J Chem Inf Model - Chemical data visualization and analysis with incremental generative topographic mapping: big data challenge.

Tópicos

{ compound(1573) activ(1297) structur(1058) }
{ data(2317) use(1299) case(1017) }
{ algorithm(1844) comput(1787) effici(935) }
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{ perform(999) metric(946) measur(919) }
{ model(2656) set(1616) predict(1553) }
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{ activ(1452) weight(1219) physic(1104) }
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{ framework(1458) process(801) describ(734) }
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{ import(1318) role(1303) understand(862) }
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{ age(1611) year(1155) adult(843) }
{ can(981) present(881) function(850) }
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{ process(1125) use(805) approach(778) }
{ can(774) often(719) complex(702) }
{ data(1737) use(1416) pattern(1282) }
{ inform(2794) health(2639) internet(1427) }
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{ error(1145) method(1030) estim(1020) }
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{ group(2977) signific(1463) compar(1072) }
{ sampl(1606) size(1419) use(1276) }
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{ cancer(2502) breast(956) screen(824) }
{ use(1733) differ(960) four(931) }
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{ estim(2440) model(1874) function(577) }
{ decis(3086) make(1611) patient(1517) }
{ method(1969) cluster(1462) data(1082) }
{ method(2212) result(1239) propos(1039) }
{ detect(2391) sensit(1101) algorithm(908) }

Resumo

This paper is devoted to the analysis and visualization in 2-dimensional space of large data sets of millions of compounds using the incremental version of generative topographic mapping (iGTM). The iGTM algorithm implemented in the in-house ISIDA-GTM program was applied to a database of more than 2 million compounds combining data sets of 36 chemicals suppliers and the NCI collection, encoded either by MOE descriptors or by MACCS keys. Taking advantage of the probabilistic nature of GTM, several approaches to data analysis were proposed. The chemical space coverage was evaluated using the normalized Shannon entropy. Different views of the data (property landscapes) were obtained by mapping various physical and chemical properties (molecular weight, aqueous solubility, LogP, etc.) onto the iGTM map. The superposition of these views helped to identify the regions in the chemical space populated by compounds with desirable physicochemical profiles and the suppliers providing them. The data sets similarity in the latent space was assessed by applying several metrics (Euclidean distance, Tanimoto and Bhattacharyya coefficients) to data probability distributions based on cumulated responsibility vectors. As a complementary approach, data sets were compared by considering them as individual objects on a meta-GTM map, built on cumulated responsibility vectors or property landscapes produced with iGTM. We believe that the iGTM methodology described in this article represents a fast and reliable way to analyze and visualize large chemical databases.

Resumo Limpo

paper devot analysi visual dimension space larg data set million compound use increment version generat topograph map igtm igtm algorithm implement inhous isidagtm program appli databas million compound combin data set chemic supplier nci collect encod either moe descriptor macc key take advantag probabilist natur gtm sever approach data analysi propos chemic space coverag evalu use normal shannon entropi differ view data properti landscap obtain map various physic chemic properti molecular weight aqueous solubl logp etc onto igtm map superposit view help identifi region chemic space popul compound desir physicochem profil supplier provid data set similar latent space assess appli sever metric euclidean distanc tanimoto bhattacharyya coeffici data probabl distribut base cumul respons vector complementari approach data set compar consid individu object metagtm map built cumul respons vector properti landscap produc igtm believ igtm methodolog describ articl repres fast reliabl way analyz visual larg chemic databas

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