Data mining in bioinformatics: problems and challenges

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Data mining in bioinformatics problems and
challenges
Sorin Draghici
Email sod_at_cs.wayne.edu WWW http//vortex.cs.wayn
e.edu http//www.cs.wayne.edu/sod

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Why bioinformatics?

• We are witnessing a "biotechnology revolution"
• Biotechnology
• has the potential to improve our lives
  dramatically (new drugs, treatments, etc.)
• has also a huge distructive potential (careless
  genetic manipulations, etc)

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Why bioinformatics?

• Human genome project
• completed by Celera
• How is that to be used?
• map functions on genes
• find/treat/correct/eliminate genetic diseases
• gene treatment
• patient oriented treatment and drugs
  (pharmacogenomics) ACE inhibitors (blood
  pressure medication

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The HIV virus

• HIV is a retrovirus that attacks the immune
  system
• Replication mechanism
• RNA based
• makes lots of mistakes during the replication
• Compensates for the primitive replication through
  a high replication speed

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Why is it so deadly?

• 10 billion copies of HIV are produced every day
• High replication speed
• Many random mutations
• Selection pressure from the drug
• very good search ability in the version space of
  all viable HIV viruses

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Current treatments

• Protease inhibitors
• Reverse transcriptease inhibitors

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Current problems

• Very few drugs available
• 5 FDA approved protease inhibitors
• 9 FDA approved RT inhibitors
• Cross-resistance
• patient treated with drug A may develop
  resistance to drug B as well

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Current problems

• Drug development is
• very slow (10 years)
• very expensive (10-30 milion/year)
• Viral mutations are
• very probable in each generation
• very rapid (10 billion copies a day)
• The result

throwing stones at fighter planes
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Our approach

• Find the structural features which
• cause drug resistance
• are common to several mutants
• Design drugs to counteract such common features
  as opposed to individual mutants
• secondary therapy

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effective
mutant HIV
wild type HIV
less effective
drug development
option 1
mutant HIV 1
option 2
mutant HIV 2
wild type HIV
resistance
genotyping
option 3
mutant HIV 3
first antiretroviral therapy (FAT)
second antiretroviral therapy (SAT)
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Our data

• Genotypic data (genetic sequences of mutants)
• easy to obtain
• there are lots of them
• Structural data (X ray crystallography)
• difficult to obtain
• not very many
• Phenotypic data (drug resistance)
• very difficult to obtain
• very few available

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Our data

• Genotypic data
• PQITLWQRPLVTIKIGGQLKEALLDTGADDT... (approx. 200
  residues for protease)
• Structure data
• Phenotypic data
• IC90 3.51
• fold resistance IC90 mutant/IC90 wildtype

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Our work

• Develop a structure-function model of HIV drug
  resistance

sequence
resistance
structure
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Dataflow
Sequence
Contacts/PDB
Structures
Machine learning
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Supervised learning

• Inputs
• Atomic contacts between the inhibitor and the
  protease
• Atomic distances
• Output
• Fold resistance

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Ligplot Contacts File
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Atomic contacts - resistance
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Unsupervised learning

• Inputs
• Contact residues (21 distinct contacts)
• Output
• A self organized map embedding structural
  information

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Ligplot Contacts File
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Self-organizing feature maps
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Residue contacts - resistance
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Problems and challenges in bioinformatics

• Insufficient data
• Example
• Largest data set has 50 mutants
• Why?
• The field is very recent
• Data collection can be very difficult (one
  structure may take 1-2 years if done from
  scratch one IC90 value may take up to two weeks)
• Data has commercial value
• Solutions
• Get more data
• Cross-validate very carefully

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Problems and challenges in bioinformatics

• Data consistency
• Example
• Same sample sent to two different labs can come
  back with different IC90 values
• Why?
• The experimental tools are not mature yet
• Solutions
• Select your data carefully
• Use data from consistent sources
• If not possible, pre-process the data to make it
  consistent (not very good since you actually
  change the data!)

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Problems and challenges in bioinformatics

• Data accuracy
• Example
• Same sample sent to the same lab at different
  times can be reported with different IC90 values
  (4 fold error)
• Why?
• The experimental tools are not mature yet
• Solutions
• Use relative values to reduce the requirement for
  high numerical precision
• Map data into clusters and attach values to
  clusters (1-4 no resistance, 4-10 reduced
  resistance, gt10 resistance)

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Problems and challenges in bioinformatics

• Data quality
• Example
• Papers reporting IC90 values do not give the
  whole sequence
• Why?
• People are not aware of its importance
• Data may have commercial value
• Solutions
• Never trust your data...

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Problems and challenges in bioinformatics

• The choice of features
• Example
• Atoms?, Residues?, Genes?, Larger structures?
• Why?
• The phenomena are very complex and span different
  scales in time and space
• Solutions
• Try to merge different types of data in order to
  capture the complexity of the phenomenon
• Use several qualitatively different analysis and
  machine learning techniques

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Problems and challenges in bioinformatics

• Lack of tools
• Example
• There were no tools able to correlate
  sequence/structure/resistance data for the HIV
  virus
• We wrote more than 15,000 lines of code for this
  problem
• Why?
• The field is new
• The structure/function problem is just starting
  to be addressed
• Solutions
• Develop your own software
• Partnerships with bioinformatics companies?

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Problems and challenges in bioinformatics

• Difficult communication between the "bio" and the
  "informatics" sides
• Example
• Definition of "successful prediction"
• Why?
• Different backgrounds, different traditions
• Solution
• Cross-training
• Exposure to "the other" field

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Conclusions

• Data mining in bioinformatics is
• Challenging
• Interesting
• Useful