• Corpus ID: 212596702


  author={Archana Mishra and R. Devi and Sachin Shrivastava},
The concept of Data mining is used in various medical applications like tumor classification, protein structure prediction, gene classification, cancer classification based on microarray data, clustering of gene expression data, statistical model of protein-protein interaction etc. Adverse drug events in prediction of medical test effectiveness can be done based on genomics and proteomics through data mining approaches. Cancer detection is one of the hot research topics in the bioinformatics… 

Tables from this paper

A framework model using multifilter feature selection to enhance colon cancer classification

A framework proposing a two-stage multifilter hybrid model of feature selection for colon cancer classification, which involves the selection of genes before classification techniques are used, improves success rates for the identification of cancer cells.

An Overview on Bioinformatics

  • Computer Science
    Machine Learning in Cancer Research With Applications in Colon Cancer and Big Data Analysis
  • 2021
This chapter presents a thorough background and deep literature review of the current topic of study, and defines the key concepts utilised throughout this investigation.



Machine Learning in DNA Microarray Analysis for Cancer Classification

This paper attempts to explore many features and classifiers using three benchmark datasets to systematically evaluate the performances of the feature selection methods and machine learning classifiers.

Report on BIOKDD04: workshop on data mining in Bioinformatics

BIOKDD'04 was held in conjunction with the 10th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, in Seattle, WA, in August 2004. There are numerous sources of biological

Data mining in bioinformatics using Weka

UNLABELLED The Weka machine learning workbench provides a general-purpose environment for automatic classification, regression, clustering and feature selection-common data mining problems in

Knowledge Discovery in Databases: An Overview

This talk defines the basic notions in data mining and KDD, defines the goals, present motivation, and gives a high-level definition of the KDD Process and how it relates to Data Mining, and focuses on data mining methods.

Data Categorization and Noise Analysis in Mobile Communication Using Machine Learning Algorithms

The base stations and also the noise levels in the busy hour can be predicted and MOR (Number of originating calls successful) predicted as best associated attribute based on Apriori and Genetic search 12:1 ratio.

Insurance Risk Modeling Using Data Mining Technology

The UPA (Underwriting Profitability Analysis) application embodies a new approach to mining Property & Casualty (P&C) insurance policy and claims data for the purpose of constructing predictive

Microarray data mining with visual programming

The system presented here enables users who are not programmers to manage microarray and genomic data flow and to customize their analyses by combining common data analysis tools to fit their needs.

Cancer Screening in the United States, 2010: A Review of Current American Cancer Society Guidelines and Issues in Cancer Screening

The current ACS guidelines and recent issues are summarized, updates of guidelines for testing for early breast cancer detection by the US Preventive Services Task Force and for prevention and early detection of cervical cancer from the American College of Obstetricians and Gynecologists are addressed, and the most recent data from the National Health Interview Survey pertaining to participation rates in cancer screening are described.

Mining topic-specific concepts and definitions on the web

The goal is to help people learn in-depth knowledge of a topic systematically on the Web, and the proposed techniques first identify those sub-topics or salient concepts of the topic, and then find and organize those informative pages, containing definitions and descriptions of thetopic and sub- topics, just like those in a book.

Data-Mining Concepts

This chapter contains sections titled: Introduction Data-Mining Roots data-mining process large data sets data Warehouses for data Mining business aspects of data mining business Aspects of Data Mining: Why a data-mining project Fails.