Main challenges
●
Deal with sublanguage of biology
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Build scalable and robust systems
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Present the results in meaningful and informative
–
ways to the biologist
Deal with interdisciplinary aspects
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Biology – chemistry – medicine
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Different views / information needs
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Specific field (biomedicine) – linguistics – computation
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and data mining

9. Main Challenges (cont.)
Specific field (biomedicine) – linguistics –
●
computation and data mining
The text is not necessarily written to be
–
comprehensible by automatic techniques
The language is dramatically different from that
–
of e.g. newswire.
Terminology, new and coined terms, usage
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ambiguity
Non­algorithmic, irrational patterns in NL
–

10. Resources
I am aware of / I am using existing resources
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Literature repositories/search engines
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Pubmed, MEDLINE, BioMed
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Google
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Parsers
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Stanford Parser
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GeniaTagger
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Terminological resorces
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Gene Ontology
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EMBL­EBI
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MeSh thesaurus
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UMLS
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Gene Synonym Finder, SBO, ...
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11. Resources (cont.)
Existing resources (cont.)
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Lexical resources
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Webservices
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Entrez
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Taverna
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SBO
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12. Resources (cont.)
I am partially developing tools for
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Named entity recognition
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Relation extraction
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I am fully tackling
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PPI mining
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Word sense disambiguation
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Nominalization
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I may have to tackle in future
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Contradiction, negation, contrasts
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Temporal text mining
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13. 2. What I still need to learn ­
Specific
There may be gaps I am unaware of
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Less of wheel reinvention
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Use other software
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Lingpipe, NLTK, Weka, RASP, ABNER, PIE,
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BIOINFER, MALLET, Julielab, SPECIALIST, EMBL­
EBI, GNN (Arizona Uni),
Use other methods/approaches
–
Machine Learning
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Dynamic programming
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CL / Bio text mining theory algorithms
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Viterbi, HMM, NN, SVM, GA, CRF,
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...
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14. 2. What I still need to learn ­
Specific
Make a resources list on our web page?
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Similar to the Stanford – outdated
–
repository
–

15. What I still need to learn – Less
general
News of the field
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Areas/opportunities for research
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Michael Phelps analogy
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Developing skills for a CV
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Ways to proove I have the skills I already have
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Presenting results
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Reasons, occasions, methods
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Writing
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Other workshops by the faculty
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16. What I still need to learn ­
General
Writing, writing, writing
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Binge writing vs. Snacking
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Write as you go
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Closer to the final output
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Paper­based dissertation? Something to consider.
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Review, get feedback, rewrite
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A pedantic editor
–

17. What I still need to learn –
General (Cont.)
Stronger coding infrastructure
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More reusable libraries
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Config files
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One­click approach
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Optimisation
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Code
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Database
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Query optimization
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Database optimization
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Server
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Load balancing
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Multi threading
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Multi processor
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18. 3. Outputs so far
Written
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Background work survey
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Mid April 2008
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5 pages (approx. 1000 words)
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Feedback from supervisor
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Never was written up
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Writing sample for CS7100 seminar
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June 2008
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Same document as above, revised and rewritten
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12 pages, 2215 words
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Feedback from Jim Miles and peer students
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19. HIV
Understanding of the problem and the goals
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Presenting the given/wanted as tables/code/
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query
Building code infrastructure
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Database tables
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Utility libraries
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Version control system
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1500+ lines of documented, reusable code
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20. HIV summary
Goal: to reproduce a human­produced table
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Each row has the following main columns
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HIV GPN (protein name, acc, and gene ID)
–
Human GPN (protein name, acc, and gene ID)
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A relation (interactoin) between the two
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A description of the interaction
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The PMIDs that the interaction has been
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reported in
The raw input: the full abstracts
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21. HIV results
HIV and human GPN names
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Most where mapped to their entities
–
1237 out of 50416 currently unmapped (2%)
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Interaction verbs
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Interesting verbs and stems identified
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The stems where found in the text
–
Working on stems, so including nominals, etc.
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Terms extracted from the interaction
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descriptions in the original data

22. Example
SELECT DISTINCT mention FROM
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index_description_term i where
termID=28;
18 variations
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CD4+ T T4 (CD) CD4+T
CD4­, T T4(CD) T (CD4)
T CD4 CD4 (T) CD4+ (T)
CD4(+) T CD4(+)T CD4(T)
CD4 T CD4+­T CD(4+) T
T4+ (CD) CD4(+)­T CD4­ T

23. Example
SELECT DISTINCT mention FROM
●
index_description_term i where
termID=28 or termID = 17;
28 variations
●
CD4+ T T4(CD) CD4+ (T) CD4(+) T cell
CD4­, T CD4 (T) CD4(T) CD4 T­cell
T CD4 CD4(+)T CD(4+) T CD4(+) T­cell
CD4(+) T CD4+­T CD4­ T CD4(+)T cell
CD4 T CD4(+)­T CD4+ T cell CD4+­T­cell
T4+ (CD) CD4+T CD4­, T cell CD4(+)­T­cell
T4 (CD) T (CD4) CD4+ T­cell CD4 T cell

24. HIV results
POS tagging with GeniaTagger
●
Parsing with Stanford parser
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Haven't used this data yet
–
Working with sentences as units
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Normalising terms
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Tables of synonyms
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Tables of verb stems and terms
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Indexes with mention/offset pairs
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25. HIV results
Looking for sentences that share all these
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properties with any of the goal table rows
A human­HIV pair of GPN
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A verb phrase containing a word with the same
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stem of the interaction verb
Any description term(s)
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Very high recall (few false negatives)
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Not­so­high precision (numerous false
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positives)
Optimisation for more complicated queries
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26. HIV next steps
Compare with other PPI mining and GPN
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recognition tools
Find optimum parameters
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Presentable results
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Integrate with the interaction ontology
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Evaluate, compare, present, get feedback
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Apply to new papers
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Apply to new organisms
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Evaluate, compare, present, get feedback...
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27. Supervision
Good points
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Moving away from theory to tackling real
–
problems very quickly
Micromanagement while I am free to manage my
–
own time and other preferences
Planning ahead, causing commitment
–
Providing common sense, insight, and savvy
–

28. Supervision – good points
(cont.)
Providing good starting points while not ruling
–
out my own ideas
Good meeting frequency
–
Group meetings?
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General support
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Addressing my needs
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Financial
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Research interests and preferences
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29. Supervision
Could be improved
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Minutes were not always thorough
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Same for tasklists
–
We could have agenda for the meetings
–
I write a list of the things that I want to discuss each
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session
Like the one I had for this report–could have been
●
there when I presented my 3­week plan
Same for TEAM meetings and HIV meetings
–
I hope we keep tackling real problems in
●
future

30. Plan
End of August
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Presenting HIV output to the group
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Writing HIV results
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Sep
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Moving to new accommodation (11­20 Sep.)
–
Moving on HIV
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Applying the ontology
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Mining new corpora
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Generalising?
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31. Plan
Oct
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Writing up HIV
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Possible publicatoin
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Ideas for PhD research
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Nov
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Finalise MPhil vs. PhD
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Finalise PhD research area
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Work on end of year report
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Dec
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Write up EOY report
–
EOY Viva
–

32. References
Ananiadou, Sophia, and John McNaught. 2006. Text Mining for Biology
●
and Biomedicine. Norwood: Artech House, Inc.
Spasić, Irena. Some Web Services relevant for biomedical applications.
●
(Presentation slides.)
Zhou, GuoDong, Jie Zhang, Jian Su, Dan Shen, and ChewLim Tan,
●
2004. Recognizing names in biomedical texts: a machine learning
approach. Bioinformatics. Vol. 20 no. 7. Pp. 1178­1190