IJERT-Analysing Pharmaceutical Compounds Based On Cluster Techniques (original) (raw)
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Analysis Of Drug Data Mining With Clustering Technique Using K-Means Algorithm
JOURNAL OF PHYSICS: CONFERENCE SERIES, 2021
Data processing is very important in the development of information technology. Almost all fields of work have information data. Data can be used to help analysis in work. At present, health information data is very important to be processed in order to help medical personnel to make decisions. So that the results of the right decision to help patients. Lately, drug data has been misused for information eliminating a depressed patient without a doctor's prescription with a total data of 53766. The results shown are very large. So it requires very much attention from the government. As a result of the deviation of information and applied to the patient will result in death. Therefore, research needs to be conducted to group data on drug data. The source of research data is obtained from the UCI Machine Learning Repository Education website. The method proposed in this research is data mining. This solution can help researchers in the analysis of these data. One technique in data mining with clustering is using the K-means algorithm. The variables used are drug name, condition, useful count. The first research results can classify three categories consisting of using the highest drugs, using medium drugs and using lace drugs. Then the accuracy of the data is obtained with condition 99.45% valid records 53471, drug name 100% with valid records 53766, useful count 100% with valid records 53766.
Feasibility Study of Fuzzy Clustering Techniques in Chemical Database for Compound Classification
2000
First and foremost we would like to express our true and sincere thanks and gratitude to PM Dr Naomie Salim, for the guidance, support and encouragements during the course of this work. Also, we would like to express our thanks to the member of our department for all the help and support during the time spent in the lab. We also take this opportunity to express our appreciation, especially to the staffs and members of the Faculty of Computer Science and Information Systems (FSKSM) for supporting our research.
Citation/Export MLA Sayyada Sara Banu, Dr. Perumal Uma, Mohammed Waseem Ashfaque , Quadri S. S. Ali Ahmed, “Application based technical Approaches of data mining in Pharmaceuticals, and Research approaches in biomedical and Bioinformatics”, March 15 Volume 3 Issue 3 , International Journal on Recent and Innovation Trends in Computing and Communication (IJRITCC), ISSN: 2321-8169, PP: 1278 - 1285, DOI: 10.17762/ijritcc2321-8169.150384 APA Sayyada Sara Banu, Dr. Perumal Uma, Mohammed Waseem Ashfaque , Quadri S. S. Ali Ahmed, March 15 Volume 3 Issue 3, “Application based technical Approaches of data mining in Pharmaceuticals, and Research approaches in biomedical and Bioinformatics”, International Journal on Recent and Innovation Trends in Computing and Communication (IJRITCC), ISSN: 2321-8169, PP: 1278 - 1285, DOI: 10.17762/ijritcc2321-8169.150384
Clustering Based Approach for Isolating the Drug Elements Causing Side Effects
International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 2019
The truthful identification of drug side effects represents a major concern for public health. Medication symptoms or Adverse Drug responses (ADRs) are a vital and complex challenge. In the pharmaceutical business, ADRs are one of the main causes of failure during the time spent in the development of drugs and of drug withdrawal once a medication has achieved the market. Medication used in prescription depends on a balance between expected advantages and conceivable dangers. Adverse Drug Reactions (ADRs) are impacts that happen when a medication is not administered or controlled at the best possible measurements. It is basic to build up an investigation pipeline to computationally foresee drug side effect symptoms from various assorted sources.
PKOM: A tool for clustering, analysis and comparison of big chemical collections
Digital Signal Processing, 2016
We describe the algorithm underlying PKOM, a methodology for clustering, analysis and visualization of multi-dimensional data onto a two-dimensional map. PKOM is based on the mixture of two very popular methods that have been widely used by the pharmaceutical industry for the clustering of genomic or SAR (Structure Activity Relationship) chemical information. The first method at the origin of PKOM is SOM (Self-Organizing Maps), a clustering technique based on neural networks. The second method is TREE MAPS, a visualization method based on hierarchical clustering by dendrograms. We initially describe herein the two methods and the reasons why we have taken the best of both to merge them into PKOM. We then describe in detail the PKOM algorithm and its advantages compared to the two former. Examples are given on how to apply this kind of 2-D topological clustering technique to the organization of big pharmaceutical collections in practical cases.
Clustering of Large Databases of Compounds: Using the MDL “Keys” as Structural Descriptors
Journal of Chemical Information and Computer Sciences, 1997
An analysis of chemical structures from several commercially available libraries of compounds is presented with a view of acquiring compounds for screening. The Jarvis-Patrick clustering method has been applied, using the MDL "keys" as structural descriptors. The nature of the MDL keys is examined in this context, some features of the clustering algorithm are discussed, and clustering statistics are presented.
Dunn’s index for cluster tendency assessment of pharmacological data sets
Canadian Journal of Physiology and Pharmacology, 2012
Cluster tendency assessment is an important stage in cluster analysis. In this sense, a group of promising techniques named visual assessment of tendency (VAT) has emerged in the literature. The presence of clusters can be detected easily through the direct observation of a dark blocks structure along the main diagonal of the intensity image. Alternatively, if the Dunn's index for a single linkage partition is greater than 1, then it is a good indication of the blocklike structure. In this report, the Dunn's index is applied as a novel measure of tendency on 8 pharmacological data sets, represented by machine-learning-selected molecular descriptors. In all cases, observed values are less than 1, thus indicating a weak tendency for data to form compact clusters. Other results suggest that there is an increasing relationship between the Dunn's index as a measure of cluster separability and the classification accuracy of various cluster algorithms tested on the same data sets.
APPLICATIONS OF DATA MINING TECHNIQUES IN PHARMACEUTICAL INDUSTRY
Almost two decades ago, the information flow in the pharmaceutical industry was relatively simple and the application of technology was limited. However, as we progress into a more integrated world where technology has become an integral part of the business processes, the process of transfer of information has become more complicated. Today increasingly technology is being used to help the pharmaceutical firms manage their inventories and to develop new product and services. The implications are such that by a simple process of merging the drug usage and cost of medicines (after completing the legal requirements) with the patient care records of doctors and hospitals helping firms to conduct nation wide trials for its new drugs. Other possible uses of information technology in the field of pharmaceuticals include pricing (two-tier pricing strategy) and exchange of information between vertically integrated drug companies for mutual benefit. Nevertheless, the challenge remains though data collection methods have improved data manipulation techniques are yet to keep pace with them.
Data structures and data transformations for clustering chemical data
TrAC - Trends in Analytical Chemistry, 2001
The quality of a clustering of chemical data is determined by a proper choice of distance measures and data transformations. The latter aspect is often neglected and its importance is shown here. It is also shown that the V-shaped data structure that is often obtained in a principal component analysis of chemical data may indicate that the clustering of the raw data can lead to classi¢cations that are not relevant from a chemical point of view and that the log double centering transform should be considered as a possible alternative. z2001 Published by Elsevier Science B.V.