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DOI | 10.1126/science.aal2014 |
Predicting human olfactory perception from chemical features of odor molecules | |
Keller, Andreas1; Gerkin, Richard C.2; Guan, Yuanfang3; Dhurandhar, Amit4; Turu, Gabor5,6; Szalai, Bence5,6; Mainland, Joel D.7,8; Ihara, Yusuke7,9; Yu, Chung Wen7; Wolfinger, Russ10; Vens, Celine11; Schietgat, Leander12; De Grave, Kurt12,13; Norel, Raquel4; Stolovitzky, Gustavo4,15; Cecchi, Guillermo A.4; Vosshall, Leslie B.1,14; Meyer, Pablo4,15 | |
2017-02-24 | |
发表期刊 | SCIENCE
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ISSN | 0036-8075 |
EISSN | 1095-9203 |
出版年 | 2017 |
卷号 | 355期号:6327页码:820-+ |
文章类型 | Article |
语种 | 英语 |
国家 | USA; Hungary; Japan; Belgium |
英文摘要 | It is still not possible to predict whether a given molecule will have a perceived odor or what olfactory percept it will produce. We therefore organized the crowd-sourced DREAM Olfaction Prediction Challenge. Using a large olfactory psychophysical data set, teams developed machine-learning algorithms to predict sensory attributes of molecules based on their chemoinformatic features. The resulting models accurately predicted odor intensity and pleasantness and also successfully predicted 8 among 19 rated semantic descriptors ("garlic," "fish," "sweet," "fruit," "burnt," "spices," "flower," and "sour"). Regularized linear models performed nearly as well as random forest-based ones, with a predictive accuracy that closely approaches a key theoretical limit. These models help to predict the perceptual qualities of virtually any molecule with high accuracy and also reverse-engineer the smell of a molecule. |
领域 | 地球科学 ; 气候变化 ; 资源环境 |
收录类别 | SCI-E |
WOS记录号 | WOS:000395127600037 |
WOS关键词 | PLEASANTNESS ; PROFILES |
WOS类目 | Multidisciplinary Sciences |
WOS研究方向 | Science & Technology - Other Topics |
URL | 查看原文 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://119.78.100.173/C666/handle/2XK7JSWQ/195492 |
专题 | 地球科学 资源环境科学 气候变化 |
作者单位 | 1.Rockefeller Univ, Lab Neurogenet & Behav, New York, NY 10065 USA; 2.Arizona State Univ, Sch Life Sci, Tempe, AZ 85281 USA; 3.Univ Michigan, Dept Computat Med & Bioinformat, Ann Arbor, MI 48109 USA; 4.IBM Corp, Thomas J Watson Computat Biol Ctr, Yorktown Hts, NY 10598 USA; 5.Semmelweis Univ, Fac Med, Dept Physiol, H-1085 Budapest, Hungary; 6.Semmelweis Univ MTA SE, Hungarian Acad Sci, Lab Mol Physiol, H-1085 Budapest, Hungary; 7.Monell Chem Senses Ctr, 3500 Market St, Philadelphia, PA 19104 USA; 8.Univ Penn, Dept Neurosci, Philadelphia, PA 19104 USA; 9.Ajinomoto Co Inc, Inst Innovat, Kawasaki, Kanagawa 2108681, Japan; 10.SAS Inst Inc, Cary, NC 27513 USA; 11.Katholieke Univ Leuven, Dept Publ Hlth & Primary Care, B-8500 Kortrijk, Belgium; 12.Katholieke Univ Leuven, Dept Comp Sci, B-3001 Leuven, Belgium; 13.Flanders Make, B-3920 Lommel, Belgium; 14.Howard Hughes Med Inst, New York, NY 10065 USA; 15.Icahn Sch Med Mt Sinai, Dept Genet & Genom Sci, New York, NY 10029 USA |
推荐引用方式 GB/T 7714 | Keller, Andreas,Gerkin, Richard C.,Guan, Yuanfang,et al. Predicting human olfactory perception from chemical features of odor molecules[J]. SCIENCE,2017,355(6327):820-+. |
APA | Keller, Andreas.,Gerkin, Richard C..,Guan, Yuanfang.,Dhurandhar, Amit.,Turu, Gabor.,...&Meyer, Pablo.(2017).Predicting human olfactory perception from chemical features of odor molecules.SCIENCE,355(6327),820-+. |
MLA | Keller, Andreas,et al."Predicting human olfactory perception from chemical features of odor molecules".SCIENCE 355.6327(2017):820-+. |
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