Knowledge Discovery in Reaction Databases:  Landscaping Organic Reactions by a Self-Organizing Neural Network

Chen, Lingran; Gasteiger, Johann

doi:10.1021/ja960027b

Cited by 73 publications

(55 citation statements)

References 24 publications

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“…[35] The SOM algorithm has been used in various disciplines [e.g., Palakal et al, 1995;Chen and Gasteiger, 1997]. In meteorology, the SOM has been used for synoptic classification of weather states [Cavazos, 1999;Hewitson and Crane, 2002], for climate study and downscaling of seasonal forecast [Malmgren and Winter, 1999;Gutiérrez et al, 2005], for cloud classification [Ambroise et al, 2000], for ENSO variability and diagnostic studies [Leloup et al, 2007], for Indian Ocean variability studies [Tozuka et al, 2008;Morioka et al, 2010] among others.…”

Section: Discussionmentioning

confidence: 99%

Can El Niño–Southern Oscillation (ENSO) events modulate intraseasonal oscillations of Indian summer monsoon?

et al. 2011

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[1] Prediction of interannual variability (IAV) of Indian summer monsoon (ISM) rainfall is limited by "internal" dynamics, and the monsoon intraseasonal oscillations (MISOs) seems to be at the heart of producing internal IAV of the ISM. If one could find an identifiable way through which these MISOs are modulated by slowly varying "external" forcing, such as El Niño-Southern Oscillation (ENSO), the uncertainty in the prediction of IAV could be reduced, leading to improvement of seasonal prediction. Such efforts, so far, have been inconclusive. In this study, the modulation of MISOs by ENSO is assessed by using a nonlinear pattern recognition technique known as the Self-Organizing Map (SOM). The SOM technique is efficient in handling the nonlinearity/event-to-event variability of the MISOs and capable of identifying various shades of MISO from large-scale dynamical/thermodynamical indices, without providing information on rainfall. It is shown that particular MISO phases are preferred during ENSO years, that is, the canonical break phase is preferred more in the El Niño years and the typical active phase is preferred during La Niña years. Interestingly, if the SOM clustering is done by removing the ENSO effect on seasonal mean, the preference for the break node remains relatively unchanged; whereas, the preference reduces/vanishes for the active node. The results indicate that the El Niño-break relationship is almost independent of the ENSO-monsoon relationship on seasonal scale whereas the La Niña-active association seems to be interwoven with the seasonal relationship.

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Section: Discussionmentioning

confidence: 99%

Can El Niño–Southern Oscillation (ENSO) events modulate intraseasonal oscillations of Indian summer monsoon?

et al. 2011

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“…The SOM algorithm has been used in various disciplines (e.g., Palakal et al 1995;Chen and Gasteiger 1997). In meteorology, the SOM is used for synoptic classification of weather states (Cavazos 1999;Hewitson and Crane 2002), climate study and downscaling of seasonal forecasts (Malmgren and Winter 1999;Gutiér-rez et al 2005), cloud classification (Ambroise et al 2000), ENSO variability and diagnostic studies (Leloup et al 2007), and so on.…”

Section: Methodsmentioning

confidence: 99%

Objective Identification of Nonlinear Convectively Coupled Phases of Monsoon Intraseasonal Oscillation: Implications for Prediction

Chattopadhyay

Sahai

Goswami

2008

Journal of the Atmospheric Sciences

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The nonlinear convectively coupled character of the summer monsoon intraseasonal oscillation (ISO) that manifests in its event-to-event variations is a major hurdle for skillful extended-range prediction of the active/break episodes. The convectively coupled character of the monsoon ISO implies that a particular nonlinear phase of the precipitation ISO is linked to a unique pattern of the large-scale variables. A methodology has been presented to capture different nonlinear phases of the precipitation ISO using a combination of a sufficiently large number of dynamical variables. This is achieved through a nonlinear pattern recognition technique known as self-organizing map (SOM) involving six daily large-scale circulation indices. It is demonstrated that the nonlinearly classified states of the large-scale circulation isolated at the SOM nodes without involving any information on rainfall are strongly linked to different phases of evolution of the rainfall ISO, including the active and break phases. While a lower SOM classification involving 9 different states identify the composite phases of the rainfall ISO, a higher SOM classification involving 81 states can identify different shades of composite phase of the rainfall ISO. The concept of isolating the nonlinear states, as well as the technique of doing so, is robust as almost identical phases of precipitation ISO are identified by the large-scale circulation indices derived from two different reanalysis datasets, namely, the 40-yr ECMWF Re-Analysis (ERA-40) and the NCEP-NCAR reanalysis.The ability of the SOM technique to isolate spatial structure and evolutionary history of nonlinear convectively coupled states of the summer monsoon ISO opens up a new possibility of extended-range prediction of summer monsoon ISO. This knowledge is used to develop an analog technique for predicting different phases of monsoon ISO. Skillful four-pentad lead prediction of rainfall over central India is demonstrated with the model using only large-scale circulation fields. A major strength of the model is that it can easily be used for real-time extended-range prediction of monsoons.

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“…BDK is an extension of the Kohonen's self-organizing map [6][7][8][9][10]. It consists of two separate Kohonen maps: one for the input X and another map for the output Y.…”

Section: Introductionmentioning

confidence: 99%

Visualization and Chemical Interpretation of Multi-Target Structure-Activity Relationships Using SOMPLS

長谷川¹,

船津²

2011

J. Comput. Aided Chem.

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In quantitative structure-activity relationships (QSAR), partial least squares (PLS) are of particular interest as a statistical method. Since successful applications of PLS to QSAR data set, PLS has evolved for coping with more demands associated with complex data structures. Especially, PLS variants focusing on visualization and chemical interpretation are highly desirable in modeling multi-target structure-activity relationships. In this paper, we employed the self-organized PLS (SOMPLS) approach to predict multiple inhibitory activities against three serine protease receptors (Thrombin, Trypsin and Factor Xa). Volsurf descriptors were used as chemical descriptors.

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Knowledge Discovery in Reaction Databases: Landscaping Organic Reactions by a Self-Organizing Neural Network

Cited by 73 publications

References 24 publications

Can El Niño–Southern Oscillation (ENSO) events modulate intraseasonal oscillations of Indian summer monsoon?

Can El Niño–Southern Oscillation (ENSO) events modulate intraseasonal oscillations of Indian summer monsoon?

Objective Identification of Nonlinear Convectively Coupled Phases of Monsoon Intraseasonal Oscillation: Implications for Prediction

Visualization and Chemical Interpretation of Multi-Target Structure-Activity Relationships Using SOMPLS

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