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Related Concept Videos

Classification of Leukocytes01:30

Classification of Leukocytes

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Leukocytes are classified into two groups based on the presence or absence of cytoplasmic granules. Granular leukocytes, which contain granules, belong to the myeloid lineage and are divided into three subtypes: neutrophils, eosinophils, and basophils. These cells are roughly spherical and characterized by the granules in their cytoplasm.
Neutrophils are the most abundant type of granular leukocytes, comprising 50-70% of all leukocytes. They feature small, evenly distributed granules and a...
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Related Experiment Video

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Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model
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Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model

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Analysis of microarray leukemia data using an efficient MapReduce-based K-nearest-neighbor classifier.

Mukesh Kumar1, Nitish Kumar Rath1, Santanu Kumar Rath1

  • 1Department of Computer Science and Engineering, NIT Rourkela, Orissa 769008, India.

Journal of Biomedical Informatics
|March 16, 2016
PubMed
Summary
This summary is machine-generated.

This study introduces MapReduce-based statistical methods for efficient feature selection and classification of big microarray data. These novel approaches significantly reduce processing time for cancer gene expression analysis.

Keywords:
Big dataClassificationHadoopK-nearest neighborMapReduceMicroarray

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Microarray gene expression profiling is crucial for cancer research, generating vast, time-sensitive big data.
  • Analyzing large microarray datasets quickly is essential due to the dynamic nature of cancer.
  • Identifying significant cancer-related genes from noisy expression data presents a major challenge.

Purpose of the Study:

  • To propose efficient MapReduce-based statistical methods for feature selection in microarray data.
  • To implement a MapReduce-based K-nearest neighbor (mrKNN) classifier for cancer gene expression data.
  • To comparatively analyze the performance of these MapReduce models on diverse microarray datasets.

Main Methods:

  • Development and implementation of various MapReduce-based statistical tests for feature selection.
  • Application of a MapReduce-based K-nearest neighbor (mrKNN) algorithm for data classification.
  • Utilizing the Hadoop framework for parallel processing of large-scale microarray datasets.

Main Results:

  • MapReduce-based statistical methods effectively select relevant features from big microarray data.
  • The mrKNN classifier demonstrates efficient classification of cancer gene expression profiles.
  • Comparative analysis shows significant reduction in execution time compared to conventional models.

Conclusions:

  • MapReduce frameworks offer a scalable and efficient solution for analyzing big microarray data.
  • Proposed methods accelerate the identification of significant genes and cancer classification.
  • This approach is vital for timely and accurate cancer diagnosis and treatment strategies.