scholarly journals Gene Expression-Assisted Cancer Prediction Techniques

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Tanima Thakur ◽  
Isha Batra ◽  
Monica Luthra ◽  
Shanmuganathan Vimal ◽  
Gaurav Dhiman ◽  
...  
Keyword(s):  
Gene Expression ◽  
Nucleic Acid ◽  
Gene Expression Data ◽  
Genetic Information ◽  
Expression Data ◽  
Next Generation ◽  
Hybrid Species ◽  
Cancer Prediction ◽  
Advantages And Disadvantages ◽  
Prediction Techniques

Cancer is one of the deadliest diseases and with its growing number, its detection and treatment become essential. Researchers have developed various methods based on gene expression. Gene expression is a process that is used to convert deoxyribose nucleic acid (DNA) to ribose nucleic acid (RNA) and then RNA to protein. This protein serves so many purposes, such as creating cells, drugs for cancer, and even hybrid species. As genes carry genetic information from one generation to another, some gene deformity is also transferred to the next generation. Therefore, the deformity needs to be detected. There are many techniques available in the literature to predict cancerous and noncancerous genes from gene expression data. This is an important development from the point of diagnostics and giving a prognosis for the condition. This paper will present a review of some of those techniques from the literature; details about the various datasets on which these techniques are implemented and the advantages and disadvantages.

scholarly journals Next-generation text-mining mediated generation of chemical response-specific gene sets for interpretation of gene expression data

2013 ◽  
Vol 6 (1) ◽  
Author(s):  
Kristina M Hettne ◽  
André Boorsma ◽  
Dorien A M van Dartel ◽  
Jelle J Goeman ◽  
Esther de Jong ◽  
...  
Keyword(s):  
Gene Expression ◽  
Text Mining ◽  
Gene Expression Data ◽  
Specific Gene ◽  
Expression Data ◽  
Next Generation ◽  
Gene Sets ◽  
Chemical Response

Active Learning Using Fuzzy-Rough Nearest Neighbor Classifier for Cancer Prediction from Microarray Gene Expression Data

2019 ◽  
Vol 34 (01) ◽  
pp. 2057001
Author(s):  
Ansuman Kumar ◽  
Anindya Halder
Keyword(s):  
Gene Expression ◽  
Active Learning ◽  
Gene Expression Data ◽  
Nearest Neighbor ◽  
Microarray Gene Expression Data ◽  
Expression Data ◽  
Microarray Gene Expression ◽  
Cancer Prediction ◽  
Microarray Gene ◽  
Neighbor Classifier

Cancer prediction from gene expression data is a very challenging area of research in the field of computational biology and bioinformatics. Conventional classifiers are often unable to achieve desired accuracy due to the lack of ‘sufficient’ training patterns in terms of clinically labeled samples. Active learning technique, in this respect, can be useful as it automatically finds only few most informative (or confusing) samples to get their class labels from the experts and those are added to the training set, which can improve the accuracy of the prediction consequently. A novel active learning technique using fuzzy-rough nearest neighbor classifier (ALFRNN) is proposed in this paper for cancer classification from microarray gene expression data. The proposed ALFRNN method is capable of dealing with the uncertainty, overlapping and indiscernibility often present in cancer subtypes (classes) of the gene expression data. The performance of the proposed method is tested using different real-life microarray gene expression cancer datasets and its performance is compared with five other state-of-the-art techniques (out of which three are active learning-based and two are traditional classification methods) in terms of percentage accuracy, precision, recall, [Formula: see text]-measures and kappa. Superiority of the proposed method over the other counterpart algorithms is established from experimental results for cancer prediction and results of the paired [Formula: see text]-test confirm statistical significance of the results in favor of the proposed method for almost all the datasets.

scholarly journals EXP-PAC: Providing comparative analysis and storage of next generation gene expression data

Genomics ◽  
2012 ◽  
Vol 100 (1) ◽  
pp. 8-13 ◽  
Author(s):  
Philip C. Church ◽  
Andrzej Goscinski ◽  
Christophe Lefèvre
Keyword(s):  
Gene Expression ◽  
Comparative Analysis ◽  
Gene Expression Data ◽  
Expression Data ◽  
Next Generation ◽  
And Storage

scholarly journals Gene Selection using a Hybrid RFE Along with LASSO for Cancer Classification

2019 ◽  
Vol 9 (1S3) ◽  
pp. 83-96
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Gene Selection ◽  
Classification Model ◽  
Recursive Feature Elimination ◽  
Support Vector ◽  
Expression Data ◽  
Cancer Prediction ◽  
Selection Operator ◽  
Selection Of

Gene expression profiling using microarray technology has done with the chip based phenomena. For studying gene expression data are more helpful in knowing various diseases and more useful in finding diseases. Recently in the bioinformatics field, cancer prediction using gene expression data had made the assuring area. Samples having the gene attributes will not surely give the efficient amount of classification. Overcoming these contribution, a strong method is required for selecting the relevant gene features for building the classification model effectively. Basically least absolute shrinkage and selection operator (LASSO) and Recursive feature elimination (RFE) are automatic gene feature selection methods used for classification. Here in our proposed work, we use these two methods as a hybrid one for selecting the features and later it applied into the Support vector machine (SVM) for easy classification. It made best when compared to the existing techniques by their performance measures, were regulated on six publically available cancer datasets. Just out it gives the good awareness in the selection of features.

Sign in / Sign up

Export Citation Format

Share Document