Fuzzy clustering as rational partition method for QSAR
Various methods are used to make the partition of data sets for QSAR development and model validation. In this work we used a fuzzy minimals partitioning and we compare this methodology with another rational partition methods like k-means clustering (KMS) and Minimal Test Set Dissimilarity (MTSD). F...
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Gespeichert in:
| Autor*in: |
Pérez-Garrido, Alfonso [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2017transfer abstract |
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| Schlagwörter: |
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| Umfang: |
6 |
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| Übergeordnetes Werk: |
Enthalten in: Migration and characterisation of nanosilver from food containers by AF4-ICP-MS - Artiaga, G. ELSEVIER, 2015, Amsterdam [u.a.] |
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| Übergeordnetes Werk: |
volume:166 ; year:2017 ; day:15 ; month:07 ; pages:1-6 ; extent:6 |
| Links: |
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| DOI / URN: |
10.1016/j.chemolab.2017.04.006 |
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ELV040340961 |
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| 520 | |a Various methods are used to make the partition of data sets for QSAR development and model validation. In this work we used a fuzzy minimals partitioning and we compare this methodology with another rational partition methods like k-means clustering (KMS) and Minimal Test Set Dissimilarity (MTSD). For the development of QSAR models Ordinary Least Squares (OLS) and Extreme Learning Machine (ELM) methods were used. The generated QSAR equations were validated by the coefficient of determination of the internal leave one out (LOO) cross validation method Q LOO 2 and then the coefficient of the external test set Q ext 2 was compared between partition methods. The results of this comparison showed that using fuzzy minimal for big and structurally diverse data sets gave an applicability domain similar to KMS and a better predictability models than both methods, KMS and MTSD. | ||
| 520 | |a Various methods are used to make the partition of data sets for QSAR development and model validation. In this work we used a fuzzy minimals partitioning and we compare this methodology with another rational partition methods like k-means clustering (KMS) and Minimal Test Set Dissimilarity (MTSD). For the development of QSAR models Ordinary Least Squares (OLS) and Extreme Learning Machine (ELM) methods were used. The generated QSAR equations were validated by the coefficient of determination of the internal leave one out (LOO) cross validation method Q LOO 2 and then the coefficient of the external test set Q ext 2 was compared between partition methods. The results of this comparison showed that using fuzzy minimal for big and structurally diverse data sets gave an applicability domain similar to KMS and a better predictability models than both methods, KMS and MTSD. | ||
| 650 | 7 | |a Validation |2 Elsevier | |
| 650 | 7 | |a Minimal Test Set Dissimilarity |2 Elsevier | |
| 650 | 7 | |a Fuzzy clustering |2 Elsevier | |
| 650 | 7 | |a Extreme Learning Machine |2 Elsevier | |
| 650 | 7 | |a QSAR |2 Elsevier | |
| 650 | 7 | |a Data partition |2 Elsevier | |
| 650 | 7 | |a Regression |2 Elsevier | |
| 650 | 7 | |a k-means clustering |2 Elsevier | |
| 700 | 1 | |a Girón-Rodríguez, Francisco |4 oth | |
| 700 | 1 | |a Bueno-Crespo, Andrés |4 oth | |
| 700 | 1 | |a Soto, Jesús |4 oth | |
| 700 | 1 | |a Pérez-Sánchez, Horacio |4 oth | |
| 700 | 1 | |a Helguera, Aliuska Morales |4 oth | |
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10.1016/j.chemolab.2017.04.006 doi GBV00000000000076A.pica (DE-627)ELV040340961 (ELSEVIER)S0169-7439(17)30193-4 DE-627 ger DE-627 rakwb eng 540 540 DE-600 540 VZ 35.00 bkl Pérez-Garrido, Alfonso verfasserin aut Fuzzy clustering as rational partition method for QSAR 2017transfer abstract 6 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Various methods are used to make the partition of data sets for QSAR development and model validation. In this work we used a fuzzy minimals partitioning and we compare this methodology with another rational partition methods like k-means clustering (KMS) and Minimal Test Set Dissimilarity (MTSD). For the development of QSAR models Ordinary Least Squares (OLS) and Extreme Learning Machine (ELM) methods were used. The generated QSAR equations were validated by the coefficient of determination of the internal leave one out (LOO) cross validation method Q LOO 2 and then the coefficient of the external test set Q ext 2 was compared between partition methods. The results of this comparison showed that using fuzzy minimal for big and structurally diverse data sets gave an applicability domain similar to KMS and a better predictability models than both methods, KMS and MTSD. Various methods are used to make the partition of data sets for QSAR development and model validation. In this work we used a fuzzy minimals partitioning and we compare this methodology with another rational partition methods like k-means clustering (KMS) and Minimal Test Set Dissimilarity (MTSD). For the development of QSAR models Ordinary Least Squares (OLS) and Extreme Learning Machine (ELM) methods were used. The generated QSAR equations were validated by the coefficient of determination of the internal leave one out (LOO) cross validation method Q LOO 2 and then the coefficient of the external test set Q ext 2 was compared between partition methods. The results of this comparison showed that using fuzzy minimal for big and structurally diverse data sets gave an applicability domain similar to KMS and a better predictability models than both methods, KMS and MTSD. Validation Elsevier Minimal Test Set Dissimilarity Elsevier Fuzzy clustering Elsevier Extreme Learning Machine Elsevier QSAR Elsevier Data partition Elsevier Regression Elsevier k-means clustering Elsevier Girón-Rodríguez, Francisco oth Bueno-Crespo, Andrés oth Soto, Jesús oth Pérez-Sánchez, Horacio oth Helguera, Aliuska Morales oth Enthalten in Elsevier Science Artiaga, G. ELSEVIER Migration and characterisation of nanosilver from food containers by AF4-ICP-MS 2015 Amsterdam [u.a.] (DE-627)ELV012980455 volume:166 year:2017 day:15 month:07 pages:1-6 extent:6 https://doi.org/10.1016/j.chemolab.2017.04.006 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA GBV_ILN_40 GBV_ILN_62 35.00 Chemie: Allgemeines VZ AR 166 2017 15 0715 1-6 6 045F 540 |
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10.1016/j.chemolab.2017.04.006 doi GBV00000000000076A.pica (DE-627)ELV040340961 (ELSEVIER)S0169-7439(17)30193-4 DE-627 ger DE-627 rakwb eng 540 540 DE-600 540 VZ 35.00 bkl Pérez-Garrido, Alfonso verfasserin aut Fuzzy clustering as rational partition method for QSAR 2017transfer abstract 6 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Various methods are used to make the partition of data sets for QSAR development and model validation. In this work we used a fuzzy minimals partitioning and we compare this methodology with another rational partition methods like k-means clustering (KMS) and Minimal Test Set Dissimilarity (MTSD). For the development of QSAR models Ordinary Least Squares (OLS) and Extreme Learning Machine (ELM) methods were used. The generated QSAR equations were validated by the coefficient of determination of the internal leave one out (LOO) cross validation method Q LOO 2 and then the coefficient of the external test set Q ext 2 was compared between partition methods. The results of this comparison showed that using fuzzy minimal for big and structurally diverse data sets gave an applicability domain similar to KMS and a better predictability models than both methods, KMS and MTSD. Various methods are used to make the partition of data sets for QSAR development and model validation. In this work we used a fuzzy minimals partitioning and we compare this methodology with another rational partition methods like k-means clustering (KMS) and Minimal Test Set Dissimilarity (MTSD). For the development of QSAR models Ordinary Least Squares (OLS) and Extreme Learning Machine (ELM) methods were used. The generated QSAR equations were validated by the coefficient of determination of the internal leave one out (LOO) cross validation method Q LOO 2 and then the coefficient of the external test set Q ext 2 was compared between partition methods. The results of this comparison showed that using fuzzy minimal for big and structurally diverse data sets gave an applicability domain similar to KMS and a better predictability models than both methods, KMS and MTSD. Validation Elsevier Minimal Test Set Dissimilarity Elsevier Fuzzy clustering Elsevier Extreme Learning Machine Elsevier QSAR Elsevier Data partition Elsevier Regression Elsevier k-means clustering Elsevier Girón-Rodríguez, Francisco oth Bueno-Crespo, Andrés oth Soto, Jesús oth Pérez-Sánchez, Horacio oth Helguera, Aliuska Morales oth Enthalten in Elsevier Science Artiaga, G. ELSEVIER Migration and characterisation of nanosilver from food containers by AF4-ICP-MS 2015 Amsterdam [u.a.] (DE-627)ELV012980455 volume:166 year:2017 day:15 month:07 pages:1-6 extent:6 https://doi.org/10.1016/j.chemolab.2017.04.006 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA GBV_ILN_40 GBV_ILN_62 35.00 Chemie: Allgemeines VZ AR 166 2017 15 0715 1-6 6 045F 540 |
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10.1016/j.chemolab.2017.04.006 doi GBV00000000000076A.pica (DE-627)ELV040340961 (ELSEVIER)S0169-7439(17)30193-4 DE-627 ger DE-627 rakwb eng 540 540 DE-600 540 VZ 35.00 bkl Pérez-Garrido, Alfonso verfasserin aut Fuzzy clustering as rational partition method for QSAR 2017transfer abstract 6 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Various methods are used to make the partition of data sets for QSAR development and model validation. In this work we used a fuzzy minimals partitioning and we compare this methodology with another rational partition methods like k-means clustering (KMS) and Minimal Test Set Dissimilarity (MTSD). For the development of QSAR models Ordinary Least Squares (OLS) and Extreme Learning Machine (ELM) methods were used. The generated QSAR equations were validated by the coefficient of determination of the internal leave one out (LOO) cross validation method Q LOO 2 and then the coefficient of the external test set Q ext 2 was compared between partition methods. The results of this comparison showed that using fuzzy minimal for big and structurally diverse data sets gave an applicability domain similar to KMS and a better predictability models than both methods, KMS and MTSD. Various methods are used to make the partition of data sets for QSAR development and model validation. In this work we used a fuzzy minimals partitioning and we compare this methodology with another rational partition methods like k-means clustering (KMS) and Minimal Test Set Dissimilarity (MTSD). For the development of QSAR models Ordinary Least Squares (OLS) and Extreme Learning Machine (ELM) methods were used. The generated QSAR equations were validated by the coefficient of determination of the internal leave one out (LOO) cross validation method Q LOO 2 and then the coefficient of the external test set Q ext 2 was compared between partition methods. The results of this comparison showed that using fuzzy minimal for big and structurally diverse data sets gave an applicability domain similar to KMS and a better predictability models than both methods, KMS and MTSD. Validation Elsevier Minimal Test Set Dissimilarity Elsevier Fuzzy clustering Elsevier Extreme Learning Machine Elsevier QSAR Elsevier Data partition Elsevier Regression Elsevier k-means clustering Elsevier Girón-Rodríguez, Francisco oth Bueno-Crespo, Andrés oth Soto, Jesús oth Pérez-Sánchez, Horacio oth Helguera, Aliuska Morales oth Enthalten in Elsevier Science Artiaga, G. ELSEVIER Migration and characterisation of nanosilver from food containers by AF4-ICP-MS 2015 Amsterdam [u.a.] (DE-627)ELV012980455 volume:166 year:2017 day:15 month:07 pages:1-6 extent:6 https://doi.org/10.1016/j.chemolab.2017.04.006 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA GBV_ILN_40 GBV_ILN_62 35.00 Chemie: Allgemeines VZ AR 166 2017 15 0715 1-6 6 045F 540 |
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10.1016/j.chemolab.2017.04.006 doi GBV00000000000076A.pica (DE-627)ELV040340961 (ELSEVIER)S0169-7439(17)30193-4 DE-627 ger DE-627 rakwb eng 540 540 DE-600 540 VZ 35.00 bkl Pérez-Garrido, Alfonso verfasserin aut Fuzzy clustering as rational partition method for QSAR 2017transfer abstract 6 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Various methods are used to make the partition of data sets for QSAR development and model validation. In this work we used a fuzzy minimals partitioning and we compare this methodology with another rational partition methods like k-means clustering (KMS) and Minimal Test Set Dissimilarity (MTSD). For the development of QSAR models Ordinary Least Squares (OLS) and Extreme Learning Machine (ELM) methods were used. The generated QSAR equations were validated by the coefficient of determination of the internal leave one out (LOO) cross validation method Q LOO 2 and then the coefficient of the external test set Q ext 2 was compared between partition methods. The results of this comparison showed that using fuzzy minimal for big and structurally diverse data sets gave an applicability domain similar to KMS and a better predictability models than both methods, KMS and MTSD. Various methods are used to make the partition of data sets for QSAR development and model validation. In this work we used a fuzzy minimals partitioning and we compare this methodology with another rational partition methods like k-means clustering (KMS) and Minimal Test Set Dissimilarity (MTSD). For the development of QSAR models Ordinary Least Squares (OLS) and Extreme Learning Machine (ELM) methods were used. The generated QSAR equations were validated by the coefficient of determination of the internal leave one out (LOO) cross validation method Q LOO 2 and then the coefficient of the external test set Q ext 2 was compared between partition methods. The results of this comparison showed that using fuzzy minimal for big and structurally diverse data sets gave an applicability domain similar to KMS and a better predictability models than both methods, KMS and MTSD. Validation Elsevier Minimal Test Set Dissimilarity Elsevier Fuzzy clustering Elsevier Extreme Learning Machine Elsevier QSAR Elsevier Data partition Elsevier Regression Elsevier k-means clustering Elsevier Girón-Rodríguez, Francisco oth Bueno-Crespo, Andrés oth Soto, Jesús oth Pérez-Sánchez, Horacio oth Helguera, Aliuska Morales oth Enthalten in Elsevier Science Artiaga, G. ELSEVIER Migration and characterisation of nanosilver from food containers by AF4-ICP-MS 2015 Amsterdam [u.a.] (DE-627)ELV012980455 volume:166 year:2017 day:15 month:07 pages:1-6 extent:6 https://doi.org/10.1016/j.chemolab.2017.04.006 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA GBV_ILN_40 GBV_ILN_62 35.00 Chemie: Allgemeines VZ AR 166 2017 15 0715 1-6 6 045F 540 |
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10.1016/j.chemolab.2017.04.006 doi GBV00000000000076A.pica (DE-627)ELV040340961 (ELSEVIER)S0169-7439(17)30193-4 DE-627 ger DE-627 rakwb eng 540 540 DE-600 540 VZ 35.00 bkl Pérez-Garrido, Alfonso verfasserin aut Fuzzy clustering as rational partition method for QSAR 2017transfer abstract 6 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Various methods are used to make the partition of data sets for QSAR development and model validation. In this work we used a fuzzy minimals partitioning and we compare this methodology with another rational partition methods like k-means clustering (KMS) and Minimal Test Set Dissimilarity (MTSD). For the development of QSAR models Ordinary Least Squares (OLS) and Extreme Learning Machine (ELM) methods were used. The generated QSAR equations were validated by the coefficient of determination of the internal leave one out (LOO) cross validation method Q LOO 2 and then the coefficient of the external test set Q ext 2 was compared between partition methods. The results of this comparison showed that using fuzzy minimal for big and structurally diverse data sets gave an applicability domain similar to KMS and a better predictability models than both methods, KMS and MTSD. Various methods are used to make the partition of data sets for QSAR development and model validation. In this work we used a fuzzy minimals partitioning and we compare this methodology with another rational partition methods like k-means clustering (KMS) and Minimal Test Set Dissimilarity (MTSD). For the development of QSAR models Ordinary Least Squares (OLS) and Extreme Learning Machine (ELM) methods were used. The generated QSAR equations were validated by the coefficient of determination of the internal leave one out (LOO) cross validation method Q LOO 2 and then the coefficient of the external test set Q ext 2 was compared between partition methods. The results of this comparison showed that using fuzzy minimal for big and structurally diverse data sets gave an applicability domain similar to KMS and a better predictability models than both methods, KMS and MTSD. Validation Elsevier Minimal Test Set Dissimilarity Elsevier Fuzzy clustering Elsevier Extreme Learning Machine Elsevier QSAR Elsevier Data partition Elsevier Regression Elsevier k-means clustering Elsevier Girón-Rodríguez, Francisco oth Bueno-Crespo, Andrés oth Soto, Jesús oth Pérez-Sánchez, Horacio oth Helguera, Aliuska Morales oth Enthalten in Elsevier Science Artiaga, G. ELSEVIER Migration and characterisation of nanosilver from food containers by AF4-ICP-MS 2015 Amsterdam [u.a.] (DE-627)ELV012980455 volume:166 year:2017 day:15 month:07 pages:1-6 extent:6 https://doi.org/10.1016/j.chemolab.2017.04.006 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA GBV_ILN_40 GBV_ILN_62 35.00 Chemie: Allgemeines VZ AR 166 2017 15 0715 1-6 6 045F 540 |
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Enthalten in Migration and characterisation of nanosilver from food containers by AF4-ICP-MS Amsterdam [u.a.] volume:166 year:2017 day:15 month:07 pages:1-6 extent:6 |
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Enthalten in Migration and characterisation of nanosilver from food containers by AF4-ICP-MS Amsterdam [u.a.] volume:166 year:2017 day:15 month:07 pages:1-6 extent:6 |
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Migration and characterisation of nanosilver from food containers by AF4-ICP-MS |
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Pérez-Garrido, Alfonso @@aut@@ Girón-Rodríguez, Francisco @@oth@@ Bueno-Crespo, Andrés @@oth@@ Soto, Jesús @@oth@@ Pérez-Sánchez, Horacio @@oth@@ Helguera, Aliuska Morales @@oth@@ |
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Various methods are used to make the partition of data sets for QSAR development and model validation. In this work we used a fuzzy minimals partitioning and we compare this methodology with another rational partition methods like k-means clustering (KMS) and Minimal Test Set Dissimilarity (MTSD). For the development of QSAR models Ordinary Least Squares (OLS) and Extreme Learning Machine (ELM) methods were used. The generated QSAR equations were validated by the coefficient of determination of the internal leave one out (LOO) cross validation method Q LOO 2 and then the coefficient of the external test set Q ext 2 was compared between partition methods. The results of this comparison showed that using fuzzy minimal for big and structurally diverse data sets gave an applicability domain similar to KMS and a better predictability models than both methods, KMS and MTSD. |
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Various methods are used to make the partition of data sets for QSAR development and model validation. In this work we used a fuzzy minimals partitioning and we compare this methodology with another rational partition methods like k-means clustering (KMS) and Minimal Test Set Dissimilarity (MTSD). For the development of QSAR models Ordinary Least Squares (OLS) and Extreme Learning Machine (ELM) methods were used. The generated QSAR equations were validated by the coefficient of determination of the internal leave one out (LOO) cross validation method Q LOO 2 and then the coefficient of the external test set Q ext 2 was compared between partition methods. The results of this comparison showed that using fuzzy minimal for big and structurally diverse data sets gave an applicability domain similar to KMS and a better predictability models than both methods, KMS and MTSD. |
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Various methods are used to make the partition of data sets for QSAR development and model validation. In this work we used a fuzzy minimals partitioning and we compare this methodology with another rational partition methods like k-means clustering (KMS) and Minimal Test Set Dissimilarity (MTSD). For the development of QSAR models Ordinary Least Squares (OLS) and Extreme Learning Machine (ELM) methods were used. The generated QSAR equations were validated by the coefficient of determination of the internal leave one out (LOO) cross validation method Q LOO 2 and then the coefficient of the external test set Q ext 2 was compared between partition methods. The results of this comparison showed that using fuzzy minimal for big and structurally diverse data sets gave an applicability domain similar to KMS and a better predictability models than both methods, KMS and MTSD. |
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