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Ensemble machine learning on gene expression data for cancer classification
2003
Applied Bioinformatics
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In this paper, we focus on three different supervised machine learning techniques in cancer classification, namely C4.5 decision tree, and bagged and boosted decision trees. ...
We have performed classification tasks on seven publicly available cancerous microarray data and compared the classification/prediction performance of these methods. ...
Acknowledgements We would like to thank colleagues in the Bioinformatics Research Centre for constructive discussions, and specifically Gilleain Torrance and Yves Deville for their useful comments and ...
pmid:15130820
fatcat:jfdkruxtnngldcmpv2sya6l6ga
Loss of CHGA Protein as a Potential Biomarker for Colon Cancer Diagnosis: A Study on Biomarker Discovery by Machine Learning and Confirmation by Immunohistochemistry in Colorectal Cancer Tissue Microarrays
2022
Cancers
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early diagnosis of colon cancer patients. ...
We started with searching for protein biomarkers based on our colorectal cancer biomarker database (CBD), finding differential expressed genes (GEGs) and non-DEGs from RNA sequencing (RNA-seq) data, and ...
The authors would like to express their gratitude to Siyu Qiao and Yu Shao for their help in network topology knowledge and Guang Hu, Dirk Repsilber, Xuye Yuan, and Shunming Liu in Machine Learning. ...
doi:10.3390/cancers14112664
pmid:35681650
pmcid:PMC9179857
fatcat:ltm6o7l6qzbrvpkujk7ual33xy
MULTI FILTER ENSEMBLE METHOD FOR CANCER PROGNOSIS AND DIAGNOSIS
2019
International Journal of Engineering Applied Sciences and Technology
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We thus proposed a multi filter ensemble based hybrid gene selection method. ...
The nature of the gene expression profiles are high dimension, very small sample size, continuous types so it is really a challenged task to achieve good classification accuracy from the tumor samples. ...
These five datasets are having two class such as normal and cancerous. Datasets chosen are leukemia, prostate cancer, Colon cancer, DLBCL, lung cancer. ...
doi:10.33564/ijeast.2019.v04i02.019
fatcat:ivp32htegrclrhpslljpgcucja
Loss of CHGA protein as a potential biomarker for colon cancer diagnosis: a study on biomarker discovery by machine learning and confirmation by immunohistochemistry in colorectal cancer tissue microarrays
[article]
2022
medRxiv
pre-print
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(PPI) networks by machine learning (ML) methods. ...
the normal colon and adjacent mucosa to colon cancer may be used as a valuable biomarker for early diagnosis of colon adenocarcinoma ...
Support Vector Machine (SVM) is a supervised based ML method focusing on classification and regression analysis, which has been developed as a popular method in bioinformatics since its good accuracy and ...
doi:10.1101/2022.04.04.22271362
fatcat:jzf55hcdjzbqbfutvlc6lqafqa
A Review on Recent Progress in Machine Learning and Deep Learning Methods for Cancer Classification on Gene Expression Data
2021
Processes
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The development of cancer classification methods based on ML and DL is mostly focused on this review. ...
The development of many machine learning methods for insight analysis in cancer classification has brought a lot of improvement in healthcare. ...
Acknowledgments: This work was supported by the Ministry of Higher Education under Fundamental Research Grant Scheme-RACER (Grant number: RACER/1/2019/ICT02/UMP//3) and Universiti Malaysia Pahang under ...
doi:10.3390/pr9081466
fatcat:mehvzadmn5dafauq4lqifb2vky
A Network-Based Methodology to Identify Subnetwork Markers for Diagnosis and Prognosis of Colorectal Cancer
2021
Frontiers in Genetics
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In comparison to individual gene biomarkers, subnetwork markers based on integrated multi-omics and network analyses may lead to better disease classification, diagnosis, and prognosis. ...
In this study, we developed and tested a novel network-based method to detect subnetwork markers for patients with colorectal cancer (CRC). ...
Identification of Biomarkers Associated with Diagnosis and Prognosis of Colorectal Cancer Patients Based on Integrated Bioinformatics Analysis. ...
doi:10.3389/fgene.2021.721949
pmid:34790220
pmcid:PMC8591094
fatcat:gol2qzvazvhijhfscfqe7q3qcm
Machine Learning Approach to Predict Lung Cancer using CT scan Images
2019
International Journal of Advanced Trends in Computer Science and Engineering
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Machine learning-based Classification method is used to detect whether the nodule is either benign or malignant. ...
Machine learning techniques can identify the patterns in complex data set and it can effectively predict cancer. ...
ACKNOWLEDGEMENT The initial work of this paper is presented in the International Conference on Wireless Sensor Networks, Ubiquitous Computing and Applications (ICWUA) held in Malaysia on 03.09.2019. ...
doi:10.30534/ijatcse/2019/48862019
fatcat:thpsafbjefhi5h3ud6pqvz3cqy
Deep Learning on Histopathological Images for Colorectal Cancer Diagnosis: A Systematic Review
2022
Diagnostics
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In histopathology, algorithms based on Deep Learning (DL) have the potential to assist in diagnosis, predict clinically relevant molecular phenotypes and microsatellite instability, identify histological ...
Herein, we aim to systematically review the current research on AI in CRC image analysis. ...
Machine learning is a branch of AI which is based on the concept that machines could have access to data and be able to learn on their own. ...
doi:10.3390/diagnostics12040837
pmid:35453885
pmcid:PMC9028395
fatcat:kl6elydxbnc7xcrlwsj2ptvzxu
The Clinical Value of Blood miR-654-5p, miR-126, miR-10b, and miR-144 in the Diagnosis of Colorectal Cancer
2022
Computational and Mathematical Methods in Medicine
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Collectively, these four feature miRNAs might be tumor biomarkers in the serum, and our study offers innovative thinking on early-stage CRC diagnosis. ...
Subsequently, principal component analysis (PCA) for dimensionality reduction was performed on samples based on the miR-654-5p, miR-126, miR-10b, and miR-144 expression data. ...
[20] trained medulloblastoma stemness index based on a machine learning method of one-class logistic regression to obtain gene expression-based stemness index and methylation-based stemness index and ...
doi:10.1155/2022/8225966
pmid:36277010
pmcid:PMC9584656
fatcat:mcyrtjabvrfudnhqg5apr2tg7e
Gene Expression Mining for Predicting Survivability of Patients in Earlystages of Lung Cancer
2014
International Journal on Bioinformatics & Biosciences
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Lung cancer accounts roughly for 30% of all cancer-related deaths in the world. Diagnosis and treatments are still based on traditional histopathology. ...
This paper describes a state-of-the-art machine learning based approach called averaged one-dependence estimators with subsumption resolution to tackle the problem of predicting whether a patient in early ...
Lung cancer prognosis essentially depends on the subtype and stage of the lung cancer. ...
doi:10.5121/ijbb.2014.4201
fatcat:hq7tlf52crbdddu3uvuje7kj4e
Cancer classification based on gene expression using neural networks
2015
Genetics and Molecular Research
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Based on gene expression, we have classified 53 colon cancer patients with UICC II into two groups: relapse and no relapse. Samples were taken from each patient, and gene information was extracted. ...
Classification accuracy obtained by S-Kohonen neural network reaches 91%, which was more accurate than classification by BP and SVM neural networks. ...
Distribution of samples of colon cancer with UICC II stage. ...
doi:10.4238/2015.december.21.33
pmid:26782405
fatcat:pfom5rh3gbcsnp6cjy7lwwhody
Machine Learning Techniques used for the Histopathological Image Analysis of Oral Cancer-A Review
2020
The Open Bioinformatics Journal
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A lot of computer aided analysis techniques have been developed by utilizing machine learning strategies for prediction and prognosis of cancer. ...
It is the most common cancer with fourteen deaths in an hour on a yearly basis, as per the WHO oral cancer incidence in India. ...
ACKNOWLEDGEMENTS The authors thank reviewers of this work for their valuable comments and suggestions that improve the presentation of this research effort. ...
doi:10.2174/1875036202013010106
fatcat:jmq3khvfhbh6pag6nbyocyvg7u
Multi-class Cancer Classification and Biomarker Identification using Deep Learning
[article]
2020
bioRxiv
pre-print
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This research revolves around multi-class cancer classification, feature extraction and relevant gene identification through deep learning methods for 12 different types of cancers using RNA-SEQ from The ...
Advent of machine learning helped researchers in supervised and unsupervised learning tasks along with gene identification but resourcefulness has not been overtly satisfactory. ...
Acknowledgements This study could not have been without the guidance and support of my supervisor Dr. Saira Karim. ...
doi:10.1101/2020.12.24.424317
fatcat:o4cjyititjcnpj7h43ewmzlm44
Supervised Machine Learning Model for High Dimensional Gene Data in Colon Cancer Detection
2015
2015 IEEE International Congress on Big Data
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A lot of research has been conducted on clustering approaches, extreme learning machine and so on. They are usuallly applied in a shallow neural network model. ...
A lot of research has been conducted on clustering approaches, extreme learning machine and so on. They are usuallly applied in a shallow neural network model. ...
In particular, numerous machine learning classification techniques have been used for the classification of cancerous and normal tissues. ...
doi:10.1109/bigdatacongress.2015.28
dblp:conf/bigdata/ChenZS0Z15
fatcat:x7bwoqgnsnba7k3pn52rb6ska4
Analyzing gene expression data for pediatric and adult cancer diagnosis using logic learning machine and standard supervised methods
2019
BMC Bioinformatics
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Logic Learning Machine (LLM) is an innovative method of supervised analysis capable of constructing models based on simple and intelligible rules. ...
LLM showed an excellent accuracy (sAUC = 0.99, 95%CI: 0.98-1.0) and outperformed any other method except SVM. LLM is a new powerful tool for the analysis of gene expression data for cancer diagnosis. ...
At least at our knowledge, DT is the most largely employed method of machine learning based on simple threshold rules. ...
doi:10.1186/s12859-019-2953-8
pmid:31757200
pmcid:PMC6873393
fatcat:q2ril4ijdbgpvamsmeogqlm5xi
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