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Related Experiment Video

Updated: Dec 15, 2025

Mapping Alzheimer's Disease Variants to Their Target Genes Using Computational Analysis of Chromatin Configuration

Mapping Alzheimer's Disease Variants to Their Target Genes Using Computational Analysis of Chromatin Configuration

Published on: January 9, 2020

Analysis of high-dimensional genomic data using MapReduce based probabilistic neural network.

Santos Kumar Baliarsingh¹, Swati Vipsita², Amir H Gandomi³

¹School of Computer Engineering, KIIT Deemed to be University, Bhubaneswar, Odisha, India.

Computer Methods and Programs in Biomedicine

|July 11, 2020

Summary

This summary is machine-generated.

This study introduces a novel distributed method for analyzing large genomics data. The proposed approach effectively handles high-dimensional microarray data, offering accurate and robust classification results.

Keywords:

Fisher score Grey wolf optimization Hadoop MapReduce Probabilistic neural network ReliefF

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Last Updated: Dec 15, 2025

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A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types

Published on: December 10, 2012

Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

Genomics data size is rapidly increasing, overwhelming conventional analysis techniques.
High-dimensional datasets require efficient, parallel processing methods for effective analysis.

Purpose of the Study:

To develop a novel distributed method for processing and classifying high-dimensional genomics data.
To enhance feature selection and classification accuracy for microarray datasets.

Main Methods:

A Map-Reduce (MR)-based approach incorporating MR-based Fisher score (mrFScore) and MR-based ReliefF (mrRefiefF) for feature selection.
MR-based probabilistic neural network (mrPNN) utilizing a weighted chaotic grey wolf optimization (WCGWO) technique for optimal smoothing parameter selection.
Implementation of the proposed algorithms within the Hadoop framework for distributed processing.

Main Results:

The proposed WCGWO-mrPNN model demonstrated superior performance in high-dimensional microarray classification compared to existing techniques.
Comparative analysis on multiple microarray datasets confirmed the model's effectiveness.
Successful implementation and validation using the Hadoop framework.

Conclusions:

The developed distributed method provides an accurate and robust framework for analyzing large-scale, high-dimensional genomics data.
This approach facilitates the application of parallel programming on Hadoop clusters for advanced bioinformatics analysis.
The proposed scheme is a reliable tool for microarray data analysis.