Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Cancer Survival Analysis01:21

Cancer Survival Analysis

784
Cancer survival analysis focuses on quantifying and interpreting the time from a key starting point, such as diagnosis or the initiation of treatment, to a specific endpoint, such as remission or death. This analysis provides critical insights into treatment effectiveness and factors that influence patient outcomes, helping to shape clinical decisions and guide prognostic evaluations. A cornerstone of oncology research, survival analysis tackles the challenges of skewed, non-normally...
784
Mouse Models of Cancer Study02:43

Mouse Models of Cancer Study

6.6K
Mice have long served as models for studying human biology and pathology because of their phylogenetic and physiological similarity with humans. They are also easy to maintain and breed in the laboratory, and hence, many inbred strains are now available for research. Studies on mice have contributed immeasurably to our understanding of cancer biology.
The development of transgenic, knockout, and knock-in mice has led to an exponential increase in their use as model organisms in research,...
6.6K
Adaptive Mechanisms in Cancer Cells02:53

Adaptive Mechanisms in Cancer Cells

7.2K
Cancer cells accumulate genetic changes at an abnormally rapid rate due to the defects in the DNA repair mechanisms. From an evolutionary perspective, such genetic instability is advantageous for cancer development. Mutant cell lines accumulate a series of beneficial mutations that contribute to their progression into cancer.
Some of the advantages that cancer cells have on normal cells include - enhanced ability to divide without terminally differentiating, induce new blood vessel formation,...
7.2K
Classification of Systems-I01:26

Classification of Systems-I

610
Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
610
Classification of Systems-II01:31

Classification of Systems-II

522
Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
522
Classification of Leukocytes01:30

Classification of Leukocytes

6.3K
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...
6.3K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Influence of trough currents on Permian reef-shoal belts and reef-capping dolomite reservoirs, Damaoping Block, Sichuan Basin, China.

Scientific reportsยท2026
Same author

Combined epigenomic landscapes of 5mC, 5hmC, and 6mA modifications in papillary thyroid carcinogenesis.

Communications biologyยท2026
Same author

Network-Level Mechanisms of Sustained Recovery from Mental Fatigue Differentially Modulated by Acute Exercise and Rest.

International journal of neural systemsยท2026
Same author

Machine learning-based predictive model for the diagnosis of PH pulmonary hypertension diagnosis integrating cardiac magnetic resonance imaging and clinical biomarkers.

BMC medical imagingยท2026
Same author

In-depth analysis of the RNA editing landscape in intracranial aneurysms and its potential role in alternative splicing.

Computational and structural biotechnology journalยท2025
Same author

An explainable machine learning model for predicting chronic coronary disease and identifying valuable text features.

Frontiers in cardiovascular medicineยท2025

Related Experiment Video

Updated: Feb 17, 2026

Quantifying the Brain Metastatic Tumor Micro-Environment using an Organ-On-A Chip 3D Model, Machine Learning, and Confocal Tomography
09:53

Quantifying the Brain Metastatic Tumor Micro-Environment using an Organ-On-A Chip 3D Model, Machine Learning, and Confocal Tomography

Published on: August 16, 2020

7.8K

Cancer Classification Based on Support Vector Machine Optimized by Particle Swarm Optimization and Artificial Bee

Lingyun Gao1, Mingquan Ye2, Changrong Wu3

  • 1School of Medical Information, Wannan Medical College, Wuhu 241002, China. anningxia55@163.com.

Molecules (Basel, Switzerland)
|November 30, 2017
PubMed
Summary

This study introduces PA-SVM, an optimized Support Vector Machine for cancer classification. It effectively filters features and improves classification accuracy across diverse cancer datasets, demonstrating robust performance.

Keywords:
ABCPSOSVMcancer classificationintelligent optimization

Related Experiment Videos

Last Updated: Feb 17, 2026

Quantifying the Brain Metastatic Tumor Micro-Environment using an Organ-On-A Chip 3D Model, Machine Learning, and Confocal Tomography
09:53

Quantifying the Brain Metastatic Tumor Micro-Environment using an Organ-On-A Chip 3D Model, Machine Learning, and Confocal Tomography

Published on: August 16, 2020

7.8K

Area of Science:

  • Computational biology
  • Bioinformatics
  • Machine learning in healthcare

Background:

  • Complex nonlinear problems in cancer research benefit from intelligent optimization algorithms.
  • Feature selection is crucial for enhancing the quality of cancer classification models.
  • Existing methods may struggle with the complexity and dimensionality of cancer datasets.

Purpose of the Study:

  • To develop an improved cancer classification method using optimized machine learning algorithms.
  • To enhance feature selection for better identification of relevant cancer-related data.
  • To evaluate the performance of the proposed method on multiple cancer datasets.

Main Methods:

  • Utilized Fast Correlation-Based Feature selection (FCBF) to filter irrelevant and redundant features.
  • Developed a novel PA-SVM classifier by optimizing Support Vector Machine (SVM) with Particle Swarm Optimization (PSO) and Artificial Bee Colony (ABC) algorithms.
  • Applied the PA-SVM method to nine diverse cancer datasets, including outcome prediction and ovarian cancer protein data.

Main Results:

  • The PA-SVM method demonstrated effectiveness in improving cancer classification accuracy.
  • Feature selection using FCBF successfully reduced data complexity and redundancy.
  • Comparative analysis showed the robustness of PA-SVM across various data types and cancer datasets.

Conclusions:

  • The proposed PA-SVM method offers a robust and effective approach for cancer classification.
  • Intelligent optimization algorithms combined with feature selection significantly enhance classification performance.
  • PA-SVM shows promise for applications in cancer outcome prediction and biomarker discovery.