{"id":655,"date":"2018-08-13T23:22:50","date_gmt":"2018-08-13T14:22:50","guid":{"rendered":"http:\/\/www.ecobioinfo.com\/?p=655"},"modified":"2022-06-05T22:51:47","modified_gmt":"2022-06-05T13:51:47","slug":"ai%e8%aa%b2%e9%a1%8c1%ef%bc%9adna%e7%9b%b8%e5%90%8c%e6%80%a7%e6%a4%9c%e7%b4%a2%e3%81%a8ai4-16srdna%e5%a1%a9%e5%9f%ba%e9%85%8d%e5%88%97%e3%81%a7%e7%b4%b0%e8%8f%8c%e5%88%86%e9%a1%9e3","status":"publish","type":"post","link":"https:\/\/www.ecobioinfo.com\/?p=655","title":{"rendered":"AI\u8ab2\u984c1\uff1aDNA\u76f8\u540c\u6027\u691c\u7d22\u3068AI(4) 16SrDNA\u5869\u57fa\u914d\u5217\u3067\u7d30\u83cc\u5206\u985e(3)"},"content":{"rendered":"

\uff08\u300cDNA\u76f8\u540c\u6027\u691c\u7d22\u3068AI(3) 16SrDNA\u5869\u57fa\u914d\u5217\u3067\u7d30\u83cc\u5206\u985e(2)<\/a>\u300d\u306e\u7d9a\u304d\u3067\u3059\u3002\uff09<\/p>\n

\u300c16SrDNA\u5869\u57fa\u914d\u5217\u3067\u7d30\u83cc\u5206\u985e(2)<\/a>\u300d\u3068\u307b\u307c\u540c\u3058\u69cb\u6210\u3067\u3001\u30e9\u30a4\u30d6\u30e9\u30ea\u306f TFLearn \u304b\u3089 Keras \u306b\u5909\u3048\u3001\u51fa\u529b\u5c64\u3092 softmax \u304b\u3089 sigmoid \u306b\u5909\u3048\u3066\u307f\u307e\u3057\u305f\u3002
\n\u51fa\u529b\u5c64 sigmoid \u3067\u306f\u3001\u305d\u308c\u305e\u308c\u306e\u7d30\u83cc\uff08\u5c5e\uff09\u306b\u3064\u3044\u3066\uff10\uff5e\uff11\u306e\u9593\u3067\u51fa\u529b\u3055\u308c\u3001
\n\u51fa\u529b\u5024\u306e\u6700\u5927\u306e\u7d30\u83cc\uff08\u5c5e\uff09\u3092\u300c\u25ce\u3042\u305f\u308a\u300d\u3001\u4e0a\u4f4d\uff15\u4f4d\u4ee5\u5185\u3092\u300c\u25b3\u304a\u3057\u3044\u300d\u3001\u305d\u308c\u4ee5\u4e0b\u3092\u300c\u2613\u306f\u305a\u308c\u300d\u3068\u3057\u3066\u8868\u793a\u3059\u308b\u3088\u3046\u306b\u3057\u307e\u3057\u305f\u3002<\/p>\n

Python\u30d7\u30ed\u30b0\u30e9\u30e0 16SrDNA\u5869\u57fa\u914d\u5217\u3067\u7d30\u83cc\u5206\u985e sigmod \u51fa\u529b
\ndnadnnsig.py (\u30e9\u30a4\u30d6\u30e9\u30ea tensorflow, keras) (UTF-8)<\/a><\/p>\n

\u30cd\u30c3\u30c8\u30ef\u30fc\u30af\u69cb\u6210\u306e\u90e8\u5206\uff08\u4e00\u90e8\u629c\u7c8b\uff09\u2015\u2015\u2015\u2015\u2015\u2015\u2015\u2015\u2015\u2015\u2015\u2015\u2015<\/p>\n

model = kr.models.Sequential()
\nmodel.add(kr.layers.Dense(units=4096, input_shape=(maxdnalength,), activation=’relu’))
\nmodel.add(kr.layers.Dense(units=4096, activation=’relu’))
\nmodel.add(kr.layers.Dense(units=4096, activation=’relu’))
\nmodel.add(kr.layers.Dense(units=4096, activation=’relu’))
\n# \u51fa\u529b\u5c64
\nmodel.add(kr.layers.Dense(units=generacount, activation=’sigmoid’))<\/p>\n

\u5b9f\u884c\u7d50\u679c\uff08\u4e00\u90e8\u629c\u7c8b\uff09\u2015\u2015\u2015\u2015\u2015\u2015\u2015\u2015\u2015\u2015\u2015\u2015\u2015<\/p>\n

DNA No.01 ——–
\n\u63a8\u5b9a No.01=Sphingomonas 0.0004 : No.02=Ochrobactrum 0.0000 : No.03=Methylobacterium 0.0000 : No.04=Paracoccus 0.0000 : No.05=Brevundimonas 0.0000 :
\n\u25ce\u3042\u305f\u308a \u6b63\u89e3=Sphingomonas
\nDNA No.02 ——–
\n\u63a8\u5b9a No.01=Bifidobacterium 0.9251 : No.02=Kocuria 0.0019 : No.03=Arthrobacter 0.0017 : No.04=Curtobacterium 0.0010 : No.05=Actinomyces 0.0009 :
\n\u25ce\u3042\u305f\u308a \u6b63\u89e3=Bifidobacterium
\nDNA No.03 ——–
\n\u63a8\u5b9a No.01=Streptococcus 0.6962 : No.02=Lactococcus 0.0001 : No.03=Oceanobacillus 0.0000 : No.04=Fusobacterium 0.0000 : No.05=Brevibacillus 0.0000 :
\n\u25ce\u3042\u305f\u308a \u6b63\u89e3=Streptococcus
\nDNA No.04 ——–
\n\u63a8\u5b9a No.01=Clostridium 0.0068 : No.02=Brevundimonas 0.0000 : No.03=Helicobacter 0.0000 : No.04=Sphingomonas 0.0000 : No.05=Peptostreptococcaceae 0.0000 :
\n\u25ce\u3042\u305f\u308a \u6b63\u89e3=Clostridium
\nDNA No.05 ——–
\n\u63a8\u5b9a No.01=Staphylococcus 0.0001 : No.02=Bacillus 0.0000 : No.03=Lactococcus 0.0000 : No.04=Virgibacillus 0.0000 : No.05=Streptococcus 0.0000 :
\n\u25ce\u3042\u305f\u308a \u6b63\u89e3=Staphylococcus<\/p>\n

\uff08\u4e2d\u7565\uff09<\/p>\n

DNA No.112 ——–
\n\u63a8\u5b9a No.01=Halorubrum 0.3010 : No.02=Actinomyces 0.0002 : No.03=Paracoccus 0.0002 : No.04=Ruegeria 0.0001 : No.05=Citrobacter 0.0001 :
\n\u25ce\u3042\u305f\u308a \u6b63\u89e3=Halorubrum
\nDNA No.113 ——–
\n\u63a8\u5b9a No.01=Nostoc 0.0000 : No.02=Acidovorax 0.0000 : No.03=Pseudomonas 0.0000 : No.04=Variovorax 0.0000 : No.05=Micrococcus 0.0000 :
\n\u2613\u306f\u305a\u308c \u6b63\u89e3=Erythrobacter \u9806\u4f4d[70]
\nDNA No.114 ——–
\n\u63a8\u5b9a No.01=Kocuria 0.0001 : No.02=Pseudonocardia 0.0000 : No.03=Micromonospora 0.0000 : No.04=Actinomycetales 0.0000 : No.05=Nocardiopsis 0.0000 :
\n\u25b3\u304a\u3057\u3044 \u6b63\u89e3=Pseudonocardia \u9806\u4f4d[2]
\n——end.(\u25ce 94.7 % \u25b3 2.6 % \u2613 2.6 % )<\/p>\n

\u5168\u7d50\u679c(UTF-8 \u30c6\u30ad\u30b9\u30c8\u30d5\u30a1\u30a4\u30eb) dnadnn_result.txt<\/a><\/p>\n

\u5b9f\u884c\u7d50\u679c\u3067\u306f\u3001\u300c\u00d7\u306f\u305a\u308c\u300d\u304c\uff13\u4ef6\u307b\u3069\u3042\u308a\u307e\u3057\u305f\u3002<\/p>\n

DNA No.23 ——–
\n\u63a8\u5b9a No.01=Agrobacterium 0.0018 : No.02=Rhizobium 0.0001 : No.03=Ensifer 0.0000 : No.04=Sinorhizobium 0.0000 : No.05=Ochrobactrum 0.0000 :
\n\u2613\u306f\u305a\u308c \u6b63\u89e3=Candidatus \u9806\u4f4d[10]<\/p>\n

Candidatus \u306f\u57f9\u990a\u306b\u6210\u529f\u3057\u3066\u3044\u306a\u3044\u539f\u6838\u751f\u7269\u306b\u4e0e\u3048\u3089\u308c\u308b\u5206\u985e\u5b66\u4e0a\u306e\u66ab\u5b9a\u7684\u306a\u5730\u4f4d\u3067\u3059\u3002\u7279\u5b9a\u306e\u7d30\u83cc\u3067\u306f\u306a\u3044\u306e\u3067\u5224\u65ad\u304c\u96e3\u3057\u3044\u3068\u601d\u308f\u308c\u307e\u3059\u3002<\/p>\n

DNA No.109 ——–
\n\u63a8\u5b9a No.01=Curtobacterium 0.0000 : No.02=Planococcus 0.0000 : No.03=Stenotrophomonas 0.0000 : No.04=Brevibacillus 0.0000 : No.05=Brevibacterium 0.0000 :
\n\u2613\u306f\u305a\u308c \u6b63\u89e3=Synechococcus \u9806\u4f4d[26]<\/p>\n

DNA No.113 ——–
\n\u63a8\u5b9a No.01=Nostoc 0.0000 : No.02=Acidovorax 0.0000 : No.03=Pseudomonas 0.0000 : No.04=Variovorax 0.0000 : No.05=Micrococcus 0.0000 :
\n\u2613\u306f\u305a\u308c \u6b63\u89e3=Erythrobacter \u9806\u4f4d[70]<\/p>\n

\u6700\u5927\u3067\u30820.0000\u3067\u3001\u3053\u306e\u5834\u5408\u306f\u300c\u4e0d\u660e\u300d\u3068\u8868\u793a\u3057\u305f\u307b\u3046\u304c\u826f\u3044\u304b\u3082\u3057\u308c\u307e\u305b\u3093\u3002<\/p>\n

 <\/p>\n

(S.Onda 2018\/8\/13)<\/p>\n

 <\/p>\n

 <\/p>\n","protected":false},"excerpt":{"rendered":"\uff08\u300cDNA\u76f8\u540c\u6027\u691c\u7d22\u3068AI(3) 16SrDNA\u5869\u57fa\u914d\u5217\u3067\u7d30\u83cc\u5206\u985e(2)\u300d\u306e\u7d9a\u304d\u3067\u3059\u3002\uff09 \u300c16SrDNA\u5869\u57fa\u914d\u5217\u3067\u7d30\u83cc\u5206\u985e(2)\u300d\u3068\u307b\u307c\u540c\u3058\u69cb\u6210\u3067\u3001\u30e9\u30a4\u30d6\u30e9\u30ea\u306f TFLearn \u304b\u3089 Keras \u306b\u5909\u3048\u3001\u51fa\u529b\u5c64\u3092 so …","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[7],"tags":[],"class_list":["post-655","post","type-post","status-publish","format-standard","hentry","category-ai_ecobio"],"_links":{"self":[{"href":"https:\/\/www.ecobioinfo.com\/index.php?rest_route=\/wp\/v2\/posts\/655","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.ecobioinfo.com\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.ecobioinfo.com\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.ecobioinfo.com\/index.php?rest_route=\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.ecobioinfo.com\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=655"}],"version-history":[{"count":6,"href":"https:\/\/www.ecobioinfo.com\/index.php?rest_route=\/wp\/v2\/posts\/655\/revisions"}],"predecessor-version":[{"id":1491,"href":"https:\/\/www.ecobioinfo.com\/index.php?rest_route=\/wp\/v2\/posts\/655\/revisions\/1491"}],"wp:attachment":[{"href":"https:\/\/www.ecobioinfo.com\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=655"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.ecobioinfo.com\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=655"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.ecobioinfo.com\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=655"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}