JPWO2020086967A5 - - Google Patents
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- JPWO2020086967A5 JPWO2020086967A5 JP2021523055A JP2021523055A JPWO2020086967A5 JP WO2020086967 A5 JPWO2020086967 A5 JP WO2020086967A5 JP 2021523055 A JP2021523055 A JP 2021523055A JP 2021523055 A JP2021523055 A JP 2021523055A JP WO2020086967 A5 JPWO2020086967 A5 JP WO2020086967A5
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- 238000010801 machine learning Methods 0.000 claims 17
- 201000007185 autism spectrum disease Diseases 0.000 claims 13
- 230000000875 corresponding Effects 0.000 claims 10
- 238000007689 inspection Methods 0.000 claims 6
- 108091007151 Piwi-interacting RNA Proteins 0.000 claims 5
- 229920001239 microRNA Polymers 0.000 claims 4
- 239000002679 microRNA Substances 0.000 claims 4
- 230000000813 microbial Effects 0.000 claims 4
- 239000004055 small Interfering RNA Substances 0.000 claims 4
- 229920000160 (ribonucleotides)n+m Polymers 0.000 claims 3
- 241000186840 Lactobacillus fermentum Species 0.000 claims 3
- 108020004388 MicroRNAs Proteins 0.000 claims 3
- 241001639641 Ottowia Species 0.000 claims 3
- 210000003296 Saliva Anatomy 0.000 claims 3
- 241000194017 Streptococcus Species 0.000 claims 3
- 229940012969 lactobacillus fermentum Drugs 0.000 claims 3
- 230000001131 transforming Effects 0.000 claims 3
- 241000132734 Actinomyces oris Species 0.000 claims 2
- 241001135756 Alphaproteobacteria Species 0.000 claims 2
- 241000186063 Arthrobacter Species 0.000 claims 2
- 241001112695 Clostridiales Species 0.000 claims 2
- 241001600130 Comamonadaceae Species 0.000 claims 2
- 241000737368 Corynebacterium uterequi Species 0.000 claims 2
- 241001489979 Cryptococcus gattii WM276 Species 0.000 claims 2
- 241000604777 Flavobacterium columnare Species 0.000 claims 2
- 241000223218 Fusarium Species 0.000 claims 2
- 241000026993 Jeotgalibacillus Species 0.000 claims 2
- 241001584978 Leadbetterella byssophila DSM 17132 Species 0.000 claims 2
- 241000192132 Leuconostoc Species 0.000 claims 2
- 102100003414 MTRNR2L4 Human genes 0.000 claims 2
- 101710010841 MTRNR2L4 Proteins 0.000 claims 2
- 102100003362 MTRNR2L8 Human genes 0.000 claims 2
- 101710010844 MTRNR2L8 Proteins 0.000 claims 2
- 241000420773 Megasphaera elsdenii DSM 20460 Species 0.000 claims 2
- 241000186359 Mycobacterium Species 0.000 claims 2
- 241000588653 Neisseria Species 0.000 claims 2
- 241000588656 Neisseriaceae Species 0.000 claims 2
- 241001170684 Oenococcus oeni PSU-1 Species 0.000 claims 2
- 241000606752 Pasteurellaceae Species 0.000 claims 2
- 241000605894 Porphyromonas Species 0.000 claims 2
- 108020004418 Ribosomal RNA Proteins 0.000 claims 2
- 241001453443 Rothia <bacteria> Species 0.000 claims 2
- 241000268542 Rothia dentocariosa ATCC 17931 Species 0.000 claims 2
- 108020003224 Small Nucleolar RNA Proteins 0.000 claims 2
- 241000191940 Staphylococcus Species 0.000 claims 2
- 241001109791 Streptococcus agalactiae CNCTC 10/84 Species 0.000 claims 2
- 241001220634 Streptococcus halotolerans Species 0.000 claims 2
- 241001487144 Streptococcus mutans UA159-FR Species 0.000 claims 2
- 241000058406 Streptococcus pneumoniae SPNA45 Species 0.000 claims 2
- 241001393263 Streptococcus salivarius CCHSS3 Species 0.000 claims 2
- 241000970979 Streptomyces griseochromogenes Species 0.000 claims 2
- 241001034637 Tsukamurella paurometabola DSM 20162 Species 0.000 claims 2
- 241000798866 Yarrowia lipolytica CLIB122 Species 0.000 claims 2
- 229920002973 ribosomal RNA Polymers 0.000 claims 2
- 241000186046 Actinomyces Species 0.000 claims 1
- 241000511582 Actinomyces meyeri Species 0.000 claims 1
- 241001600148 Burkholderiales Species 0.000 claims 1
- 241000056141 Chryseobacterium sp. Species 0.000 claims 1
- 241001187099 Dickeya Species 0.000 claims 1
- 241000186394 Eubacterium Species 0.000 claims 1
- 241000579722 Kocuria Species 0.000 claims 1
- 241000460492 Kocuria flava Species 0.000 claims 1
- 241001247311 Kocuria rhizophila Species 0.000 claims 1
- 241000559104 Kocuria turfanensis Species 0.000 claims 1
- 241001331186 Leadbetterella Species 0.000 claims 1
- 108020005198 Long Noncoding RNA Proteins 0.000 claims 1
- 241000568397 Lysinibacillus Species 0.000 claims 1
- 241000193386 Lysinibacillus sphaericus Species 0.000 claims 1
- 241001348279 Maribacter Species 0.000 claims 1
- 241000863391 Methylophilus Species 0.000 claims 1
- 229920000348 MiR-155 Polymers 0.000 claims 1
- 241000191938 Micrococcus luteus Species 0.000 claims 1
- 241000606860 Pasteurella Species 0.000 claims 1
- 229940051027 Pasteurella multocida Drugs 0.000 claims 1
- 241000606856 Pasteurella multocida Species 0.000 claims 1
- -1 RNA5S Proteins 0.000 claims 1
- 241000203719 Rothia dentocariosa Species 0.000 claims 1
- 229920000632 Small nucleolar RNA Polymers 0.000 claims 1
- 241000077999 Trichormus Species 0.000 claims 1
- 108020004417 Untranslated RNA Proteins 0.000 claims 1
- 238000007477 logistic regression Methods 0.000 claims 1
- 244000005700 microbiome Species 0.000 claims 1
- 229920001894 non-coding RNA Polymers 0.000 claims 1
- 230000004044 response Effects 0.000 claims 1
Claims (13)
前記処理回路が、患者病歴及び患者唾液から得られるデータを、特徴の検査パネルに対応するデータへと変換し、前記特徴についての前記データが、ヒトマイクロトランスクリプトームデータ及び微生物トランスクリプトームデータを含み、前記トランスクリプトームデータが、ＡＳＤに対するそれぞれのＲＮＡカテゴリーと関連し、
前記検査パネルの前記特徴と関連するトレーニングデータを使用してＡＳＤを検出するようにトレーニングされている前記処理回路に前記変換データを適用することによって前記データを前記処理回路が分類し、
前記トレーニングされた処理回路が、分類境界を定義するベクトルを含み、
前記トレーニングされた処理回路が、サポートベクトルマシンであり、前記分類境界を定義する前記ベクトルが、サポートベクトルであり、
前記処理回路が、前記データを、前記特徴の検査パネルに対応するデータへと変換し、前記特徴の検査パネルが、
患者データ主成分及び患者年齢、
マイクロＲＮＡ（ｈｓａ－ｍｉｒ－１４６ａ、ｈｓａ－ｍｉｒ－１４６ｂ、ｈｓａ－ｍｉＲ－９２ａ－３ｐ、ｈｓａ－ｍｉＲ－１０６－５ｐ、ｈｓａ－ｍｉＲ－３９１６、ｈｓａ－ｍｉｒ－１０ａ、ｈｓａ－ｍｉＲ－３７８ａ－３ｐ、ｈｓａ－ｍｉＲ－１２５ａ－５ｐ、ｈｓａ－ｍｉＲ１４６ｂ－５ｐ、ｈｓａ－ｍｉＲ－３６１－５ｐ、ｈｓａ－ｍｉｒ－４１０を含む）、
ｐｉＲＮＡ（ｐｉＲ－ｈｓａ－１５０２３、ｐｉＲ－ｈｓａ－２７４００、ｐｉＲ－ｈｓａ－９４９１、ｐｉＲ－ｈｓａ－２９１１４、ｐｉＲ－ｈｓａ－６４６３、ｐｉＲ－ｈｓａ－２４０８５、ｐｉＲ－ｈｓａ－１２４２３、ｐｉＲ－ｈｓａ－２４６８４を含む）、
核小体低分子ＲＮＡ（ＳＮＯＲＤ１１８を含む）、ならびに
微生物（Ｓｔｒｅｐｔｏｃｏｃｃｕｓ ｇａｌｌｏｌｙｔｉｃｕｓの亜種ｇａｌｌｏｌｙｔｉｃｕｓ ＤＳＭ１６８３１、Ｙａｒｒｏｗｉａ ｌｉｐｏｌｙｔｉｃａ ＣＬＩＢ１２２、Ｃｌｏｓｔｒｉｄｉａｌｅｓ、Ｏｅｎｏｃｏｃｃｕｓ ｏｅｎｉ ＰＳＵ－１、Ｆｕｓａｒｉｕｍ、Ａｌｐｈａｐｒｏｔｅｏｂａｃｔｅｒｉａ、Ｌａｃｔｏｂａｃｉｌｌｕｓ ｆｅｒｍｅｎｔｕｍ、Ｃｏｒｙｎｅｂａｃｔｅｒｉｕｍ ｕｔｅｒｅｑｕｉ、Ｏｔｔｏｗｉａ属の１種ｏｒａｌ ｔａｘｏｎ８９４、Ｐａｓｔｅｕｒｅｌｌａ ｍｕｌｔｏｃｉｄａの亜種ｍｕｌｔｏｃｉｄａ ＯＨ４８０７、Ｌｅａｄｂｅｔｔｅｒｅｌｌａ ｂｙｓｓｏｐｈｉｌａ ＤＳＭ１７１３２、Ｓｔａｐｈｙｌｏｃｏｃｃｕｓを含む）、
を含む、
機械学習分類器。 1. A machine learning classifier for diagnosing autism spectrum disorder (ASD), comprising a processing circuit,
The processing circuitry converts data obtained from the patient history and patient saliva into data corresponding to a test panel of characteristics, the data for the characteristics comprising human microtranscriptome data and microbial transcriptome data. wherein the transcriptome data are associated with each RNA category for ASD;
said processing circuitry classifying said data by applying said transformation data to said processing circuitry that has been trained to detect ASD using training data associated with said features of said test panel;
wherein the trained processing circuitry includes vectors defining classification boundaries;
said trained processing circuitry is a support vector machine, said vectors defining said classification boundaries are support vectors;
The processing circuitry converts the data into data corresponding to the inspection panel of features, the inspection panel of features:
patient data principal components and patient age,
MicroRNAs (hsa-mir-146a, hsa-mir-146b, hsa-miR-92a-3p, hsa-miR-106-5p, hsa-miR-3916, hsa-mir-10a, hsa-miR-378a-3p , hsa-miR-125a-5p, hsa-miR146b-5p, hsa-miR-361-5p, hsa-mir-410),
piRNA (piR-hsa-15023, piR-hsa-27400, piR-hsa-9491, piR-hsa-29114, piR-hsa-6463, piR-hsa-24085, piR-hsa-12423, piR-hsa-24684 include),
核小体低分子ＲＮＡ（ＳＮＯＲＤ１１８を含む）、ならびに 微生物（Ｓｔｒｅｐｔｏｃｏｃｃｕｓ ｇａｌｌｏｌｙｔｉｃｕｓの亜種ｇａｌｌｏｌｙｔｉｃｕｓ ＤＳＭ１６８３１、Ｙａｒｒｏｗｉａ ｌｉｐｏｌｙｔｉｃａ ＣＬＩＢ１２２、Ｃｌｏｓｔｒｉｄｉａｌｅｓ、Ｏｅｎｏｃｏｃｃｕｓ ｏｅｎｉ ＰＳＵ－１、Ｆｕｓａｒｉｕｍ、Ａｌｐｈａｐｒｏｔｅｏｂａｃｔｅｒｉａ、Ｌａｃｔｏｂａｃｉｌｌｕｓ ｆｅｒｍｅｎｔｕｍ、Ｃｏｒｙｎｅｂａｃｔｅｒｉｕｍ ｕｔｅｒｅｑｕｉ、Ｏｔｔｏｗｉａ属の１種oral taxon 894, Pasteurella multocida subspecies multocida OH4807, Leadbetterella byssophila DSM17132, Staphylococcus),
including,
Machine learning classifier.
前記処理回路が、前記カテゴリー患者特徴を因子分解し、それぞれの特徴をバイナリ応答に変換する、請求項１に記載の機械学習分類器。 wherein the data obtained from the patient history correspond to categorical and numerical patient characteristics;
2. The machine learning classifier of claim 1, wherein the processing circuitry factors the categorical patient features and transforms each feature into a binary response.
前記患者データ主成分、患者年齢、及び患者性別、
マイクロＲＮＡ（ｈｓａ－ｌｅｔ－７ａ－２、ｈｓａ－ｍｉＲ－１０ｂ－５ｐ、ｈｓａ－ｍｉＲ－１２５ａ－５ｐ、ｈｓａ－ｍｉＲ－１２５ｂ－２－３ｐ、ｈｓａ－ｍｉＲ－１４２－３ｐ、ｈｓａ－ｍｉＲ－１４６ａ－５ｐ、ｈｓａ－ｍｉＲ－２１８－５ｐ、ｈｓａ－ｍｉｒ－３７８ｄ－１、ｈｓａ－ｍｉｒ－４１０、ｈｓａ－ｍｉｒ－４２１、ｈｓａ－ｍｉｒ－４２８４、ｈｓａ－ｍｉＲ－４６９８、ｈｓａ－ｍｉｒ－４７９８、ｈｓａ－ｍｉＲ－５１５－５ｐ、ｈｓａ－ｍｉｒ－５５７２、ｈｓａ－ｍｉＲ－６７４８－３ｐを含む）、
ｐｉＲＮＡ（ｐｉＲ－ｈｓａ－１２４２３、ｐｉＲ－ｈｓａ－１５０２３、ｐｉＲ－ｈｓａ－１８９０５、ｐｉＲ－ｈｓａ－２３６３８、ｐｉＲ－ｈｓａ－２４６８４、ｐｉＲ－ｈｓａ－２７１３３、ｐｉＲ－ｈｓａ－３２４、ｐｉＲ－ｈｓａ－９４９１を含む）、
微生物（Ａｃｔｉｎｏｍｙｃｅｓ、Ａｒｔｈｒｏｂａｃｔｅｒ、Ｊｅｏｔｇａｌｉｂａｃｉｌｌｕｓ、Ｌｅａｄｂｅｔｔｅｒｅｌｌａ、Ｌｅｕｃｏｎｏｓｔｏｃ、Ｍｙｃｏｂａｃｔｅｒｉｕｍ、Ｏｔｔｏｗｉａ、Ｓａｃｃｈａｒｏｍｙｃｅｓを含む）、ならびに
微生物活性（Ｋ００５２０、Ｋ１４２２１、Ｋ０１５９１、Ｋ０２１１１、Ｋ１４２５５、Ｋ１４３２、Ｋ００１３３、Ｋ０３１１１を含む）、
を含む、請求項７に記載の機械学習分類器。 The processing circuitry converts the data into data corresponding to the inspection panel of features, the inspection panel of features:
the patient data principal components, patient age, and patient gender;
MicroRNAs (hsa-let-7a-2, hsa-miR-10b-5p, hsa-miR-125a-5p, hsa-miR-125b-2-3p, hsa-miR-142-3p, hsa-miR-146a -5p, hsa-miR-218-5p, hsa-mir-378d-1, hsa-mir-410, hsa-mir-421, hsa-mir-4284, hsa-miR-4698, hsa-mir-4798, hsa -miR-515-5p, hsa-mir-5572, hsa-miR-6748-3p),
piRNA (piR-hsa-12423, piR-hsa-15023, piR-hsa-18905, piR-hsa-23638, piR-hsa-24684, piR-hsa-27133, piR-hsa-324, piR-hsa-9491 include),
微生物（Ａｃｔｉｎｏｍｙｃｅｓ、Ａｒｔｈｒｏｂａｃｔｅｒ、Ｊｅｏｔｇａｌｉｂａｃｉｌｌｕｓ、Ｌｅａｄｂｅｔｔｅｒｅｌｌａ、Ｌｅｕｃｏｎｏｓｔｏｃ、Ｍｙｃｏｂａｃｔｅｒｉｕｍ、Ｏｔｔｏｗｉａ、Ｓａｃｃｈａｒｏｍｙｃｅｓを含む）、ならびに 微生物活性（Ｋ００５２０、Ｋ１４２２１、Ｋ０１５９１、Ｋ０２１１１、Ｋ１４２５５、Ｋ１４３２、Ｋ００１３３、Ｋ０３１１１を含む）、
8. The machine learning classifier of claim 7, comprising:
前記処理回路が、患者病歴及び患者唾液から得られるデータを、特徴の検査パネルに対応するデータへと変換し、前記特徴についての前記データが、ヒトマイクロトランスクリプトームデータ及び微生物トランスクリプトームデータを含み、前記トランスクリプトームデータが、ＡＳＤに対するそれぞれのＲＮＡカテゴリーと関連し、
前記検査パネルの前記特徴と関連するトレーニングデータを使用してＡＳＤを検出するようにトレーニングされている前記処理回路に前記変換データを適用することによって前記データを前記処理回路が分類し、
前記トレーニングされた処理回路が、分類境界を定義するベクトルを含み、
前記トレーニングされた処理回路が、サポートベクトルマシンであり、前記分類境界を定義する前記ベクトルが、サポートベクトルであり、
前記特徴の検査パネルと、前記分類境界を定義する前記ベクトルとが、特徴のマスターパネル中の特徴の数を順位順序で増やしながら予測性能がプラトーに達するまで予測モデルをフィッティングさせることによって前記処理回路によって決定され、
第２の機械学習モデルが、前記特徴を順位付けし、前記マスターパネルにおける特徴の順位順序を生成するために用いられ、前記第２の機械学習モデルは、前記サポートベクトルマシンとは異なる、
機械学習分類器。 1. A machine learning classifier for diagnosing autism spectrum disorder (ASD), comprising a processing circuit,
The processing circuitry converts data obtained from the patient history and patient saliva into data corresponding to a test panel of characteristics, the data for the characteristics comprising human microtranscriptome data and microbial transcriptome data. wherein the transcriptome data are associated with each RNA category for ASD;
said processing circuitry classifying said data by applying said transformation data to said processing circuitry that has been trained to detect ASD using training data associated with said features of said test panel;
wherein the trained processing circuitry includes vectors defining classification boundaries;
said trained processing circuitry is a support vector machine, said vectors defining said classification boundaries are support vectors;
said test panel of features and said vectors defining said classification boundaries said processing circuit by fitting a predictive model while increasing the number of features in the master panel of features in rank order until predictive performance reaches a plateau; determined by
a second machine learning model is used to rank the features and generate a ranking order of the features in the master panel, the second machine learning model being different from the support vector machine;
Machine learning classifier.
前記処理回路が、前記カテゴリー患者特徴を主成分上に射影し、
前記処理回路が、前記データを、前記特徴のマスターパネルに対応するデータへと変換し、前記特徴のマスターパネルが、
因子分解された患者のデータ及び患者年齢、
マイクロＲＮＡ（ｈｓａ－ｍｉｒ－１４６ａ、ｈｓａ－ｍｉｒ－１４６ｂ、ｈｓａ－ｍｉＲ－９２ａ－３ｐ、ｈｓａ－ｍｉＲ－１０６－５ｐ、ｈｓａ－ｍｉＲ－３９１６、ｈｓａ－ｍｉｒ－１０ａ、ｈｓａ－ｍｉＲ－３７８ａ－３ｐ、ｈｓａ－ｍｉＲ－１２５ａ－５ｐ、ｈｓａ－ｍｉＲ１４６ｂ－５ｐ、ｈｓａ－ｍｉＲ－３６１－５ｐ、ｈｓａ－ｍｉｒ－４１０、ｈｓａ－ｍｉｒ－４４６１、ｈｓａ－ｍｉＲ－１５ａ－５ｐ、ｈｓａ－ｍｉＲ－６７６３－３ｐ、ｈｓａ－ｍｉＲ－１９６ａ－５ｐ、ｈｓａ－ｍｉＲ－４６６８－５ｐ、ｈｓａ－ｍｉＲ－３７８ｄ、ｈｓａ－ｍｉＲ－１４２－３ｐ、ｈｓａ－ｍｉｒ－３０ｃ－１、ｈｓａ－ｍｉｒ－１０１－２、ｈｓａ－ｍｉｒ－１５１ａ、ｈｓａ－ｍｉＲ－１２５ｂ－２－３ｐ、ｈｓａ－ｍｉｒ－１４８ａ－５ｐ、ｈｓａ－ｍｉｒ－５４８Ｉ、ｈｓａ－ｍｉＲ－９８－５ｐ、ｈｓａ－ｍｉＲ－８０６５、ｈｓａ－ｍｉｒ－３７８ｄ－１、ｈｓａ－ｌｅｔ－７ｆ－１、及びｈｓａ－ｌｅｔ－７ｄ－３ｐを含む）、
ｐｉＲＮＡ（ｐｉＲ－ｈｓａ－１５０２３、ｐｉＲ－ｈｓａ－２７４００、ｐｉＲ－ｈｓａ－９４９１、ｐｉＲ－ｈｓａ－２９１１４、ｐｉＲ－ｈｓａ－６４６３、ｐｉＲ－ｈｓａ－２４０８５、ｐｉＲ－ｈｓａ－１２４２３、ｐｉＲ－ｈｓａ－２４６８４、ｐｉＲ－ｈｓａ－３４０５、ｐｉＲ－ｈｓａ－３２４、ｐｉＲ－ｈｓａ－１８９０５、ｐｉＲ－ｈｓａ－２３２４８、ｐｉＲ－ｈｓａ－２８２２３、ｐｉＲ－ｈｓａ－２８４００、ｐｉＲ－ｈｓａ－１１７７、及びｐｉＲ－ｈｓａ－２６５９２を含む）、
核小体低分子ＲＮＡ（ＳＮＯＲＤ１１８、ＳＮＯＲＤ２９、ＳＮＯＲＤ５３Ｂ、ＳＮＯＲＤ６８、ＳＮＯＲＤ２０、ＳＮＯＲＤ４１、ＳＮＯＲＤ３０、及びＳＮＯＲＤ３４を含む）、
リボソームＲＮＡ（ＲＮＡ５Ｓ、ＭＴＲＮＲ２Ｌ４、及びＭＴＲＮＲ２Ｌ８を含む）、
長鎖非コードＲＮＡ（ＬＯＣ７３０３３８を含む）、
微生物（Ｓｔｒｅｐｔｏｃｏｃｃｕｓ ｇａｌｌｏｌｙｔｉｃｕｓの亜種ｇａｌｌｏｌｙｔｉｃｕｓ ＤＳＭ１６８３１、Ｙａｒｒｏｗｉａ ｌｉｐｏｌｙｔｉｃａ ＣＬＩＢ１２２、Ｃｌｏｓｔｒｉｄｉａｌｅｓ、Ｏｅｎｏｃｏｃｃｕｓ ｏｅｎｉ ＰＳＵ－１、Ｆｕｓａｒｉｕｍ、Ａｌｐｈａｐｒｏｔｅｏｂａｃｔｅｒｉａ、Ｌａｃｔｏｂａｃｉｌｌｕｓ ｆｅｒｍｅｎｔｕｍ、Ｃｏｒｙｎｅｂａｃｔｅｒｉｕｍ ｕｔｅｒｅｑｕｉ、Ｏｔｔｏｗｉａ属の１種ｏｒａｌ ｔａｘｏｎ８９４、Ｐａｓｔｅｕｒｅｌｌａ ｍｕｌｔｏｃｉｄａの亜種ｍｕｌｔｏｃｉｄａ ＯＨ４８０７、Ｌｅａｄｂｅｔｔｅｒｅｌｌａ ｂｙｓｓｏｐｈｉｌａ ＤＳＭ１７１３２、Ｓｔａｐｈｙｌｏｃｏｃｃｕｓ、Ｒｏｔｈｉａ、Ｃｒｙｐｔｏｃｏｃｃｕｓ ｇａｔｔｉｉ ＷＭ２７６、Ｎｅｉｓｓｅｒｉａｃｅａｅ、Ｒｏｔｈｉａ ｄｅｎｔｏｃａｒｉｏｓａ ＡＴＣＣ１７９３１、Ｃｈｒｙｓｅｏｂａｃｔｅｒｉｕｍ属の１種ＩＨＢ Ｂ １７０１９、Ｓｔｒｅｐｔｏｃｏｃｃｕｓ ａｇａｌａｃｔｉａｅ ＣＮＣＴＣ１０／８４、Ｓｔｒｅｐｔｏｃｏｃｃｕｓ ｐｎｅｕｍｏｎｉａｅ ＳＰＮＡ４５、Ｔｓｕｋａｍｕｒｅｌｌａ ｐａｕｒｏｍｅｔａｂｏｌａ ＤＳＭ２０１６２、Ｓｔｒｅｐｔｏｃｏｃｃｕｓ ｍｕｔａｎｓ ＵＡ１５９－ＦＲ、Ａｃｔｉｎｏｍｙｃｅｓ ｏｒｉｓ、Ｃｏｍａｍｏｎａｄａｃｅａｅ、Ｓｔｒｅｐｔｏｃｏｃｃｕｓ ｈａｌｏｔｏｌｅｒａｎｓ、Ｆｌａｖｏｂａｃｔｅｒｉｕｍ ｃｏｌｕｍｎａｒｅ、Ｓｔｒｅｐｔｏｍｙｃｅｓ ｇｒｉｓｅｏｃｈｒｏｍｏｇｅｎｅｓ、Ｎｅｉｓｓｅｒｉａ、Ｐｏｒｐｈｙｒｏｍｏｎａｓ、Ｓｔｒｅｐｔｏｃｏｃｃｕｓ ｓａｌｉｖａｒｉｕｓ ＣＣＨＳＳ３、Ｍｅｇａｓｐｈａｅｒａ ｅｌｓｄｅｎｉｉ ＤＳＭ２０４６０、Ｐａｓｔｅｕｒｅｌｌａｃｅａｅ、及び未分類のＢｕｒｋｈｏｌｄｅｒｉａｌｅｓを含む）、
を含む、請求項９に記載の機械学習分類器。 wherein the data obtained from the patient history correspond to categorical and numerical patient characteristics;
the processing circuitry projects the categorical patient features onto principal components;
The processing circuitry transforms the data into data corresponding to the master panel of features, the master panel of features comprising:
factorized patient data and patient age,
MicroRNAs (hsa-mir-146a, hsa-mir-146b, hsa-miR-92a-3p, hsa-miR-106-5p, hsa-miR-3916, hsa-mir-10a, hsa-miR-378a-3p , hsa-miR-125a-5p, hsa-miR146b-5p, hsa-miR-361-5p, hsa-mir-410, hsa-mir-4461, hsa-miR-15a-5p, hsa-miR-6763-3p , hsa-miR-196a-5p, hsa-miR-4668-5p, hsa-miR-378d, hsa-miR-142-3p, hsa-mir-30c-1, hsa-mir-101-2, hsa-mir -151a, hsa-miR-125b-2-3p, hsa-mir-148a-5p, hsa-mir-548I, hsa-miR-98-5p, hsa-miR-8065, hsa-mir-378d-1, hsa -let-7f-1, and hsa-let-7d-3p),
piRNA (piR-hsa-15023, piR-hsa-27400, piR-hsa-9491, piR-hsa-29114, piR-hsa-6463, piR-hsa-24085, piR-hsa-12423, piR-hsa-24684, including piR-hsa-3405, piR-hsa-324, piR-hsa-18905, piR-hsa-23248, piR-hsa-28223, piR-hsa-28400, piR-hsa-1177, and piR-hsa-26592 ),
small nucleolar RNAs (including SNORD118, SNORD29, SNORD53B, SNORD68, SNORD20, SNORD41, SNORD30, and SNORD34),
ribosomal RNA (including RNA5S, MTRNR2L4, and MTRNR2L8),
long non-coding RNAs (including LOC730338),
微生物（Ｓｔｒｅｐｔｏｃｏｃｃｕｓ ｇａｌｌｏｌｙｔｉｃｕｓの亜種ｇａｌｌｏｌｙｔｉｃｕｓ ＤＳＭ１６８３１、Ｙａｒｒｏｗｉａ ｌｉｐｏｌｙｔｉｃａ ＣＬＩＢ１２２、Ｃｌｏｓｔｒｉｄｉａｌｅｓ、Ｏｅｎｏｃｏｃｃｕｓ ｏｅｎｉ ＰＳＵ－１、Ｆｕｓａｒｉｕｍ、Ａｌｐｈａｐｒｏｔｅｏｂａｃｔｅｒｉａ、Ｌａｃｔｏｂａｃｉｌｌｕｓ ｆｅｒｍｅｎｔｕｍ、Ｃｏｒｙｎｅｂａｃｔｅｒｉｕｍ ｕｔｅｒｅｑｕｉ、Ｏｔｔｏｗｉａ属の１種ｏｒａｌ ｔａｘｏｎ８９４、Ｐａｓｔｅｕｒｅｌｌａ ｍｕｌｔｏｃｉｄａの亜種ｍｕｌｔｏｃｉｄａ ＯＨ４８０７、Ｌｅａｄｂｅｔｔｅｒｅｌｌａ ｂｙｓｓｏｐｈｉｌａ ＤＳＭ１７１３２、Ｓｔａｐｈｙｌｏｃｏｃｃｕｓ、Ｒｏｔｈｉａ、Ｃｒｙｐｔｏｃｏｃｃｕｓ ｇａｔｔｉｉ ＷＭ２７６、Ｎｅｉｓｓｅｒｉａｃｅａｅ、Ｒｏｔｈｉａ ｄｅｎｔｏｃａｒｉｏｓａ ＡＴＣＣ１７９３１、Ｃｈｒｙｓｅｏｂａｃｔｅｒｉｕｍ属の１種ＩＨＢ Ｂ １７０１９、Ｓｔｒｅｐｔｏｃｏｃｃｕｓ ａｇａｌａｃｔｉａｅ ＣＮＣＴＣ１０／８４、Ｓｔｒｅｐｔｏｃｏｃｃｕｓ ｐｎｅｕｍｏｎｉａｅ ＳＰＮＡ４５、Ｔｓｕｋａｍｕｒｅｌｌａ ｐａｕｒｏｍｅｔａｂｏｌａ ＤＳＭ２０１６２、Ｓｔｒｅｐｔｏｃｏｃｃｕｓ ｍｕｔａｎｓ ＵＡ１５９－ＦＲ、Ａｃｔｉｎｏｍｙｃｅｓ ｏｒｉｓ、Ｃｏｍａｍｏｎａｄａｃｅａｅ、 Ｓｔｒｅｐｔｏｃｏｃｃｕｓ ｈａｌｏｔｏｌｅｒａｎｓ、Ｆｌａｖｏｂａｃｔｅｒｉｕｍ ｃｏｌｕｍｎａｒｅ、Ｓｔｒｅｐｔｏｍｙｃｅｓ ｇｒｉｓｅｏｃｈｒｏｍｏｇｅｎｅｓ、Ｎｅｉｓｓｅｒｉａ、Ｐｏｒｐｈｙｒｏｍｏｎａｓ、Ｓｔｒｅｐｔｏｃｏｃｃｕｓ ｓａｌｉｖａｒｉｕｓ ＣＣＨＳＳ３、Ｍｅｇａｓｐｈａｅｒａ ｅｌｓｄｅｎｉｉ ＤＳＭ２０４６０、Ｐａｓｔｅｕｒｅｌｌａｃｅａｅ、及び未分類のＢｕｒｋｈｏｌｄｅｒｉａｌｅｓを含む）、
10. The machine learning classifier of claim 9, comprising
前記処理回路が、患者病歴及び患者唾液から得られるデータを、特徴の検査パネルに対応するデータへと変換し、前記特徴についての前記データが、ヒトマイクロトランスクリプトームデータ及び微生物トランスクリプトームデータを含み、前記トランスクリプトームデータが、ＡＳＤに対するそれぞれのＲＮＡカテゴリーと関連し、
前記検査パネルの前記特徴と関連するトレーニングデータを使用してＡＳＤを検出するようにトレーニングされている前記処理回路に前記変換データを適用することによって前記データを前記処理回路が分類し、
前記トレーニングされた処理回路が、分類境界を定義するベクトルを含み、
前記トレーニングされた処理回路が、サポートベクトルマシンであり、前記分類境界を定義する前記ベクトルが、サポートベクトルであり、
前記処理回路が、前記特徴の検査パネルを決定し、前記特徴の検査パネルが、
マイクロＲＮＡ（ｈｓａ＿ｌｅｔ＿７ｄ＿５ｐ、ｈｓａ＿ｌｅｔ＿７ｇ＿５ｐ、ｈｓａ＿ｍｉＲ＿１０１＿３ｐ、ｈｓａ＿ｍｉＲ＿１３０７＿５ｐ、ｈｓａ＿ｍｉＲ＿１４２＿５ｐ、ｈｓａ＿ｍｉＲ＿１５１ａ＿３ｐ、ｈｓａ＿ｍｉＲ＿１５ａ＿５ｐ、ｈｓａ＿ｍｉＲ＿２１０＿３ｐ、ｈｓａ＿ｍｉＲ＿２８＿３ｐ、ｈｓａ＿ｍｉＲ＿２９ａ＿３ｐ、ｈｓａ＿ｍｉＲ＿３０７４＿５ｐ、ｈｓａ＿ｍｉＲ＿３７４ａ＿５ｐ、ｈｓａ＿ｍｉＲ＿９２ａ＿３ｐを含む）、
ｐｉＲＮＡ（ｈｓａ－ｐｉＲＮＡ＿３４９９、ｈｓａ－ｐｉＲＮＡ＿１４３３、ｈｓａ－ｐｉＲＮＡ＿９８４３、ｈｓａ－ｐｉＲＮＡ＿２５３３を含む）、
微生物（Ａｃｔｉｎｏｍｙｃｅｓ ｍｅｙｅｒｉ、Ｅｕｂａｃｔｅｒｉｕｍ、Ｋｏｃｕｒｉａ ｆｌａｖａ、Ｋｏｃｕｒｉａ ｒｈｉｚｏｐｈｉｌａ、Ｋｏｃｕｒｉａ ｔｕｒｆａｎｅｎｓｉｓ、Ｌａｃｔｏｂａｃｉｌｌｕｓ ｆｅｒｍｅｎｔｕｍ、Ｌｙｓｉｎｉｂａｃｉｌｌｕｓ ｓｐｈａｅｒｉｃｕｓ、Ｍｉｃｒｏｃｏｃｃｕｓ ｌｕｔｅｕｓ、Ｏｔｔｏｗｉａ、Ｒｏｔｈｉａ ｄｅｎｔｏｃａｒｉｏｓａ、Ｓｔｒｅｐｔｏｃｏｃｃｕｓ ｄｙｓｇａｌａｃｔｉａｅを含む）、
微生物活性（Ｋ０１８６７、Ｋ０２００５、Ｋ０２７９５、Ｋ１９９７２を含む）、
を含む、機械学習分類器。 1. A machine learning classifier for diagnosing autism spectrum disorder (ASD), comprising a processing circuit,
The processing circuitry converts data obtained from the patient history and patient saliva into data corresponding to a test panel of characteristics, the data for the characteristics comprising human microtranscriptome data and microbial transcriptome data. wherein the transcriptome data are associated with each RNA category for ASD;
said processing circuitry classifying said data by applying said transformation data to said processing circuitry that has been trained to detect ASD using training data associated with said features of said test panel;
wherein the trained processing circuitry includes vectors defining classification boundaries;
said trained processing circuitry is a support vector machine, said vectors defining said classification boundaries are support vectors;
The processing circuitry determines the inspection panel of features, the inspection panel of features comprising:
マイクロＲＮＡ（ｈｓａ＿ｌｅｔ＿７ｄ＿５ｐ、ｈｓａ＿ｌｅｔ＿７ｇ＿５ｐ、ｈｓａ＿ｍｉＲ＿１０１＿３ｐ、ｈｓａ＿ｍｉＲ＿１３０７＿５ｐ、ｈｓａ＿ｍｉＲ＿１４２＿５ｐ、ｈｓａ＿ｍｉＲ＿１５１ａ＿３ｐ、ｈｓａ＿ｍｉＲ＿１５ａ＿５ｐ、ｈｓａ＿ｍｉＲ＿２１０＿３ｐ、ｈｓａ＿ｍｉＲ＿２８＿３ｐ、ｈｓａ＿ｍｉＲ＿２９ａ＿３ｐ、ｈｓａ＿ｍｉＲ＿３０７４＿５ｐ、ｈｓａ＿ｍｉＲ＿３７４ａ＿５ｐ、ｈｓａ＿ｍｉＲ＿９２ａ＿３ｐを含む）、
piRNAs (including hsa-piRNA_3499, hsa-piRNA_1433, hsa-piRNA_9843, hsa-piRNA_2533),
微生物（Ａｃｔｉｎｏｍｙｃｅｓ ｍｅｙｅｒｉ、Ｅｕｂａｃｔｅｒｉｕｍ、Ｋｏｃｕｒｉａ ｆｌａｖａ、Ｋｏｃｕｒｉａ ｒｈｉｚｏｐｈｉｌａ、Ｋｏｃｕｒｉａ ｔｕｒｆａｎｅｎｓｉｓ、Ｌａｃｔｏｂａｃｉｌｌｕｓ ｆｅｒｍｅｎｔｕｍ、Ｌｙｓｉｎｉｂａｃｉｌｌｕｓ ｓｐｈａｅｒｉｃｕｓ、Ｍｉｃｒｏｃｏｃｃｕｓ ｌｕｔｅｕｓ、Ｏｔｔｏｗｉａ、Ｒｏｔｈｉａ ｄｅｎｔｏｃａｒｉｏｓａ、Ｓｔｒｅｐｔｏｃｏｃｃｕｓ ｄｙｓｇａｌａｃｔｉａｅを含む）、
microbial activity (including K01867, K02005, K02795, K19972);
, including machine learning classifiers.
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| US11923048B1 (en) | 2017-10-03 | 2024-03-05 | Cerner Innovation, Inc. | Determining mucopolysaccharidoses and decision support tool |
| BR112021018770A2 (en) * | 2019-03-22 | 2022-02-15 | Cognoa Inc | Personalized digital therapy methods and devices |
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| US20140045702A1 (en) * | 2012-08-13 | 2014-02-13 | Synapdx Corporation | Systems and methods for distinguishing between autism spectrum disorders (asd) and non-asd development delay |
| WO2015022545A2 (en) * | 2013-08-14 | 2015-02-19 | Reneuron Limited | Stem cell microparticles and mirna |
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| KR102359013B1 (en) * | 2017-03-21 | 2022-02-04 | 콰드런트 바이오사이언시즈 인코포레이티드 | Analysis of Autism Spectrum Disorders |
| US20190228836A1 (en) * | 2018-01-15 | 2019-07-25 | SensOmics, Inc. | Systems and methods for predicting genetic diseases |
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- 2019-10-25 US US17/288,399 patent/US20210383924A1/en active Pending
- 2019-10-25 WO PCT/US2019/058073 patent/WO2020086967A1/en unknown
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