Concept Modeling in Bio-informatics

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Concept Modeling in Bio-informatics

• Sanida Omerovic, Saso Tomazic,
• Mateo Valero, Milos Milovanovic, David
  Torrents
• University of Ljubljana, Slovenia
• UPC, Barcelona, Catalonia
• 
  IPSI Firence-2007

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WHAT IS CONCEPT ?
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Decision Making Algorithm
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Concept Modeling Layer

• What is concept?
• How is it modeled?
• How is it built?
• How is it exploited?
• How is it updated?

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Classification of concept modeling (CM) and
decision making systems (DMS)

• This classification is made based on the
  following assumption
• Any decision making system, regardless if the
  process is performed entirely by humans,
  supported by machines or totally automated, is a
  layered process, with one layer
• (explicit or implicit) which can be called
• Concept Modeling Layer

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Purpose (DMS)

• General
• Specialized (Bio-informatics)

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Bio-informatics

• Genomic researchers mostly deal with
  similarity issues between genomic sequences.
  Genomic sequences are treated as long sequences
  of letters
• A (adenine)
• G (Guanine)
• C (Cytosine)
• T (Thymine)
• which represents nitrogenous bases in protein
  structure.

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DNA sequence
DNA sequence is presented as an array of letters
which are mapping the nucleotides in DNA
(consisted of one of four types of
nitrogenousbases A/G/C/T, a five-carbon sugar,
and molecule of phosphoric acid).
(A)
(T)
(G)
(C)
DNA chemistry compound
DNA sequence
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DNA sequence analysis

• GATTCATCGA CCATCAAAT GATT

Useful data
Noisy data
Start sequence
Start sequence
End sequence
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Bio-informatics in DMS

• Sequence concept
• (still impossible/there is no protein
  conceptual model)
• Sequence analysis (software BLAST, Smith
  Waterman, FASTA, etc)
• Sequence retrieval (easy/ available for free on
  the WEB ENSEMBL.ORG, NCBI, UCSC, etc.)
• Sequencing (hard/laboratory work on the level of
  chemical reactions to conclude weather C/T/G/A is
  in question in DNA chain)

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Sequence analysis

• In the example shown at next two figures, one can
  see a fraction of the results obtained from a
  BLAST comparison of protein SLC7A7 (human)
  against a SwissProt database of proteins.
• We selected two illustrative examples that show
  from a perfect (word) mach to a similar mach.

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BLAST Sample session, perfect match

• gtgi12643348spQ9UHI5LAT2_HUMAN
  lthttp//www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd
  RetrievedbProteinlist_uids12643348doptGenPe
  ptgt Gene info lthttp//www.ncbi.nlm.nih.gov/entrez/
  query.fcgi?dbgenecmdsearchterm126433485BPUID
  5Dgt Large neutral amino acids transporter small
  subunit 2 (L-type
• amino acid transporter 2) (hLAT2)
• Length535
• Score 665 bits (1717), Expect 0.0, Method
  Composition-based stats.
• Identities 332/332 (100), Positives 332/332
  (100), Gaps 0/332 (0)
• Query 1 MGIVQICKGEYFWLEPKNAFENFQEPDIGLVALAFL
  QGSFAYGGWNFLNYVTEELVDPYK 60
• MGIVQICKGEYFWLEPKNAFENFQEPDIGL
  VALAFLQGSFAYGGWNFLNYVTEELVDPYK
• Sbjct 204 MGIVQICKGEYFWLEPKNAFENFQEPDIGLVALAFL
  QGSFAYGGWNFLNYVTEELVDPYK 263
• Query 61 NLPRAIFISIPLVTFVYVFANVAYVTAMSPQELLASN
  AVAVTFGEKLLGVMAWIMPISVA 120
• NLPRAIFISIPLVTFVYVFANVAYVTAMSP
  QELLASNAVAVTFGEKLLGVMAWIMPISVA
• Sbjct 264 NLPRAIFISIPLVTFVYVFANVAYVTAMSPQELLASN
  AVAVTFGEKLLGVMAWIMPISVA 323
• Query 121 LSTFGGVNGSLFTSSRLFFAGAREGHLPSVLAMIHVKR
  CTPIPALLFTCISTLLMLVTSD 180
• LSTFGGVNGSLFTSSRLFFAGAREGHLPSV
  LAMIHVKRCTPIPALLFTCISTLLMLVTSD
• Sbjct 324 LSTFGGVNGSLFTSSRLFFAGAREGHLPSVLAMIHV
  KRCTPIPALLFTCISTLLMLVTSD 383
• Query 181 MYTLINYVGFINYLFYGVTVAGQIVLRWKKPDIPRPIK
  INLLFPIIYLLFWAFLLVFSLW 240
• MYTLINYVGFINYLFYGVTVAGQIVLRWKK
  PDIPRPIKINLLFPIIYLLFWAFLLVFSLW
• Sbjct 384 MYTLINYVGFINYLFYGVTVAGQIVLRWKKPDIPRP
  IKINLLFPIIYLLFWAFLLVFSLW 443
• Query 241 SEPVVCGIGLAIMLTGVPVYFLGVYWQHKPKCFSDFIE
  LLTLVSQKMCVVVYPEVERGSG 300
• SEPVVCGIGLAIMLTGVPVYFLGVYWQHKP
  KCFSDFIELLTLVSQKMCVVVYPEVERGSG

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BLAST Sample session, similar match

• gtgi12643378spQ9UM01YLA1_HUMAN
  lthttp//www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd
  RetrievedbProteinlist_uids12643378doptGenPe
  ptgt Gene info lthttp//www.ncbi.nlm.nih.gov/entrez/
  query.fcgi?dbgenecmdsearchterm126433785BPUID
  5Dgt YL amino acid transporter 1 (y()L-type
  amino acid transporter
• 1) (yLAT-1) (YLAT1) (Monocyte amino acid
  permease 2) (MOP-2)
• Length511
• Score 257 bits (656), Expect 4e-68, Method
  Composition-based stats.
• Identities 138/315 (43), Positives 203/315
  (64), Gaps 10/315 (3)
• Query 2 GIVQICKGEYFWLEPKNAFENFQEPDIGLVALA
  FLQGSFAYGGWNFLNYVTEELVDPYKN 61
• GIV G E NFE
  G ALA FY GW LNYVTEE P N
• Sbjct 202 GIVRLGQGASTHFE--NSFEG-SSFAVGDIALA
  LYSALFSYSGWDTLNYVTEEIKNPERN 258
• Query 62 LPRAIFISIPLVTFVYVFANVAYVTAMSPQELLA
  SNAVAVTFGEKLLGVMAWIMPISVAL 121
• LP I ISPVT Y NVAY T
  LASAVAVTF G WIPSVAL
• Sbjct 259 LPLSIGISMPIVTIIYILTNVAYYTVLDMRDIL
  ASDAVAVTFADQIFGIFNWIIPLSVAL 318
• Query 122 STFGGVNGSLFTSSRLFFAGAREGHLPSVLAMIHVK
  RCTPIPALLFTCISTLLMLVTSDM 181
• S FGGN S SRLFF GREGHLP
  MIHVR TPPLLF I L L D
• Sbjct 319 SCFGGLNASIVAASRLFFVGSREGHLPDAICMIHV
  ERFTPVPSLLFNGIMALIYLCVEDI 378
• Query 182 YTLINYVGFINYLFYGVTVAGQIVLRWKKPDIPRPI
  KINLLFPIIYLLFWAFLLVFSLWS 241
• LINY F F G GQ
  LRWKPD PRPK FPI L FL LS
• Sbjct 379 FQLINYYSFSYWFFVGLSIVGQLYLRWKEPDRPR
  PLKLSVFFPIVFCLCTIFLVAVPLYS 438
• Query 242 EPVVCGIGLAIMLTGVPVYFL--GVYWQHKPKCFSD
  FIELLTLVSQKMCVVVYPEVERGS 299
• IGAI LGP YFL
  V P T Q C V E

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BLAST outputScore 257 bits (656), Expect
4e-68, Method Composition-based
stats.Identities 138/315 (43), Positives
203/315 (64), Gaps 10/315 (3)

• BLAST expresses the level of similarity between
  query sequence and database sequence in terms of
  Score, Expectations, Method, Identities,
  Positives, and Gaps. Here is where our DMA layer
  is finishing, and from this point inferring need
  to be done by researchers on the bases of
  software (ex. BLAST) output, and knowledge
  gathered elsewhere (book, computers, brains).
• Also, a forthcoming challenge in the field of
  comparative genomic analysis is to compare large
  amounts of genomic data (letters).For example,
  if one wants to compare one mammalian genomic
  sequence against all existing mammalian
  sequences, one would need a database with memory
  storage of 60 GB (Saragasso Sea project).

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Application for text analysis

• Frequency (number of occurrences)
• Distance
• --------------------------
• Exclude stop word lists (and, if, or etc)
• Stemming (traveling gt travel traveled gt
  travel)
• Synonyms (sick ill)
• Visual Basic

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Home-made Brandy Production

• Grape-gathering is the first phase in the
  production of brandy, through it might be made
  also from plums, figs, pears or cornel berries.
  The gathered grapes are crushed and then poured
  into wooden barrels. They are mixed several times
  a day, the more often the better. The obtained
  mass is called wine-marc. The process of
  alcoholic fermentation usually lasts fifteen or
  thirty days. When it is finished, or when, as
  usually people say the marc is still,
  distillation begins i.e. the making of brandy,
  which is done in special copper cauldrons. Hand
  made copper cauldrons can still be found in
  Tuscany households

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word word frequency distance

• brandy grape 10 0
• brandy alcohol 4 1
• brandy distillation 3 3
• brandy strength 3 5
• brandy making 2 5

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Concept criteria

• Frequency gt 5
• Distance lt 2
• Concepts
• brandy grape
• brandy alcohol
• Transcription
• brandy - made of - grapes
• brandy - kind of - alcohol

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Concept Modeling layer

• Implicit (concepts are not explicitly mentioned)
  
• Protein
  conceptual model
• Explicit (concepts are explicitly mentioned
  and/or defined)
• 
  Frequency gt 5
• 
  Distance lt 2

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Concept definition (CM)

• Node in concept network
• (semantic web)
• Node in concept web

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Concept definitons

• Structure that carries meaning.
• Needs other concepts and relations among them to
  be defined. Without relations concept can not
  exist.
• Relations between concepts can also be observed
  as concepts.
• All concepts can be related among each other,
  forming whether
• 1. concept web (where relations are concepts
  also)
• 2. concept network (where relations are not
  concepts)

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Concept Network Concept Web
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Concept Modeling Learning Module
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Thank you for your attention! Questions?