Performance of random forests and logic regression methods using mini-exome sequence data
Abstract Machine learning approaches are an attractive option for analyzing large-scale data to detect genetic variants that contribute to variation of a quantitative trait, without requiring specific distributional assumptions. We evaluate two machine learning methods, random forests and logic regr...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Kim, Yoonhee [verfasserIn] |
|---|
| Format: |
E-Artikel |
|---|---|
| Sprache: |
Englisch |
| Erschienen: |
2011 |
|---|
| Schlagwörter: |
|---|
| Anmerkung: |
© Kim et al; licensee BioMed Central Ltd. 2011. This article is published under license to BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License ( |
|---|
| Übergeordnetes Werk: |
Enthalten in: BMC proceedings - London : BioMed Central, 2007, 5(2011), Suppl 9 vom: 29. Nov. |
|---|---|
| Übergeordnetes Werk: |
volume:5 ; year:2011 ; number:Suppl 9 ; day:29 ; month:11 |
| Links: |
|---|
| DOI / URN: |
10.1186/1753-6561-5-S9-S104 |
|---|
| Katalog-ID: |
SPR028445910 |
|---|
| LEADER | 01000caa a22002652 4500 | ||
|---|---|---|---|
| 001 | SPR028445910 | ||
| 003 | DE-627 | ||
| 005 | 20230520001838.0 | ||
| 007 | cr uuu---uuuuu | ||
| 008 | 201007s2011 xx |||||o 00| ||eng c | ||
| 024 | 7 | |a 10.1186/1753-6561-5-S9-S104 |2 doi | |
| 035 | |a (DE-627)SPR028445910 | ||
| 035 | |a (SPR)1753-6561-5-S9-S104-e | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 100 | 1 | |a Kim, Yoonhee |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Performance of random forests and logic regression methods using mini-exome sequence data |
| 264 | 1 | |c 2011 | |
| 336 | |a Text |b txt |2 rdacontent | ||
| 337 | |a Computermedien |b c |2 rdamedia | ||
| 338 | |a Online-Ressource |b cr |2 rdacarrier | ||
| 500 | |a © Kim et al; licensee BioMed Central Ltd. 2011. This article is published under license to BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License ( | ||
| 520 | |a Abstract Machine learning approaches are an attractive option for analyzing large-scale data to detect genetic variants that contribute to variation of a quantitative trait, without requiring specific distributional assumptions. We evaluate two machine learning methods, random forests and logic regression, and compare them to standard simple univariate linear regression, using the Genetic Analysis Workshop 17 mini-exome data. We also apply these methods after collapsing multiple rare variants within genes and within gene pathways. Linear regression and the random forest method performed better when rare variants were collapsed based on genes or gene pathways than when each variant was analyzed separately. Logic regression performed better when rare variants were collapsed based on genes rather than on pathways. | ||
| 650 | 4 | |a Random Forest |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Causal Gene |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Random Forest Model |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Random Forest Method |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Random Forest Analysis |7 (dpeaa)DE-He213 | |
| 700 | 1 | |a Li, Qing |4 aut | |
| 700 | 1 | |a Cropp, Cheryl D |4 aut | |
| 700 | 1 | |a Sung, Heejong |4 aut | |
| 700 | 1 | |a Cai, Juanliang |4 aut | |
| 700 | 1 | |a Simpson, Claire L |4 aut | |
| 700 | 1 | |a Perry, Brian |4 aut | |
| 700 | 1 | |a Dasgupta, Abhijit |4 aut | |
| 700 | 1 | |a Malley, James D |4 aut | |
| 700 | 1 | |a Wilson, Alexander F |4 aut | |
| 700 | 1 | |a Bailey-Wilson, Joan E |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t BMC proceedings |d London : BioMed Central, 2007 |g 5(2011), Suppl 9 vom: 29. Nov. |w (DE-627)559080840 |w (DE-600)2411867-9 |x 1753-6561 |7 nnns |
| 773 | 1 | 8 | |g volume:5 |g year:2011 |g number:Suppl 9 |g day:29 |g month:11 |
| 856 | 4 | 0 | |u https://dx.doi.org/10.1186/1753-6561-5-S9-S104 |z kostenfrei |3 Volltext |
| 912 | |a GBV_USEFLAG_A | ||
| 912 | |a SYSFLAG_A | ||
| 912 | |a GBV_SPRINGER | ||
| 912 | |a SSG-OLC-PHA | ||
| 912 | |a GBV_ILN_20 | ||
| 912 | |a GBV_ILN_22 | ||
| 912 | |a GBV_ILN_23 | ||
| 912 | |a GBV_ILN_24 | ||
| 912 | |a GBV_ILN_31 | ||
| 912 | |a GBV_ILN_39 | ||
| 912 | |a GBV_ILN_40 | ||
| 912 | |a GBV_ILN_60 | ||
| 912 | |a GBV_ILN_62 | ||
| 912 | |a GBV_ILN_63 | ||
| 912 | |a GBV_ILN_65 | ||
| 912 | |a GBV_ILN_69 | ||
| 912 | |a GBV_ILN_73 | ||
| 912 | |a GBV_ILN_74 | ||
| 912 | |a GBV_ILN_95 | ||
| 912 | |a GBV_ILN_105 | ||
| 912 | |a GBV_ILN_110 | ||
| 912 | |a GBV_ILN_151 | ||
| 912 | |a GBV_ILN_161 | ||
| 912 | |a GBV_ILN_170 | ||
| 912 | |a GBV_ILN_206 | ||
| 912 | |a GBV_ILN_213 | ||
| 912 | |a GBV_ILN_230 | ||
| 912 | |a GBV_ILN_285 | ||
| 912 | |a GBV_ILN_293 | ||
| 912 | |a GBV_ILN_602 | ||
| 912 | |a GBV_ILN_2005 | ||
| 912 | |a GBV_ILN_2009 | ||
| 912 | |a GBV_ILN_2011 | ||
| 912 | |a GBV_ILN_2014 | ||
| 912 | |a GBV_ILN_2027 | ||
| 912 | |a GBV_ILN_2055 | ||
| 912 | |a GBV_ILN_2111 | ||
| 912 | |a GBV_ILN_4012 | ||
| 912 | |a GBV_ILN_4037 | ||
| 912 | |a GBV_ILN_4112 | ||
| 912 | |a GBV_ILN_4125 | ||
| 912 | |a GBV_ILN_4126 | ||
| 912 | |a GBV_ILN_4249 | ||
| 912 | |a GBV_ILN_4305 | ||
| 912 | |a GBV_ILN_4306 | ||
| 912 | |a GBV_ILN_4307 | ||
| 912 | |a GBV_ILN_4313 | ||
| 912 | |a GBV_ILN_4322 | ||
| 912 | |a GBV_ILN_4323 | ||
| 912 | |a GBV_ILN_4324 | ||
| 912 | |a GBV_ILN_4325 | ||
| 912 | |a GBV_ILN_4338 | ||
| 912 | |a GBV_ILN_4367 | ||
| 912 | |a GBV_ILN_4700 | ||
| 951 | |a AR | ||
| 952 | |d 5 |j 2011 |e Suppl 9 |b 29 |c 11 | ||
| author_variant |
y k yk q l ql c d c cd cdc h s hs j c jc c l s cl cls b p bp a d ad j d m jd jdm a f w af afw j e b w jeb jebw |
|---|---|
| matchkey_str |
article:17536561:2011----::efracornofrssnlgcersinehduig |
| hierarchy_sort_str |
2011 |
| publishDate |
2011 |
| allfields |
10.1186/1753-6561-5-S9-S104 doi (DE-627)SPR028445910 (SPR)1753-6561-5-S9-S104-e DE-627 ger DE-627 rakwb eng Kim, Yoonhee verfasserin aut Performance of random forests and logic regression methods using mini-exome sequence data 2011 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Kim et al; licensee BioMed Central Ltd. 2011. This article is published under license to BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License ( Abstract Machine learning approaches are an attractive option for analyzing large-scale data to detect genetic variants that contribute to variation of a quantitative trait, without requiring specific distributional assumptions. We evaluate two machine learning methods, random forests and logic regression, and compare them to standard simple univariate linear regression, using the Genetic Analysis Workshop 17 mini-exome data. We also apply these methods after collapsing multiple rare variants within genes and within gene pathways. Linear regression and the random forest method performed better when rare variants were collapsed based on genes or gene pathways than when each variant was analyzed separately. Logic regression performed better when rare variants were collapsed based on genes rather than on pathways. Random Forest (dpeaa)DE-He213 Causal Gene (dpeaa)DE-He213 Random Forest Model (dpeaa)DE-He213 Random Forest Method (dpeaa)DE-He213 Random Forest Analysis (dpeaa)DE-He213 Li, Qing aut Cropp, Cheryl D aut Sung, Heejong aut Cai, Juanliang aut Simpson, Claire L aut Perry, Brian aut Dasgupta, Abhijit aut Malley, James D aut Wilson, Alexander F aut Bailey-Wilson, Joan E aut Enthalten in BMC proceedings London : BioMed Central, 2007 5(2011), Suppl 9 vom: 29. Nov. (DE-627)559080840 (DE-600)2411867-9 1753-6561 nnns volume:5 year:2011 number:Suppl 9 day:29 month:11 https://dx.doi.org/10.1186/1753-6561-5-S9-S104 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2027 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 5 2011 Suppl 9 29 11 |
| spelling |
10.1186/1753-6561-5-S9-S104 doi (DE-627)SPR028445910 (SPR)1753-6561-5-S9-S104-e DE-627 ger DE-627 rakwb eng Kim, Yoonhee verfasserin aut Performance of random forests and logic regression methods using mini-exome sequence data 2011 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Kim et al; licensee BioMed Central Ltd. 2011. This article is published under license to BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License ( Abstract Machine learning approaches are an attractive option for analyzing large-scale data to detect genetic variants that contribute to variation of a quantitative trait, without requiring specific distributional assumptions. We evaluate two machine learning methods, random forests and logic regression, and compare them to standard simple univariate linear regression, using the Genetic Analysis Workshop 17 mini-exome data. We also apply these methods after collapsing multiple rare variants within genes and within gene pathways. Linear regression and the random forest method performed better when rare variants were collapsed based on genes or gene pathways than when each variant was analyzed separately. Logic regression performed better when rare variants were collapsed based on genes rather than on pathways. Random Forest (dpeaa)DE-He213 Causal Gene (dpeaa)DE-He213 Random Forest Model (dpeaa)DE-He213 Random Forest Method (dpeaa)DE-He213 Random Forest Analysis (dpeaa)DE-He213 Li, Qing aut Cropp, Cheryl D aut Sung, Heejong aut Cai, Juanliang aut Simpson, Claire L aut Perry, Brian aut Dasgupta, Abhijit aut Malley, James D aut Wilson, Alexander F aut Bailey-Wilson, Joan E aut Enthalten in BMC proceedings London : BioMed Central, 2007 5(2011), Suppl 9 vom: 29. Nov. (DE-627)559080840 (DE-600)2411867-9 1753-6561 nnns volume:5 year:2011 number:Suppl 9 day:29 month:11 https://dx.doi.org/10.1186/1753-6561-5-S9-S104 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2027 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 5 2011 Suppl 9 29 11 |
| allfields_unstemmed |
10.1186/1753-6561-5-S9-S104 doi (DE-627)SPR028445910 (SPR)1753-6561-5-S9-S104-e DE-627 ger DE-627 rakwb eng Kim, Yoonhee verfasserin aut Performance of random forests and logic regression methods using mini-exome sequence data 2011 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Kim et al; licensee BioMed Central Ltd. 2011. This article is published under license to BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License ( Abstract Machine learning approaches are an attractive option for analyzing large-scale data to detect genetic variants that contribute to variation of a quantitative trait, without requiring specific distributional assumptions. We evaluate two machine learning methods, random forests and logic regression, and compare them to standard simple univariate linear regression, using the Genetic Analysis Workshop 17 mini-exome data. We also apply these methods after collapsing multiple rare variants within genes and within gene pathways. Linear regression and the random forest method performed better when rare variants were collapsed based on genes or gene pathways than when each variant was analyzed separately. Logic regression performed better when rare variants were collapsed based on genes rather than on pathways. Random Forest (dpeaa)DE-He213 Causal Gene (dpeaa)DE-He213 Random Forest Model (dpeaa)DE-He213 Random Forest Method (dpeaa)DE-He213 Random Forest Analysis (dpeaa)DE-He213 Li, Qing aut Cropp, Cheryl D aut Sung, Heejong aut Cai, Juanliang aut Simpson, Claire L aut Perry, Brian aut Dasgupta, Abhijit aut Malley, James D aut Wilson, Alexander F aut Bailey-Wilson, Joan E aut Enthalten in BMC proceedings London : BioMed Central, 2007 5(2011), Suppl 9 vom: 29. Nov. (DE-627)559080840 (DE-600)2411867-9 1753-6561 nnns volume:5 year:2011 number:Suppl 9 day:29 month:11 https://dx.doi.org/10.1186/1753-6561-5-S9-S104 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2027 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 5 2011 Suppl 9 29 11 |
| allfieldsGer |
10.1186/1753-6561-5-S9-S104 doi (DE-627)SPR028445910 (SPR)1753-6561-5-S9-S104-e DE-627 ger DE-627 rakwb eng Kim, Yoonhee verfasserin aut Performance of random forests and logic regression methods using mini-exome sequence data 2011 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Kim et al; licensee BioMed Central Ltd. 2011. This article is published under license to BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License ( Abstract Machine learning approaches are an attractive option for analyzing large-scale data to detect genetic variants that contribute to variation of a quantitative trait, without requiring specific distributional assumptions. We evaluate two machine learning methods, random forests and logic regression, and compare them to standard simple univariate linear regression, using the Genetic Analysis Workshop 17 mini-exome data. We also apply these methods after collapsing multiple rare variants within genes and within gene pathways. Linear regression and the random forest method performed better when rare variants were collapsed based on genes or gene pathways than when each variant was analyzed separately. Logic regression performed better when rare variants were collapsed based on genes rather than on pathways. Random Forest (dpeaa)DE-He213 Causal Gene (dpeaa)DE-He213 Random Forest Model (dpeaa)DE-He213 Random Forest Method (dpeaa)DE-He213 Random Forest Analysis (dpeaa)DE-He213 Li, Qing aut Cropp, Cheryl D aut Sung, Heejong aut Cai, Juanliang aut Simpson, Claire L aut Perry, Brian aut Dasgupta, Abhijit aut Malley, James D aut Wilson, Alexander F aut Bailey-Wilson, Joan E aut Enthalten in BMC proceedings London : BioMed Central, 2007 5(2011), Suppl 9 vom: 29. Nov. (DE-627)559080840 (DE-600)2411867-9 1753-6561 nnns volume:5 year:2011 number:Suppl 9 day:29 month:11 https://dx.doi.org/10.1186/1753-6561-5-S9-S104 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2027 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 5 2011 Suppl 9 29 11 |
| allfieldsSound |
10.1186/1753-6561-5-S9-S104 doi (DE-627)SPR028445910 (SPR)1753-6561-5-S9-S104-e DE-627 ger DE-627 rakwb eng Kim, Yoonhee verfasserin aut Performance of random forests and logic regression methods using mini-exome sequence data 2011 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Kim et al; licensee BioMed Central Ltd. 2011. This article is published under license to BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License ( Abstract Machine learning approaches are an attractive option for analyzing large-scale data to detect genetic variants that contribute to variation of a quantitative trait, without requiring specific distributional assumptions. We evaluate two machine learning methods, random forests and logic regression, and compare them to standard simple univariate linear regression, using the Genetic Analysis Workshop 17 mini-exome data. We also apply these methods after collapsing multiple rare variants within genes and within gene pathways. Linear regression and the random forest method performed better when rare variants were collapsed based on genes or gene pathways than when each variant was analyzed separately. Logic regression performed better when rare variants were collapsed based on genes rather than on pathways. Random Forest (dpeaa)DE-He213 Causal Gene (dpeaa)DE-He213 Random Forest Model (dpeaa)DE-He213 Random Forest Method (dpeaa)DE-He213 Random Forest Analysis (dpeaa)DE-He213 Li, Qing aut Cropp, Cheryl D aut Sung, Heejong aut Cai, Juanliang aut Simpson, Claire L aut Perry, Brian aut Dasgupta, Abhijit aut Malley, James D aut Wilson, Alexander F aut Bailey-Wilson, Joan E aut Enthalten in BMC proceedings London : BioMed Central, 2007 5(2011), Suppl 9 vom: 29. Nov. (DE-627)559080840 (DE-600)2411867-9 1753-6561 nnns volume:5 year:2011 number:Suppl 9 day:29 month:11 https://dx.doi.org/10.1186/1753-6561-5-S9-S104 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2027 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 5 2011 Suppl 9 29 11 |
| language |
English |
| source |
Enthalten in BMC proceedings 5(2011), Suppl 9 vom: 29. Nov. volume:5 year:2011 number:Suppl 9 day:29 month:11 |
| sourceStr |
Enthalten in BMC proceedings 5(2011), Suppl 9 vom: 29. Nov. volume:5 year:2011 number:Suppl 9 day:29 month:11 |
| format_phy_str_mv |
Article |
| institution |
findex.gbv.de |
| topic_facet |
Random Forest Causal Gene Random Forest Model Random Forest Method Random Forest Analysis |
| isfreeaccess_bool |
true |
| container_title |
BMC proceedings |
| authorswithroles_txt_mv |
Kim, Yoonhee @@aut@@ Li, Qing @@aut@@ Cropp, Cheryl D @@aut@@ Sung, Heejong @@aut@@ Cai, Juanliang @@aut@@ Simpson, Claire L @@aut@@ Perry, Brian @@aut@@ Dasgupta, Abhijit @@aut@@ Malley, James D @@aut@@ Wilson, Alexander F @@aut@@ Bailey-Wilson, Joan E @@aut@@ |
| publishDateDaySort_date |
2011-11-29T00:00:00Z |
| hierarchy_top_id |
559080840 |
| id |
SPR028445910 |
| language_de |
englisch |
| fullrecord |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">SPR028445910</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230520001838.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">201007s2011 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1186/1753-6561-5-S9-S104</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)SPR028445910</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(SPR)1753-6561-5-S9-S104-e</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Kim, Yoonhee</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Performance of random forests and logic regression methods using mini-exome sequence data</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2011</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© Kim et al; licensee BioMed Central Ltd. 2011. This article is published under license to BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License (</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract Machine learning approaches are an attractive option for analyzing large-scale data to detect genetic variants that contribute to variation of a quantitative trait, without requiring specific distributional assumptions. We evaluate two machine learning methods, random forests and logic regression, and compare them to standard simple univariate linear regression, using the Genetic Analysis Workshop 17 mini-exome data. We also apply these methods after collapsing multiple rare variants within genes and within gene pathways. Linear regression and the random forest method performed better when rare variants were collapsed based on genes or gene pathways than when each variant was analyzed separately. Logic regression performed better when rare variants were collapsed based on genes rather than on pathways.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Random Forest</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Causal Gene</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Random Forest Model</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Random Forest Method</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Random Forest Analysis</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Li, Qing</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Cropp, Cheryl D</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Sung, Heejong</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Cai, Juanliang</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Simpson, Claire L</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Perry, Brian</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Dasgupta, Abhijit</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Malley, James D</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Wilson, Alexander F</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Bailey-Wilson, Joan E</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">BMC proceedings</subfield><subfield code="d">London : BioMed Central, 2007</subfield><subfield code="g">5(2011), Suppl 9 vom: 29. Nov.</subfield><subfield code="w">(DE-627)559080840</subfield><subfield code="w">(DE-600)2411867-9</subfield><subfield code="x">1753-6561</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:5</subfield><subfield code="g">year:2011</subfield><subfield code="g">number:Suppl 9</subfield><subfield code="g">day:29</subfield><subfield code="g">month:11</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://dx.doi.org/10.1186/1753-6561-5-S9-S104</subfield><subfield code="z">kostenfrei</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_SPRINGER</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-PHA</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_20</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_22</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_23</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_24</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_31</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_39</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_40</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_60</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_62</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_63</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_65</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_69</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_73</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_74</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_95</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_105</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_110</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_151</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_161</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_170</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_206</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_213</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_230</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_285</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_293</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_602</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2005</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2009</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2011</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2014</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2027</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2055</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2111</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4012</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4037</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4125</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4126</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4249</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4305</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4306</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4307</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4313</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4322</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4323</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4324</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4325</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4338</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4367</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4700</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">5</subfield><subfield code="j">2011</subfield><subfield code="e">Suppl 9</subfield><subfield code="b">29</subfield><subfield code="c">11</subfield></datafield></record></collection>
|
| author |
Kim, Yoonhee |
| spellingShingle |
Kim, Yoonhee misc Random Forest misc Causal Gene misc Random Forest Model misc Random Forest Method misc Random Forest Analysis Performance of random forests and logic regression methods using mini-exome sequence data |
| authorStr |
Kim, Yoonhee |
| ppnlink_with_tag_str_mv |
@@773@@(DE-627)559080840 |
| format |
electronic Article |
| delete_txt_mv |
keep |
| author_role |
aut aut aut aut aut aut aut aut aut aut aut |
| collection |
springer |
| remote_str |
true |
| illustrated |
Not Illustrated |
| issn |
1753-6561 |
| topic_title |
Performance of random forests and logic regression methods using mini-exome sequence data Random Forest (dpeaa)DE-He213 Causal Gene (dpeaa)DE-He213 Random Forest Model (dpeaa)DE-He213 Random Forest Method (dpeaa)DE-He213 Random Forest Analysis (dpeaa)DE-He213 |
| topic |
misc Random Forest misc Causal Gene misc Random Forest Model misc Random Forest Method misc Random Forest Analysis |
| topic_unstemmed |
misc Random Forest misc Causal Gene misc Random Forest Model misc Random Forest Method misc Random Forest Analysis |
| topic_browse |
misc Random Forest misc Causal Gene misc Random Forest Model misc Random Forest Method misc Random Forest Analysis |
| format_facet |
Elektronische Aufsätze Aufsätze Elektronische Ressource |
| format_main_str_mv |
Text Zeitschrift/Artikel |
| carriertype_str_mv |
cr |
| hierarchy_parent_title |
BMC proceedings |
| hierarchy_parent_id |
559080840 |
| hierarchy_top_title |
BMC proceedings |
| isfreeaccess_txt |
true |
| familylinks_str_mv |
(DE-627)559080840 (DE-600)2411867-9 |
| title |
Performance of random forests and logic regression methods using mini-exome sequence data |
| ctrlnum |
(DE-627)SPR028445910 (SPR)1753-6561-5-S9-S104-e |
| title_full |
Performance of random forests and logic regression methods using mini-exome sequence data |
| author_sort |
Kim, Yoonhee |
| journal |
BMC proceedings |
| journalStr |
BMC proceedings |
| lang_code |
eng |
| isOA_bool |
true |
| recordtype |
marc |
| publishDateSort |
2011 |
| contenttype_str_mv |
txt |
| author_browse |
Kim, Yoonhee Li, Qing Cropp, Cheryl D Sung, Heejong Cai, Juanliang Simpson, Claire L Perry, Brian Dasgupta, Abhijit Malley, James D Wilson, Alexander F Bailey-Wilson, Joan E |
| container_volume |
5 |
| format_se |
Elektronische Aufsätze |
| author-letter |
Kim, Yoonhee |
| doi_str_mv |
10.1186/1753-6561-5-S9-S104 |
| title_sort |
performance of random forests and logic regression methods using mini-exome sequence data |
| title_auth |
Performance of random forests and logic regression methods using mini-exome sequence data |
| abstract |
Abstract Machine learning approaches are an attractive option for analyzing large-scale data to detect genetic variants that contribute to variation of a quantitative trait, without requiring specific distributional assumptions. We evaluate two machine learning methods, random forests and logic regression, and compare them to standard simple univariate linear regression, using the Genetic Analysis Workshop 17 mini-exome data. We also apply these methods after collapsing multiple rare variants within genes and within gene pathways. Linear regression and the random forest method performed better when rare variants were collapsed based on genes or gene pathways than when each variant was analyzed separately. Logic regression performed better when rare variants were collapsed based on genes rather than on pathways. © Kim et al; licensee BioMed Central Ltd. 2011. This article is published under license to BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License ( |
| abstractGer |
Abstract Machine learning approaches are an attractive option for analyzing large-scale data to detect genetic variants that contribute to variation of a quantitative trait, without requiring specific distributional assumptions. We evaluate two machine learning methods, random forests and logic regression, and compare them to standard simple univariate linear regression, using the Genetic Analysis Workshop 17 mini-exome data. We also apply these methods after collapsing multiple rare variants within genes and within gene pathways. Linear regression and the random forest method performed better when rare variants were collapsed based on genes or gene pathways than when each variant was analyzed separately. Logic regression performed better when rare variants were collapsed based on genes rather than on pathways. © Kim et al; licensee BioMed Central Ltd. 2011. This article is published under license to BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License ( |
| abstract_unstemmed |
Abstract Machine learning approaches are an attractive option for analyzing large-scale data to detect genetic variants that contribute to variation of a quantitative trait, without requiring specific distributional assumptions. We evaluate two machine learning methods, random forests and logic regression, and compare them to standard simple univariate linear regression, using the Genetic Analysis Workshop 17 mini-exome data. We also apply these methods after collapsing multiple rare variants within genes and within gene pathways. Linear regression and the random forest method performed better when rare variants were collapsed based on genes or gene pathways than when each variant was analyzed separately. Logic regression performed better when rare variants were collapsed based on genes rather than on pathways. © Kim et al; licensee BioMed Central Ltd. 2011. This article is published under license to BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License ( |
| collection_details |
GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2027 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 |
| container_issue |
Suppl 9 |
| title_short |
Performance of random forests and logic regression methods using mini-exome sequence data |
| url |
https://dx.doi.org/10.1186/1753-6561-5-S9-S104 |
| remote_bool |
true |
| author2 |
Li, Qing Cropp, Cheryl D Sung, Heejong Cai, Juanliang Simpson, Claire L Perry, Brian Dasgupta, Abhijit Malley, James D Wilson, Alexander F Bailey-Wilson, Joan E |
| author2Str |
Li, Qing Cropp, Cheryl D Sung, Heejong Cai, Juanliang Simpson, Claire L Perry, Brian Dasgupta, Abhijit Malley, James D Wilson, Alexander F Bailey-Wilson, Joan E |
| ppnlink |
559080840 |
| mediatype_str_mv |
c |
| isOA_txt |
true |
| hochschulschrift_bool |
false |
| doi_str |
10.1186/1753-6561-5-S9-S104 |
| up_date |
2024-07-03T19:30:33.730Z |
| _version_ |
1803587455535808512 |
| fullrecord_marcxml |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">SPR028445910</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230520001838.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">201007s2011 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1186/1753-6561-5-S9-S104</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)SPR028445910</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(SPR)1753-6561-5-S9-S104-e</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Kim, Yoonhee</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Performance of random forests and logic regression methods using mini-exome sequence data</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2011</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© Kim et al; licensee BioMed Central Ltd. 2011. This article is published under license to BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License (</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract Machine learning approaches are an attractive option for analyzing large-scale data to detect genetic variants that contribute to variation of a quantitative trait, without requiring specific distributional assumptions. We evaluate two machine learning methods, random forests and logic regression, and compare them to standard simple univariate linear regression, using the Genetic Analysis Workshop 17 mini-exome data. We also apply these methods after collapsing multiple rare variants within genes and within gene pathways. Linear regression and the random forest method performed better when rare variants were collapsed based on genes or gene pathways than when each variant was analyzed separately. Logic regression performed better when rare variants were collapsed based on genes rather than on pathways.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Random Forest</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Causal Gene</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Random Forest Model</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Random Forest Method</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Random Forest Analysis</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Li, Qing</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Cropp, Cheryl D</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Sung, Heejong</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Cai, Juanliang</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Simpson, Claire L</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Perry, Brian</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Dasgupta, Abhijit</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Malley, James D</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Wilson, Alexander F</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Bailey-Wilson, Joan E</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">BMC proceedings</subfield><subfield code="d">London : BioMed Central, 2007</subfield><subfield code="g">5(2011), Suppl 9 vom: 29. Nov.</subfield><subfield code="w">(DE-627)559080840</subfield><subfield code="w">(DE-600)2411867-9</subfield><subfield code="x">1753-6561</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:5</subfield><subfield code="g">year:2011</subfield><subfield code="g">number:Suppl 9</subfield><subfield code="g">day:29</subfield><subfield code="g">month:11</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://dx.doi.org/10.1186/1753-6561-5-S9-S104</subfield><subfield code="z">kostenfrei</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_SPRINGER</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-PHA</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_20</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_22</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_23</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_24</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_31</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_39</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_40</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_60</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_62</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_63</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_65</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_69</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_73</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_74</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_95</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_105</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_110</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_151</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_161</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_170</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_206</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_213</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_230</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_285</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_293</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_602</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2005</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2009</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2011</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2014</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2027</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2055</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2111</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4012</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4037</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4125</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4126</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4249</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4305</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4306</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4307</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4313</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4322</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4323</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4324</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4325</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4338</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4367</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4700</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">5</subfield><subfield code="j">2011</subfield><subfield code="e">Suppl 9</subfield><subfield code="b">29</subfield><subfield code="c">11</subfield></datafield></record></collection>
|
| score |
7.401309 |