Study on Data Mining with Drug Discovery

2018 ◽  
pp. 513-520
Author(s):  
Bahul Diwan ◽  
Shweta Bhardwaj
Keyword(s):  
Data Mining ◽  
Drug Discovery

Data mining and machine learning for lysosomal disease drug discovery and beyond

2019 ◽  
Vol 126 (2) ◽  
pp. S86 ◽  
Author(s):  
Jennifer J. Klein ◽  
Nancy C. Baker ◽  
Kimberley M. Zorn ◽  
Daniel P. Russo ◽  
Ana P. Rubio ◽  
...  
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Drug Discovery ◽  
Lysosomal Disease

ChemSpaceShuttle: A tool for data mining in drug discovery by classification, projection, and 3D visualization

2003 ◽  
Vol 22 (5) ◽  
pp. 549-559 ◽  
Author(s):  
Alireza Givehchi ◽  
Axel Dietrich ◽  
Paul Wrede ◽  
Gisbert Schneider
Keyword(s):  
Data Mining ◽  
Drug Discovery ◽  
3D Visualization

In vivo metabolite detection and identification in drug discovery via LC-MS/MS with data-dependent scanning and postacquisition data mining

2005 ◽  
Vol 40 (12) ◽  
pp. 1572-1582 ◽  
Author(s):  
Antonio Triolo ◽  
Maria Altamura ◽  
Tula Dimoulas ◽  
Antonio Guidi ◽  
Alessandro Lecci ◽  
...  
Keyword(s):  
Data Mining ◽  
Drug Discovery ◽  
Detection And Identification ◽  
Metabolite Detection

Drug Discovery Applications for KNIME: An Open Source Data Mining Platform

2012 ◽  
Vol 12 (18) ◽  
pp. 1965-1979 ◽  
Author(s):  
Michael P. Mazanetz ◽  
Robert J. Marmon ◽  
Catherine B. T. Reisser ◽  
Inaki Morao
Keyword(s):  
Data Mining ◽  
Drug Discovery ◽  
Open Source ◽  
Open Source Data ◽  
Source Data

Drug Discovery Applications for KNIME: An Open Source Data Mining Platform

2013 ◽  
Vol 12 (18) ◽  
pp. 1965-1979 ◽  
Author(s):  
Michael P. Mazanetz ◽  
Catherine B. T. Reisser ◽  
Robert J. Marmon ◽  
Inaki Morao
Keyword(s):  
Data Mining ◽  
Drug Discovery ◽  
Open Source ◽  
High Throughput Screening ◽  
Drug Targets ◽  
Large Data ◽  
Industrial Applications ◽  
Screening Methods ◽  
Open Source Data ◽  
Source Data

Technological advances in high-throughput screening methods, combinatorial chemistry and the design of virtual libraries have evolved in the pursuit of challenging drug targets. Over the last two decades a vast amount of data has been generated within these fields and as a consequence data mining methods have been developed to extract key pieces of information from these large data pools. Much of this data is now available in the public domain. This has been helpful in the arena of drug discovery for both academic groups and for small to medium sized enterprises which previously would not have had access to such data resources. Commercial data mining software is sometimes prohibitively expensive and the alternate open source data mining software is gaining momentum in both academia and in industrial applications as the costs of research and development continue to rise. KNIME, the Konstanz Information Miner, has emerged as a leader in open source data mining tools. KNIME provides an integrated solution for the data mining requirements across the drug discovery pipeline through a visual assembly of data workflows drawing from an extensive repository of tools. This review will examine KNIME as an open source data mining tool and its applications in drug discovery.

scholarly journals Article Commentary: The Power of the Web in Cancer Drug Discovery and Clinical Trial Design: Research without a Laboratory?

2010 ◽  
Vol 9 ◽  
pp. CIN.S3191 ◽  
Author(s):  
Christine Galustian ◽  
Angus G. Dalgleish
Keyword(s):  
Data Mining ◽  
Drug Discovery ◽  
Design Research ◽  
Clinical Trial Design ◽  
Cancer Drug ◽  
Cancer Treatments ◽  
Protein Targets ◽  
Cancer Drug Discovery ◽  
Use Of Data ◽  
The Usa

The discovery of effective cancer treatments is a key goal for pharmaceutical companies. However, the current costs of bringing a cancer drug to the market in the USA is now estimated at $1 billion per FDA approved drug, with many months of research at the bench and costly clinical trials. A growing number of papers highlight the use of data mining tools to determine associations between drugs, genes or protein targets, and possible mechanism of actions or therapeutic efficacy which could be harnessed to provide information that can refine or direct new clinical cancer studies and lower costs. This report reviews the paper by R.J. Epstein, which illustrates the potential of text mining using Boolean parameters in cancer drug discovery, and other studies which use alternative data mining approaches to aid cancer research.

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