MX2014004471A - Fenotipificacion de precision utilizando analisis de proximidad de espacio de puntuacion. - Google Patents

Fenotipificacion de precision utilizando analisis de proximidad de espacio de puntuacion.

Info

Publication number
MX2014004471A
MX2014004471A MX2014004471A MX2014004471A MX2014004471A MX 2014004471 A MX2014004471 A MX 2014004471A MX 2014004471 A MX2014004471 A MX 2014004471A MX 2014004471 A MX2014004471 A MX 2014004471A MX 2014004471 A MX2014004471 A MX 2014004471A
Authority
MX
Mexico
Prior art keywords
organisms
experimental group
statistical analysis
score space
data
Prior art date
Application number
MX2014004471A
Other languages
English (en)
Inventor
James Janni
Jan Hazebroek
Stephen L Wright
Original Assignee
Pioner Hi Bred International Inc
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Pioner Hi Bred International Inc filed Critical Pioner Hi Bred International Inc
Publication of MX2014004471A publication Critical patent/MX2014004471A/es

Links

Classifications

    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B20/00—ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B20/00—ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations
    • G16B20/20—Allele or variant detection, e.g. single nucleotide polymorphism [SNP] detection
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B40/00—ICT specially adapted for biostatistics; ICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B40/00—ICT specially adapted for biostatistics; ICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding
    • G16B40/20—Supervised data analysis
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B40/00—ICT specially adapted for biostatistics; ICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding
    • G16B40/30—Unsupervised data analysis
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B20/00—ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations
    • G16B20/50—Mutagenesis

Landscapes

  • Engineering & Computer Science (AREA)
  • Health & Medical Sciences (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Physics & Mathematics (AREA)
  • Medical Informatics (AREA)
  • Bioinformatics & Cheminformatics (AREA)
  • Bioinformatics & Computational Biology (AREA)
  • Biotechnology (AREA)
  • Data Mining & Analysis (AREA)
  • Theoretical Computer Science (AREA)
  • Spectroscopy & Molecular Physics (AREA)
  • General Health & Medical Sciences (AREA)
  • Biophysics (AREA)
  • Evolutionary Biology (AREA)
  • Public Health (AREA)
  • Bioethics (AREA)
  • Artificial Intelligence (AREA)
  • Software Systems (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Evolutionary Computation (AREA)
  • Epidemiology (AREA)
  • Databases & Information Systems (AREA)
  • Analytical Chemistry (AREA)
  • Chemical & Material Sciences (AREA)
  • Genetics & Genomics (AREA)
  • Molecular Biology (AREA)
  • Proteomics, Peptides & Aminoacids (AREA)
  • Measuring Or Testing Involving Enzymes Or Micro-Organisms (AREA)
  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)
  • Breeding Of Plants And Reproduction By Means Of Culturing (AREA)
  • Farming Of Fish And Shellfish (AREA)
  • Investigating Or Analyzing Materials By The Use Of Electric Means (AREA)
  • Complex Calculations (AREA)

Abstract

Se proporcionan los métodos para determinar el nivel de perturbación de un fenotipo en un organismo mediante el uso de un análisis estadístico multivariante. El método comprende una primera etapa de recolectar al menos una medición de al menos un grupo de control de organismos y al menos un grupo experimental de organismos para producir un conjunto de datos. El método comprende, además, una segunda etapa de usar un procesador para conducir un análisis estadístico multivariante sobre el conjunto de datos para determinar el nivel de perturbación de un fenotipo o rasgo de interés en el grupo experimental de organismos. Tal análisis estadístico multivariante comprende las etapas de organizar el conjunto de datos en una matriz, expresar la matriz en un conjunto de funciones base nuevas y proyectar el conjunto de datos en el conjunto de funciones base nuevas para calcular un conjunto de puntuaciones para cada uno de los dos grupos de organismos. El análisis estadístico multivariante comprende, además, las etapas de determinar un espacio de puntuación mediante el cálculo de la distancia entre el conjunto de puntuaciones generadas por el grupo de control de los organismos y por el grupo experimental de organismos, y usar del espacio de puntuación para determinar el nivel de perturbación del fenotipo de interés en el grupo experimental de organismos. Los métodos se proporcionan, además, para seleccionar un grupo de organismos en base a la distancia en el espacio de puntuación entre el grupo de control de organismos y el grupo experimental de organismos.
MX2014004471A 2011-10-13 2012-10-09 Fenotipificacion de precision utilizando analisis de proximidad de espacio de puntuacion. MX2014004471A (es)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
US201161546672P 2011-10-13 2011-10-13
PCT/US2012/059290 WO2013055651A2 (en) 2011-10-13 2012-10-09 Precision phenotyping using score space proximity analysis

Publications (1)

Publication Number Publication Date
MX2014004471A true MX2014004471A (es) 2014-08-01

Family

ID=47080839

Family Applications (1)

Application Number Title Priority Date Filing Date
MX2014004471A MX2014004471A (es) 2011-10-13 2012-10-09 Fenotipificacion de precision utilizando analisis de proximidad de espacio de puntuacion.

Country Status (8)

Country Link
US (1) US20130179085A1 (es)
EP (1) EP2766837A2 (es)
AR (1) AR088276A1 (es)
AU (2) AU2012323405A1 (es)
BR (1) BR112014009059A2 (es)
CA (1) CA2852001A1 (es)
MX (1) MX2014004471A (es)
WO (1) WO2013055651A2 (es)

Families Citing this family (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103760114B (zh) * 2014-01-27 2016-06-08 林兴志 一种基于高光谱遥感的甘蔗糖分预测方法
CN103760113B (zh) * 2014-01-27 2016-06-29 林兴志 高光谱遥感甘蔗糖分分析装置
CN104881018B (zh) * 2015-03-26 2018-07-24 河海大学 用于小型灌区的水田灌溉水利用系数测试系统及测试方法
CN107966116B (zh) * 2017-11-20 2019-10-11 苏州市农业科学院 一种水稻种植面积的遥感监测方法及系统

Family Cites Families (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
GB9717926D0 (en) 1997-08-22 1997-10-29 Micromass Ltd Methods and apparatus for tandem mass spectrometry
US6920231B1 (en) * 2000-06-30 2005-07-19 Indentix Incorporated Method and system of transitive matching for object recognition, in particular for biometric searches
US20040018501A1 (en) * 2001-11-21 2004-01-29 Keith Allen Methods and systems for analyzing complex biological systems
EP1936370A1 (en) * 2006-12-22 2008-06-25 Max-Planck-Gesellschaft zur Förderung der Wissenschaften e.V. Determination and prediction of the expression of traits of plants from the metabolite profile as a biomarker
US8429115B1 (en) * 2009-12-23 2013-04-23 Decision Lens, Inc. Measuring change distance of a factor in a decision

Also Published As

Publication number Publication date
EP2766837A2 (en) 2014-08-20
AU2018200030A1 (en) 2018-01-25
AR088276A1 (es) 2014-05-21
US20130179085A1 (en) 2013-07-11
WO2013055651A2 (en) 2013-04-18
CA2852001A1 (en) 2013-04-18
WO2013055651A3 (en) 2013-10-10
AU2012323405A1 (en) 2014-05-01
BR112014009059A2 (pt) 2017-04-18

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