Artificial intelligence in cancer target identification and drug discovery
Artificial intelligence is an advanced method to identify novel anticancer targets and discover
novel drugs from biology networks because the networks can effectively preserve and …
novel drugs from biology networks because the networks can effectively preserve and …
Swarm intelligence algorithms for feature selection: a review
Featured Application The paper analyzes the usage and mechanisms of feature selection
methods that are based on swarm intelligence in different application areas. Abstract The …
methods that are based on swarm intelligence in different application areas. Abstract The …
A comprehensive survey on particle swarm optimization algorithm and its applications
Y Zhang, S Wang, G Ji - Mathematical problems in engineering, 2015 - Wiley Online Library
Particle swarm optimization (PSO) is a heuristic global optimization method, proposed
originally by Kennedy and Eberhart in 1995. It is now one of the most commonly used …
originally by Kennedy and Eberhart in 1995. It is now one of the most commonly used …
An efficient binary chimp optimization algorithm for feature selection in biomedical data classification
Accurate classification of high-dimensional biomedical data highly depends on the efficient
recognition of the data's main features which can be used to assist diagnose related …
recognition of the data's main features which can be used to assist diagnose related …
Efficient machine learning technique for tumor classification based on gene expression data
C Venkatesan, D Balamurugan… - 2022 8th …, 2022 - ieeexplore.ieee.org
In bioinformatics research, cancer classification is a crucial domain. The use of microarray
technology to identify specific illnesses is common. A small number of genes uncovered in …
technology to identify specific illnesses is common. A small number of genes uncovered in …
Binary black hole algorithm for feature selection and classification on biological data
Biological data often consist of redundant and irrelevant features. These features can lead to
misleading in modeling the algorithms and overfitting problem. Without a feature selection …
misleading in modeling the algorithms and overfitting problem. Without a feature selection …
Gene selection from microarray gene expression data for classification of cancer subgroups employing PSO and adaptive K-nearest neighborhood technique
These days, microarray gene expression data are playing an essential role in cancer
classifications. However, due to the availability of small number of effective samples …
classifications. However, due to the availability of small number of effective samples …
Hybrid method based on information gain and support vector machine for gene selection in cancer classification
L Gao, M Ye, X Lu, D Huang - Genomics, Proteomics and …, 2017 - academic.oup.com
It remains a great challenge to achieve sufficient cancer classification accuracy with the
entire set of genes, due to the high dimensions, small sample size, and big noise of gene …
entire set of genes, due to the high dimensions, small sample size, and big noise of gene …
Machine learning based computational gene selection models: a survey, performance evaluation, open issues, and future research directions
N Mahendran, PM Durai Raj Vincent… - Frontiers in …, 2020 - frontiersin.org
Gene Expression is the process of determining the physical characteristics of living beings
by generating the necessary proteins. Gene Expression takes place in two steps, translation …
by generating the necessary proteins. Gene Expression takes place in two steps, translation …
C-HMOSHSSA: Gene selection for cancer classification using multi-objective meta-heuristic and machine learning methods
Background and objective: Over the last two decades, DNA microarray technology has
emerged as a powerful tool for early cancer detection and prevention. It helps to provide a …
emerged as a powerful tool for early cancer detection and prevention. It helps to provide a …