Big data approach with artificial neural network and molecular similarity for chemical data mining and endocrine disruption prediction (Record no. 8346)
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| fixed length control field | a |
| 003 - CONTROL NUMBER IDENTIFIER | |
| control field | OSt |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20190514115928.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 190226b xxu||||| |||| 00| 0 eng d |
| 040 ## - CATALOGING SOURCE | |
| Original cataloging agency | AIKTC-KRRC |
| Transcribing agency | AIKTC-KRRC |
| 100 ## - MAIN ENTRY--PERSONAL NAME | |
| 9 (RLIN) | 7668 |
| Author | Paulose, Renjith |
| 245 ## - TITLE STATEMENT | |
| Title | Big data approach with artificial neural network and molecular similarity for chemical data mining and endocrine disruption prediction |
| 250 ## - EDITION STATEMENT | |
| Volume, Issue number | Vol. 50(4), July-August |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. | |
| Place of publication, distribution, etc. | Mumbai |
| Year | 2018 |
| Name of publisher, distributor, etc. | Wolter Kluwer |
| 300 ## - PHYSICAL DESCRIPTION | |
| Pagination | 169-176 |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc. | Chemical toxicity prediction at early stage drug discovery phase has been researched for years, and newest methods are always investigated. Research data comprising chemical physicochemical properties, toxicity, assay, and activity details create massive data which are becoming difficult to manage. Identifying the desired featured chemical with the desired biological activity from millions of chemicals is a challenging task. AIMS: In this study, we investigate and explore big data technologies and machine learning approaches to do an efficient chemical data mining for endocrine receptor disruption prediction and virtual compound screening. The power of artificial neural network (ANN) in predicting chemicals' activity toward androgen receptor (AR) and estrogen receptor (ER) and thereby classifying into human endocrine disruptor or nondisruptor is investigated. SUBJECTS AND METHODS: Molecules are collected along with their Inhibitory Concentration (IC50) values toward AR and ER. Training and test datasets are created with active and inactive classes of molecules. Molecular fingerprints of Electro Topological State (E-State) are generated for describing every compound. ANN machine learning model is created using Apache Spark and implemented in Hadoop big data environment. Test chemical's structural similarity toward active class of training compounds is estimated and combined with ANN model for improving prediction accuracy. RESULTS: AR and ER predictive models applied on corresponding test datasets gave 86.31% and 89.57% accuracies, respectively, in correctly classifying molecules as disruptor or nondisruptor. Molecular fragments and functional groups are ranked based on their importance in forming ANN model and influence toward the AR and ER disruption behavior. Training molecules that are specific to the test molecules' endocrine disruption prediction are retrieved based on the structural similarity values. CONCLUSIONS: The current study demonstrates a new approach of chemical endocrine receptor disruption prediction combining ANN machine learning method and molecular similarity in a big data environment. This method of predictive modeling can be further tested with more receptors and hormones and predictive power can be examined. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| 9 (RLIN) | 4774 |
| Topical term or geographic name entry element | PHARMACOLOGY |
| 700 ## - ADDED ENTRY--PERSONAL NAME | |
| 9 (RLIN) | 7669 |
| Co-Author | Jagatheesan, Kalirajan |
| 700 ## - ADDED ENTRY--PERSONAL NAME | |
| 9 (RLIN) | 7670 |
| Co-Author | Balakrishnan, Gopal Samy |
| 773 0# - HOST ITEM ENTRY | |
| International Standard Serial Number | 0253-7613 |
| Place, publisher, and date of publication | Andheri - Mumbai Wolters Kluwer India Private Limited |
| Title | Indian Journal of Pharmacology |
| 856 ## - ELECTRONIC LOCATION AND ACCESS | |
| Link text | Click here |
| URL | http://www.ijp-online.com/article.asp?issn=0253-7613;year=2018;volume=50;issue=4;spage=169;epage=176;aulast=Paulose;type=0 |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Source of classification or shelving scheme | Dewey Decimal Classification |
| Koha item type | Articles Abstract Database |
| Withdrawn status | Lost status | Source of classification or shelving scheme | Damaged status | Not for loan | Home library | Current library | Shelving location | Date acquired | Total Checkouts | Barcode | Date last seen | Price effective from | Koha item type |
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| Dewey Decimal Classification | School of Pharmacy | School of Pharmacy | Archieval Section | 29/03/2019 | 2018273 | 19/06/2019 | 29/03/2019 | Articles Abstract Database |