文脈解釈機能および意味的分析機構による腸内細菌叢-ヒト属性関連性の統合的抽出方式
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(3) F ? G " - (,PN,A#;. 1* ×´ àř 1,a) xĄ £ř 2 ĜĄ Õ 1 4 I S Ćŀ $ƩţŐŧ¡LeG ²ʼndâdÑƦ! #SMÉâ #ƚâ" ƩúŢŵƔĒš å¬Ķ ĈĒĎ4ĪŔ¦Ķ"î1ýÖ4öċ1ƪƉÑƩţŐŧ¡#ƚâ4ĥĶ"î1 ". Ʃ nij&#ƃĤ ăÚ2 1 ƩĦ³$l"lÔĐĈ".0LeGy#© /²ʼn-ÑƦ! #SM# Éâä· ţŐŧĿ#½( !ƚ4ô1 " ( 1ƪĆýÖ $ƩŐŧÁ#ũĺł /S\e_ EK6=E SMÉâ"ƚ1ĢÂ#ţŐŧĿ#ő)¦34úŢ ƩŕśĶ"Ůù#=^EG_c>5` A_FYƧK-means =^EG_c>ƩƜÊĶ=^EG_c>ƨ4ÃũdŒĉĖƇ4ũ ".0ƩĂå!ţŐŧ ¡ƚâ4SM#yĶÉâä· ţŐŧĿ#ùĶ© î1ƪĆýÖ#ĢÝ$Ʃ=^EG_c>5` A_FY#ĢÝƩ.%ƩÄƂLeG"¦3āƏ5`A_FY4{ĪĈ4ũ ".0ƩĂå!ţŐ ŧ¡-SMÉâƚƌâ4Ü1 4¢š 1Ğ"1ƪĆŀ $Ʃ3 ²ƧUSA, Malawi, Venezuelaƨ#ŬƤş 60 n 1254 Ŀ#ŐŧLeG4ÄƂ Ʃ²ʼn"ƚ1úŢ"{ĪÃƤ4ũŒĉƩāƏ5`A_FY ƜÊĶ= ^EG_c> Ɛí2ƩUSA "É1SM#) ª(21=^EGä·ƧBacteroides Prevotella #Ȧƨ. ţŐŧ¡-SMÉâƚƌâ î21 4ļƪ. Integrated Human Gut Microbiome Analysis System with Context-Awareness and Semantic-Analysis Functions SHIORI HIKICHI1,a) SHIORI SASAKI2 YASUSHI KIYOKI1 1. . Ã|4Ī ĆýÖ#Ăâ%ÃĦ¢šâ4Čż1ƪ. SMţŐŧ¡ $ƩyŐŠù4Ǝ " ù#Őŧ#. SMÉâ"ƚ1ĢÂ#ţŐŧĿ#ő)¦34. Ɲ¦y4ļƩĩy"ď!ÙƠ4i 1 Ĺ/. úŢ źÂƩŕśĶ"Ůù#=^EG_c>5`A. 2 1[1]ƪƉÑƩējrCe?cCc>ƧNGSƨ".0. _FYƧK-means =^EG_c>ƩƜÊĶ=^EG_c>ƨ. ŧ¡#ŵĈìŪ ƍ)ƩţŐŧ¡#įÏ Ĵ-ŋÈı[2]Ʃ. 4ÃũdŒĉĖƇ4ũ Ʃā,Ăå!ţŐŧ¡ƚ. ĝIJâ½ţĝ[3] fƒ#İã #ƚƌ ·«2. â4SM#yĶÉâä· ţŐŧĿ#ùĶ© . 0Ʃ“Microbiomarker” fĿ#ĩyñĐ Ěķ. î1ƪĆýÖ4Ī1 ".0ƩţŐŧ¡. 2 1ƪ. SMÉâ#ƚ" ïĶ!ħŵ ƍ)ƩÅćĶ". ţŐŧ¡ SMÉâ#ƚâ4ĥĶ"î1 . $ŐŧÁĶŴĞ /ü!ėiję#Ļń"Ř 1 ăÚ. ". Ʃnij&#ƃĤ ăÚ2 1 ƩƑt. 21ƪ. ¿!. ĆýÖ".0ƩŐŧÁ#ũĺł /S\e_EK6=E. #@QYƓä·4ë@QX=E-ƩmRNA #ĵ. ĦģĘ4ëM^cE=_UMX=E!. #p#:XI=. EŵĈ"Ė'1 ŵĈêę !
(4) ƩĦ³$l"lÔ. 2. P N < =. ĐĈ".0yLeG#© /²ʼn-ÑƦ!. #SM. Ħ³ƩţŐŧ¡ŵĈ" ƩUniFrac ƅƞ[7]4{Ī. #Éâä· ţŐŧĿ#½( !ƚ4ô1 " . lÔĐĈ[8] lě ! 0ƩyLeG#©. ( 1ƪp#:XI=EŵĈ $ƩqÎ1ä·, ª+LeGĈêę-#ĺłéĉ4ŨŁË½!. /²ʼn-ÑƦ!. #SM#Éâä· ţŐŧĿ#ƚ. [9,10,11]4ô1 ¢š ! 1ƪŤ½!ţŐ. LeGVeE 0ƩLeG#Ă-Ī4ũď. ŧ¡LeG4îć1ŭŚ ƩNGS Ò
(5) {Ī2. !Ŕ¦Ķ!ŵĈ ¢š ! 1+ƩţŐŧ¡ŵĈ. 1 ƩOMIM [12]- GAD[13]ƩGene Expression Omnibus. $Ŕ¦Ķ!ŵĈêę#ÃĦ ½ !ƀƢ ! 1ƪ. Ƨ. ƩĆŀ $ţŐŧ¡"ƚ1LeGVeE4Ä. Ƨhttp://www.ebi.ac.uk/microarray-as/ae/ƨƩStanford Microarray. Ƃ úŢŵƔĒš[4].%å¬ĶĈĒĎ[5,6]".. Database Ƨ http://smd.stanford.edu/ ƨƩ GO Ƨ http://www.. 1ţŐŧ¡-SMÉâƚƌâ#Ŕ¦ĶîýÖ4öċƩ. geneontology.org/ƨ!. 1. 2. a). çèŝ¹½ÁĨºä·Áƒ Faculty of Environment and Information Studies, Keio University çèŝ¹½Á÷ŇdZL65峼 Graduate school of Media and Governance, Keio University [email protected]. © 2015 Information Processing Society of Japan. http://www.ncbi.nlm.nih.gov/geo/. ƨ Ʃ. ArrayExpress. #p#Ƒt¿LeGVeE"Ė'1. ƩŨŁ2 1LeGù Ç!
(6) ƩþÀ#ţŐŧ¡ ŵĈJe` 1 QIIME[14] ,ŵĈCEKY#Ƙĵ ½ !ƀƢ ! 1ƪ. 96.
(7) WebDB Forum 2015. NGS #LeGŵĈ#ŅfĕƜ ƩÂƗĶ!ŐŧLeG. ļƪ. #ƚƌ 4ũğ#ē ŗÆ Ʃ=^EG_c>. !. S M É â L e G V e E Ƨ Human Attributeƨ ƨ : ƚƌ. fŦĶ"{Ī2 0ƩLeGVeEƖ ,=^E. ĺł /î 60 n#²ʼndâdÑƦ!. G_c>4Ī¼ď!LeGĈýÖ öċ2 1. 11 Ŀ#SM#Éâ ĊŎ2 1[11]ƪ. [15,16]ƪ. #. Ő ŧ L e G V e E Ƨ Bacteriaƨ ƨ : 60 n#SM#Ō. !. fýƩLeGW7Pc>#Ɩ $Ʃy#( !. /Č2 N ĿƧN=1254ƨ#ţŐŧ#Č. ©4ô1.0,ƩúŢŵƔĒš".1ƙõĶ!ĈÄ. ¦4ļ[11]ƪÂƗĶ!ĖƇÄƂ Ʃ_eNù. ƂLeG#ñÂ4ũƩå¬ĶĈĒĎ".1ƒĶ"Ģ. ƧējrCe=9cBe /Ü/2Ƒt¿ûġ#. ©î4ũ ¼
(8) ƩĂâ ļ2 1. Ćùƨ"Ä1_eNù#ȦƧ%ƨ ĊŎ2. [4,5,6]ƪ2( #ĺł $ijƖ#N<\ZcMŜ". 1ƪ. ƚ1å¬ĶĈĒĎ#ƏĪ ũ32 [17,18] Ʃţ. ú Ţ L e G V e E Ƨ Contextƨ ƨ : ƚƌĺł /î. !. Őŧ¡"ƚ1LeGVeE&#Əá$=^EG_c>. 2 Ŀ#ŐŧĿ#ő)¦3 ĩéƩ². êę"}ÀĶ 0ƩŮù#=^EG_c>êę4Ī. ʼnƩŖÓƩâ"ƚ1 3 Ŀ#úŢ ĊŎ2 . Ŕ¦Ķ!ŵĈCEKY#ÃĦ$2 ! [19]ƪ. 1 [11,20,21] ƪ ú Ţ " . 0 {N*(N-1)}/2 (=1254*1253. Ćŀ öċ1ţŐŧ¡-SMÉâƚƌâ#Ŕ¦Ķî. =1571262) Ɗ0#ő)¦3#k / 1 Ɗ0#ő). ýÖ $Ʃøͱ-ōŔđ4Ī y#©4¢Ų. ¦34î1ƪ. g Ʃā,Ăå!ţŐŧ¡-SMÉâ#ƚƌâ4ţ ŐŧĿ#ùĶ© SM#yĶÉâä· î 1ƪĆýÖ $ƩfŦĶ"{Ī2 1=^EG_c>. Human Attribute ! ID. int. 5`A_FY 1 K-means =^EG_c>%ƜÊĶ=. • Sex. text. • Age. float. ^EG_c>".1Ĉ4ũ ƩúŢ".0ñÂ. • Host. text. • Material. text. • Target. text. • Latitude. float. 2LeG#Í"ƚ3/Ʃ=^EG_c>5`A_F. `A_FY#ĢÝ#f Ʃ=^EGù#ñÂÛƩ. • Longitude float • Country. text. • Family. text. • Rundate. date. Context. int. ! Bacteria 1 float ! Bacteria 2 float. { Bacteria 1 (int) , Bacteria 2 (int) } { Bacteria 2 (int) , Bacteria 3 (int) } { Bacteria 3 (int) , Bacteria 4 (int) }. ! Bacteria 3 float. . . { Bacteria N-1 (int) , Bacteria N (int) }. . ƌâ4Ü1 ¢š !1ƪyĶ!=^EG_c>5. ! ID. . Y#ĢÝ-ÄƂLeG"¦3ţŐŧ¡-SMÉâƚ. Bacteria. ! Bacteria N float. K-means =^EG_c> $=^EG#LeGù Ɖu ± 1 ER ±. !1#"ÄƩƜÊĶ=^EG_c> $ƅƞũ" ¶ =^EG#ó04ũ+Ʃ=^EG#Le. ID. SexAge Host Material Target Latitude Longitude Country. Gù Ɖu $ƛ/! =^EG"ª(21L. 4489001 M 10 Human Feces. V4 38.64699. 4489002 F 49 Human Feces. V4. eGù#Ì ò/21ƪK-means =^EG_c>#Ģį. 4489060 M 78 Human Feces. V4 5.410833. Ķ!¯ƢĞ 6 ľÓ#wŊÓ ò/21 ƩĆ ýÖ $ŊÓż#+Ʃù°Ƨ25 °ľÓƨ#=^EG _c>4Ãũg #Œĉ4 ÿ1ƪƚƌĺł".0 Ü/2ĹŰ4S\e_EK6=E Ī1 " .0Ʃœ0ƈ(2LeG&#yĶ!5UaeH ¢š. -15.38. -90.225 35.3. USA Malawi. -67.609. Family. Rundate. Daughter 7/25/2011 Mother. 8/1/2011. Venezuela Father 7/25/2011. (a) ID. Bacteria 1 Bacteria 2 Bacteria 3 (Bacteroides) (Prevotella) (Clostridium) 4489921 0.0499324 45.7239709 2.4636939 4489910 0.689054 43.8654007 5.27736 4489371 1.4049097 39.9688861 6.0859655. Bacteria N-1Bacteria N 1.8676578 6.5054534 1.8537779 0.00838 1.1551756 0.059875. (b). !0ƩĆýÖ#nij&#ƃĤ ăÚ21ƪ ID. 3. F ? G " - (,PN,A#;. 1*4I 3.1 5 M. Bacteria 1 Bacteria 2 Bacteria 3 Bacteria Bacteria (Bacteroides), (Prevotella), (Clostridium), N-2, N-1, Bacteria 2 Bacteria 3 Bacteria 4 Bacteria Bacteria (Prevotella) (Clostridium) (Bifidobacterium) N-1 N country 1 0 0 0 0 latitude 0 0 0 1 0 sex 0 0 0 0 1. ĆýÖ $Ʃƚƌĺł /SM#²ʼn #ƚƌâ ļ®. (c). 2 1 2 Ŀ#ŐŧĿ#ő)¦34²ʼnúŢ Ʃ. ± 2 {ĪLeG|: (a) SMÉâLeGƩ. R^ZeGe#k /ĢÂ#R^ZeGe4îƩĈ. (b) ŐŧLeGƩ(c) úŢLeGƪ. ÄƂ 1ƒŃƙ4Ɛí1ƪÃĦ| ƩÄƂLe G4Ƙ2 1 3 ² 60 n#ţŐŧ#ȦLe. 3.2 0 E & B ! 2 P /. G [11] Ʃ 3 Ŀ#LeGVeE4ÄƂ 1ƪER ±. ĆýÖ#úŢLeGVeE" Ʃ¥úŢ4sh#.. ¥LeGVeE"ª(2 1LeG|4± 1Ʃ± 2 4. "Âŝ1ƪ. © 2015 Information Processing Society of Japan. 97.
(9) WebDB Forum 2015. ² ʼn ú ŢƧ countryƨ ƨ$ƩČŏ=9_4ñÂ1Ƨ|Ƭ. !. {country: Venezuela}ƨ Ʃ²ʼn ƚƌ 1 2. !. !. Ŀ#ţŐŧĿƧ|Ƭ{Bacteria1: BacteroidesƩBacteria2:. fextraction $ƩŐŧÁ#ũĺł /S\e_EK6 =E SMÉâ"ƚ1ĢÂ#ţŐŧĿ#ő). eG#ƛÂ4ũ[11]ƪ. ¦34ļúŢ4{ĪƩÄƂùLeG#ƛÂ4. Ŗ Ó ú ŢƧ latitudeƨ ƨ$ƩČŏ=9_4ñÂ1Ƨ|Ƭ. ũƩéØ2ùLeG41ƪLeG. {latitude: 38.64699}ƨ ƩŖÓ ƚƌ 1 2. Ĉş#ĹƁ"á <ebeN41 Ʃfextraction. Ŀ#ţŐŧĿƧ|Ƭ{Bacteria1: BacteroidesƩBacteria2:. ".0<ebeN fť1ŐŧLeG4î1ƪ. Firmicutes}ƨ4ñÂƩŐŧLeGVeE /ĈL. ´ħ-â"ƚ1ŐŧLeG$²ʼn-ŖÓƩâ. eG#ƛÂ4ũ[20]ƪ. úŢ".0Ɛí21ƪ. â ú Ţ Ƨ sexƨ ƨ $ƩČŏ=9_4ñÂ1Ƨ|Ƭ. úŢLeGVeEƧe.g. ²ʼn-ŖÓƩâƨ". {sex: M}ƨ Ʃâ ƚƌ 1 2 Ŀ#ţŐ. ƩúŢ".0ñÂ21Őŧ 2 Ŀ#ő)¦3 A={ai,. ŧ Ŀ Ƨ | Ƭ {Bacteria1: Actinobacteria Ʃ Bacteria2:. aj} <ebeN keyc 4źÂƩŐŧLeGVeE"ª. Bacteroidetes}ƨ4ñÂƩŐŧLeGVeE /. (2 1fƌ#ČŐŧ¦LeG B={b1, b2, …,. ĈLeG#ƛÂ4ũ[21]ƪ. bx}4ĠƩfextraction ".0hŹ#."RGecſƁ. ± 3 $ţŐŧ¡-SMÉâƚƌâ#Ŕ¦ĶîýÖ# #Ĉƚù".0ÃĦ1ƪĆýÖ#ĢÝ$ē# 2 Ğ . 4Ī ŐŧLeG4î1ƪ fextraction (keyc, A, B){be | be = akeyc} ƨ : fnormalization (2) Ĕ ű Ƨ Normalizerƨ fnormalization $ƩĈÄƂùLeG"Ä Ʃ2 Ŀ. 1ƪ. 2.. fextraction. Prevotella}ƨ4ñÂƩŐŧLeGVeE /ĈL. čĭ±4ļƪúŢ".1LeGĈ4ũĆýÖ$ 5 . 1.. ƨ: (1) L e G ñ ÂƧ Context-based Bacteria Data Extractorƨ. <ebeN".0úŢ ñÂ2SMÉâ4. #ĔűêęƧ#1: Y = (X – Xmean) / XS.D.Ʃ#2: Y = (X –. ÄƂ =^EG4ØéƩ#úŢ"Äá1. Xmin) / (Xmax – Xmin)ƨ4{ĪƩĔűÛ#ùLeG. ŐŧLeG#ĢÝ4î 1ƪ(ƩúŢ".0. 41ƪLeGĈş ŐŧLeG B ". ñÂ2 2 Ŀ#Őŧ#¦4øͱ-ōŔđ4Ī. á ƩĔűêę # 4 m ñÂ1 Ʃ. ¢Ų1ƪ. ŐŧLeG#Ĕű4ũƪ. SMÉâLeG#ĦơÓ4Īå¬Ĉ".0Ʃ. fnormalization ".0ƩhŹ#."ñÂĔűê. ąĹ#ţŐŧ¡-SMÉâƚƌâ4î1ƪ. ę4Ī ƩŐŧLeG4î1ƪ. Data Collection. Accumulating already-known data Context data (Bacteria species combination) Data Collector. Data Analysis. Analyst. Extracting unknown knowledge. Input1: Data Input2: Human-Microbiome-Relations DB Keyword Output: Context-based Bacteria Data Extractor Graphs Bacteria Data Normalizer. _FY4ĪŒĉŽ~ýę Ʃ=^EG"É. Human-Microbiome-Relation Visualizer. 1LeGù"1¥=^EG#LeGù#¦ Ƨ%ƨ Ʃ¥²ʼn"É1LeGù"1²ʼn%. (a) Data Collector Input: Context. Input1: Data. Human Attribute data. Bacteria data. Context-based Bacteria Data Extractor “latitude”. “country”. Bacteria Data Normalizer Y = ( X – Xmean) / XS.D.. =^EG "É1LeGù#¦Ƨ%ƨ#Ł4 Analyst. Input2: Keyword. Human-Microbiome-Relations DB Context data. =^EG_c>ƨ4 fclustering ".0Ãũ2Ʃ¥ùL cx}Ʃ± g øͱ-ōŔđ41ƪ¥5`A. Metadata Frequency Analyzer. Specialists’ Knowledge. c>5`A_FYƧK-means =^EG_c>ƩƜÊĶ eG É1fƌ#=^EGĮ¤ C = {c1, c2, …,. Cluster Analyzer. Web. fnormalization (m, be){bn} (3) = ^ E G _ c > Ƨ Cluster Evaluatorƨ ƨ : fclustering 2ùLeG$ŕśĶ"Ůù#=^EG_. “sex”. Output: Graphs. Y = ( X – Xmin) / ( Xmax – Xmin). Ī1 ".0Ʃā,ƥ4Ăå!Œĉ î 1ƪhŹ#.!āƏ=^EG_c>Ž~ýę4 Ī1 ".0Ʃ=^EG_c>5`A_FY "ĩé21=^EGLeGù#Ì%ŐŧLeG VeE"É1²ʼn #LeGù#Ì4Şæ1 ¢š !1ƪƟƜÊĶ=^EG_c> $. Cluster Analyzer. Clustering Algorithm 1 Clustering Algorithm 2 (e.g. K-means clustering) (e.g. Hierarchical clustering). Clustering Evaluator. Metadata Frequency Analyzer Human-Microbiome-Relation Visualizer Scatter Diagram Dendrogram. Metadata Frequency. (b) ± 3 čĭ±: (a) CEKYĎƋƩ(b) ĈTaeH[eM. © 2015 Information Processing Society of Japan. K-means =^EG_c>Ƨk=3ƨƩƟƜÊĶ=^EG_ c>Ƨk=3ƨ $]e=_INƅƞƩƕ)q еę ƧWPGMAƨ4Ī =^EG_c>4Ãũ21ƪ fclustering ".0ƩhŹ#."=^EGĮ¤LeG±4î1ƪ. 98.
(10) WebDB Forum 2015. fclustering (be){C, g} or fclustering (bn){C, g}. “weighted”ƨƩK-means =^EG_c>Ƨk = 3ƨ 4{Ī1ƪ.
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(14) . ×100(%). (4) å ¬ Ķ L e G W 7 P c > Ƨ Metadata Frequency Analyzerƨ ƨ : fmining fmining $Ʃfextraction - fnormalization ".0Ü/2Őŧ LeG fclustering ".0Ü/2=^EGĮ¤LeGƩ SMÉâLeG D={d1, d2, …, dy}4Ī Ĉ4ũƩ ¥=^EGZGLeG;8cMùƧZGLeG#. ± 4 ²ʼnúŢ4ĪÃŭŒĉ|. ĦơÓƨk ($± g 41ƪLeGĈş#Ĺ Ɓ"á SMÉâLeGVeE"À³1<eb eN keyh Ƨe.g. country, sex and ageƨ41 ". 4. ' R. .0Ʃ<ebeN".0ñÂ2Éâ"Ěķ. 4.1 ' R A: % > 0 E : Q ) ; . Ĉ4ũ ¢š !1Ƨ|Ƭ country #Ā" $ USA, Malawi, Venezuela 4ļƨƪ=^EG_c> #SMÉâLeGĻſĀ!.
(15) F ? G " - (,PN,. ĆÃƤ#ķĶ$Ʃ²ʼnúŢ4ĪţŐŧ¡-SMÉâ. Ʃ=^EGĮ¤LeG4. ƚƌâ4î1 ".0ƩSMÉâ ţŐŧĿ#ù. ßů !¸¦,Ʃfmining ".0=^EGĮ¤LeG. Ķ© ÂƗĶ"î1ĆýÖ#ÃĦ¢šâ4ļ. 4"ZGLeG;8cM4ũ ¢š . "1ƪŒĉ4± 5 "ļƪ. 1ƪ. Ĕűêę".1ÙƠ4ŞæƩĔű#:_DO`. fmining ".0ƩhŹ#."ZGLeG;8cMù-. LeG4{Īƪ²ʼnúŢ4{ĪĈLeG#ƛÂ. ±Ʃ<ebeNñÂĀ#)SMÉâLeG4î1ƪ. ÛƩ60 n#ŐŧLeG4ÄƂ ƜÊĶ=^EG_c>. fmining (keyh, B, C, D){k, dkeyh, g} or fmining (keyh, B, D){k, g} (5) ¢ Ų Ƨ Visualizerƨ ƨ : fvisualization fvisualization $ƩfclusteringƩfmining ".0Ü/2±4h Ź#."øͱ-ōŔđ ūļ1ƪLeG Ĉş$ fvisualization ".0ţŐŧ¡ SMÉâ#ƚƌâ 4ŲųĶ"Ļſ1 ¢š !1ƪ fvisualization (be, bn, C, k, dkeyh, g)screen. 4Əá1 ".0ōŔđ4zéƪ SMÉâ ţŐŧĿ#ùĶ© ÂƗĶ"î 1+"Ʃ²ʼnúŢ"ª(21 2 Ŀ#Őŧ#ő)¦3 4Ɔ Ʃ²ʼnÉâ #:_DO`LeG#øͱ :_DO`LeG4ÄƂ ƜÊĶ=^EG_c>Œ ĉ#LeGøͱ4zéƪ²ʼnÉâZGLeG4;8 cMŒĉƧĦơÓƨ".0 2 Ŀ#øͱ4ĖƇƩ ¥=^EG#ƕÞƧcentroidƨ ùĶ©4î. 3.3 ' H. 1ƪ2 Ŀ#Ĕűêę4Ī =^EG "LeGøÍ. 3.2 #ýÖ".0ƩUaMG7UCEKY#Ãŭ4ũ. ±4zéƪ. ƪùŷň-¢Ų"$ Numpy(http://www.numpy.org/)Ʃ. 2/#Œĉ".0Ʃ4 n#SM"ĢÝĶ!ŐŧLeG. Scipy(http://www.scipy.org/)ƩMatplotlib(http://matplotlib.org/). =^EG#Ʃ²ʼnÉâZGLeG4;8cMŒĉƩ4. 4{ĪƩøͱ-ōŔđ4zéƪ¥=^EG#Z. n USA "É1 Ļſ2 /ƩCluster. GLeG;8cMƧZGLeGĦơÓƨĈ"$ sqlite3. 1 $ USA "É1SM ðţŐŧ¡#ĢÝĶ!©4. 4{Īƪ ŐŧLeGVeEƩSMÉâLeGVeEƩúŢLe. ļ Ş / 2 1 Ƨ ± 5(d) ƨƪ = ^ E G ä · 1 Bacteroides Prevotella #Ȧ#е4Ļſ1 Ʃ. GVeE$ƚƌĺł4¶"zé[11]ƪĆCEKY $. Cluster 1 $ Bacteroides #Ȧ ƥ
(16) ƩPrevotella #Č. ŐŧLeGƩSMÉâLeGƩúŢLeG4fïƩ. ¦ w p#=^EG $į!1©4ļ. øͱ($ōŔđ!. Ƨ± 5(e)ƨƪ#Œĉ$ţŐŧ¡ USA "É1SM . #>^T41ƪ. ĆÃŭ $Ʃ²ʼnúŢ4źÂƩ²ʼn ţŐŧ¡# ƚƌâî4ž)ƪ 2 Ŀ#=^EG_c>êę Ʃ. © 2015 Information Processing Society of Japan. #Ĺ/2 !ƚâ4ļ 1 Ş/21ƪĦ³ #fŦĶ!ėij" $ƩSM#Éâ$Şæ2 ! ƩÃƤ A #Œĉ".0ƩÇ!
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(18) WebDB Forum 2015. Prevotella " $Ʃn ".0Əėiję Ʃ ± 5 ²ʼnúŢ4ĪƜÊĶ=^EG_c>".1ţŐŧ ²ʼn"į!1ėiję4öċć1¢šâ Ű/2ƪ. ¡-SMÉâƚƌâîŒĉ: (a) Ĕű"zéōŔđƩ. ƩÃƤƭ $ƩUSA "É1SM#ţŐŧ¡#. (b)²ʼnÉâ #:_DO`LeG#øͱƩ(c) :_DO`. ĢÝ4ô1 $¢š ! ƩMalawi - Venezuela. LeG4ÄƂ ƜÊĶ=^EG_c>4ÃũŒĉ#L. "É1SM" $ţŐŧ¡-SMÉâ#ƚƌâ4. eGøͱƩ (d)²ʼnÉâZGLeG4;8cMŒĉƧơ. î1 $ć! ƪ#+ƩƜÊĶ=^EG. ÓƨƩ(e)¥=^EG#ƕÞƧcentroidƨ. _c>4ĪţŐŧÊ-SMÉâƚƌâîýÖ$Ʃ USA "É1SM#ƚƌâî"$Ə 1 Ʃ§². 4.2 ' R B: K-means
(19) 2 @ 3 . ʼnúŢ".0¢Ų1 ć1 Malawi - Venezuela. 7L Q);8+. "É1SM" $fč"ƜÊĶ=^EG_c> Ə. ĆÃƤ $ƩK-means =^EG_c>".1Ĉ ƜÊ. 1 $Ŷ!ƪLeGƟ}ÀĶ!Ăå!ƚƌâî. Ķ=^EG_c>".1Ĉ#Œĉ4ĖƇ1 ".0Ʃ. 4ũ+"$Ʃ{Ī1úŢ#) $!
(20) Ʃĸķ1. =^EG_c>5`A_FY#ĢÝƩ.%ƩÄƂLeG. ²ʼnÉâ". ,=^EG_c>5`A_FY#ČŸ. "¦3ƧSMÉâ#ƨāƏ5`A_FYĪ#ÃĦ. ßů 1 Ş/21ƪ. ¢šâ" ļƪ± 6 "Œĉ4ļƪ. ÃƤ A $ƩţŐŧ¡#ùĶ© Ȧ. Ž~ýę Ʃ=^EG"É1LeGù"1. Ƨ%ƨ4ūļ1+ƩĔűêę".1v#»4Ş. ¥=^EG#LeGù#¦Ƨ%ƨ Ʃ¥²ʼn"É1L. æƩĔű#:_DO`LeG4Īƚƌâî4ũ. eGù"1²ʼn%=^EG "É1LeGù#. ƩƜÊĶ=^EG_c>Ā#Ĕűêę#ČŸ,ß. ¦Ƨ%ƨ#Ł4źÂƪUSA "É1SM"ĸķƩ. ů 1 Ş/21ƪ. ¥=^EG_c>5`A_FY".0Ü/2Ž~4Ė ƇƧ± 6(a)ƨƩùĶ©4î1+"Ʃ²ʼnúŢ" .0ñÂ2 2 Ŀ#ŐŧĿ#ő)¦34Īøͱ 4zéƩ=^EGZGLeG;8cM4ũƧ± 6(b), (c)ƨƪMalawi Venezuela "É1SM#ŐŧLeG" , 2 Ŀ#=^EG_c>5`A_FY4ƏáƩUSA "É1SM#ŐŧLeG §ď#Ž~ýę".0ĈŒ ĉ#ĖƇ4ũƧ± 6(d),(e)ƨƪ 2/#Œĉ".0ƩUSA "É1SM#ţŐŧLe G"$ƜÊĶ=^EG_c> Ə 1 4ļ2 Ƨ± 6(a)ƨƪK-means =^EG_c>#Ž~ wħī (a). $Ʃ=^EG_c>5`A_FY#âƄgƩ¥=^E G"ª(21LeG#ù µņ"!1."=^EGØé 4ũ+Ʃƫ#=^EG"Ůù#²ʼnÉâ ª* Ş/21ƪ=^EGZGLeG;8cM#Ż4 Ļſ1 ƩƜÊĶ=^EG_c> $ USA "É1SM #ŐŧLeG#) î2=^EG Øé2 . ƩK-means =^EG_c> $p#²ʼn"É1ŐŧL eG,Č2ƩĢÂ#²¨#ŐŧLeG#)î1. (b). (c). $ć! ƪ ÃƤ A ".0ƩMalawi - Venezuela "É1SM#Őŧ LeG" $fč"ƜÊĶ=^EG_c> Ə 1 $Ŷ! ļ2 ƩUSA "É1SM#Ő. (%) Bacteroides Prevotella Cluster 1 26.186195 0.01122793 Cluster 2 2.3850325 24.0535318 Cluster 3 4.797647 1.90437364. ŧLeG §ď#Ž~ýę".0ƩMalawi "É1SM# ŐŧLeG" $ƜÊĶ=^EG_c>ƩVenezuela "É1ŐŧLeG" $ K-means =^EG_c># Ž~ ƥ ļ2ƪ Ʃ=^EGZGL eG;8cM#Ż4Ļſ1 ƩƜÊĶ=^EG_c>. (d). © 2015 Information Processing Society of Japan. (e). ƏĪÛ# USA "É1SM#ŐŧLeG į!0Ʃ=^E G_c>5`A_FY"ƚ3/ 1 Ŀ#²ʼn"#)É1. 100.
(21) WebDB Forum 2015. =^EG4ĩé1 ć! ƪ#+Ʃ. 5. @ K. Malawi - Venezuela "É1SM#ŐŧLeG" $Ʃ. Ćŀ $ƩúŢŵƔĒš%Ůù#=^EG_c>êę. Ó=^EG_c>5`A_FY#ČŸ4ƩĂå!ţ Őŧ¡-SMÉâƚƌâ4î1 ßů 1 Ş /21ƪ Ć Ã Ƥ $ Ʃ Ĉ 1 S M # É â Malawi Ʃ VenezuelaƩUSA # 3 ;² =^EGùƧk=3ƨ §ù#É âĿƣù4ð²ʼnúŢ4{Ī ƩoÛ$Ĭâ-¾â 2 Ŀ#Éâ4ðâ#SMÉâLeG"ĸķ âúŢ4{ĪĈ,ũƩÉâ#Ŀƣù"¶ =^EGù#Øé" ,ČŸ1ƪ. 4ĪƩ²ʼnúŢ".0ñÂ2fƒ#LeG" Ʃ ţŐŧ¡LeG SM#Éâ #ƚâ#Ŕ¦Ķîý Ö4öċƪĆýÖ".0ƩţŐŧ¡ 1ĢÂ#S MÉâ #ƚƌâ ƩāƏ5`A_FY ƜÊĶ =^EG_c> Ɛí2ƩUSA "É1SM#) ª( 21=^EGä·ƧBacteroides Prevotella #Ȧƨ 4ĵŰĶ"î1 ¢š !ƪ oÛ$ƩÄƂŐŧLeG"áƏ!Ĕűêę#Č ŸƩ%ƩĆýÖ"¥ĿúŢ-į!1ŐŧLeG4ƏĪ 1¸¦#Ăâ" ,Čż1ÃƤ4ũm 1ƪ. C09. (a). (b). (c). (d). (e). ± 6 ²ʼnúŢ4Ī K-means =^EG_c>".1Ĉ ƜÊĶ=^EG_c>".1Ĉ#ĖƇŒĉ: (a) USA "É 1SM"ĸķ=^EG_c>êę #ĈŒĉĖƇƩ(b) :_DO`LeG4ÄƂ K-means =^EG_c>4Ãũ Œĉ#LeGøͱƩ(c) ²ʼnÉâZGLeG4;8cM ŒĉƧơÓƨƩ(d) Malawi "É1SM"ĸķ=^EG _c>êę #ĈŒĉĖƇƩ(e) Venezuela "É1SM" ĸķ=^EG_c>êę #ĈŒĉĖƇ. © 2015 Information Processing Society of Japan. 1. Bäckhed,F. et al,2005. Host-bacterial mutualism in the human intestine. Science,307,1915-1920. 2. Rehman,A. et al,2015. Geographical patterns of the standing and active human gut microbiome in health and IBD. Gut. 3. Yoshimoto,S. et al,2013. Obesity-induced gut microbial metabolite promotes liver cancer through senescence secretome. Nature,499,97-+. 4. Takano,K. et al,2005. A semantic Associative Search Method with Dynamic Context-Awareness Functions for Computing Causal Relations of Event Data Sets. TOD,46,SIG5(TOD25),40-55. 5. Kiyoki,Y., Kitagawa,T. and Hayama,T.,1994. A metadatabase system for semantic image search by a mathematical model of meaning. ACM SIGMOD Record,23(4),34-41. 6. Kiyoki,Y. and Kitagawa,T.,1995. A semantic associative search method for knowledge acquisition. Information Modelling and Knowledge Bases (IOS Press),VI,121-130. 7. Chen,J. et al,2012. Associating microbiome composition with environmental covariates using generalized UniFrac distances. Bioinformatics,28,2106-2113. 8. Lozupone,C. and Knight,R.,2005. UniFrac: a new phylogenetic method for comparing microbial communities. Applied and Environmental Microbiology,71,8228-8235. 9. Clarke,S.F. et al,2014. Exercise and associated dietary extremes impact on gut microbial diversity. Gut,63,1913-1920. 10. Walters,W.A. et al,2014. Meta-analyses of human gut microbes associated with obesity and IBD. Febs Letters, 588, 4223-4233. 11. Yatsunenko,T. et al,2012. Human gut microbiome viewed across age and geography. Nature, 486, 222-+. 12. McKusick,V.A.,2007. Mendelian inheritance in man and its online version, OMIM. American Journal of Human Genetics,80,588-604. 13. Becker,K.G. et al,2004. The Genetic Association Database. Nature Genetics,36,431-432. 14. D'Argenio,V. et al,2014. Comparative Metagenomic Analysis of Human Gut Microbiome Composition Using Two Different Bioinformatic Pipelines. Biomed Research International. 15. Han,J. and Kanber,M., 2000. Data mining: concepts and techniques. Morgan Kaufmann Publishers. 16. Jain,A.K., Murty,M.N. and Flynn,P.J.,1999. Data clustering: a review. ACM Computing Surveys,31(3). 17. Zushi,T. et al,2002. A semantic knowledge discovery method by recursively applying context dependent dynamic clustering to document data. IPSJ Journal,43,216-230. 18. Kawamoto,M. et al,2003. An implementation method of semantic associative search spaces for medical documents. 19. Hikichi,S. et al,2015. Human-microbiome-relations extraction and visualization system with context-dependent clustering and semantic analysis. 12th International Conference on Applied Computing 2015 (AC2015), Maynooth, Greater Dublin, Ireland, accepted 8 pages, October 24-26. (to appear) 20. Suzuki,T.A. and Worobey,M.,2014. Geographical variation of human gut microbial composition. Biology Letters, 10. 21. Dominianni,C. et al,2015. Sex, Body Mass Index, and Dietary Fiber Intake Influence the Human Gut Microbiome. Plos One,10(4).. 101.
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