Microarray-Based Cancer Prediction Using Single-Gene Ensemble Classifier

2018 ◽  
pp. 589-600
Author(s):  
Ziyi Yang ◽  
Yanqiong Ren ◽  
Hui Zhang ◽  
Yong Liang
Keyword(s):  
Single Gene ◽  
Ensemble Classifier ◽  
Cancer Prediction

scholarly journals Breast Cancer Prediction and Diagnosis through a New Approach based on Majority Voting Ensemble Classifier

2021 ◽  
Vol 191 ◽  
pp. 481-486
Author(s):  
Mohammed Amine Naji ◽  
Sanaa El Filali ◽  
Meriem Bouhlal ◽  
EL Habib Benlahmar ◽  
Rachida Ait Abdelouhahid ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Ensemble Classifier ◽  
Majority Voting ◽  
New Approach ◽  
Cancer Prediction

A Hybrid of Random Over Sample Examples and Boosted C5.0 Algorithms for Breast Cancer Diagnosis on Imbalanced Data

2020 ◽  
Vol 10 (11) ◽  
pp. 2686-2692
Author(s):  
Jianxue Tian ◽  
Jue Zhang ◽  
Xiaofen Tang ◽  
Ting Dong
Keyword(s):  
Breast Cancer ◽  
Cancer Diagnosis ◽  
Imbalanced Data ◽  
Breast Cancer Diagnosis ◽  
Clinical Decision ◽  
Ensemble Classifier ◽  
Breast Cancer Dataset ◽  
Cancer Dataset ◽  
Cancer Prediction ◽  
Imbalanced Data Classification

To surmount the two-class imbalanced problem existing in the breast cancer diagnosis, a hybrid method of ROSE sampling approach with Boosted C5.0 ensemble classifier (R-Boosted C5.0) is proposed. ROSE as the sampling method is utilized to balance the class distribution. Boosted C5.0 is then used as the classifier. To serve this purpose, Wisconsin Breast Cancer Dataset (WBCD), Wisconsin Diagnosis Breast Cancer (WDBC) and three imbalanced datasets have been studied. Assessing by Matthews Correlation Coefficient (MCC), the performance of proposed method on WBCD and WDBC datasets were 98.5% and 93.0%, respectively. The experimental results show that the proposed work outperforms in contrast with the rest of the classifiers. It can be used as a clinical decision support system to assist breast cancer prediction. In practice, the proposed methodology can be further applied to class imbalanced data classification.

Linking transcription, RNA polymerase II elongation and alternative splicing

2020 ◽  
Vol 477 (16) ◽  
pp. 3091-3104 ◽  
Author(s):  
Luciana E. Giono ◽  
Alberto R. Kornblihtt
Keyword(s):  
Gene Expression ◽  
Alternative Splicing ◽  
Rna Polymerase Ii ◽  
Environmental Changes ◽  
Transcription Elongation ◽  
Single Gene ◽  
Regulation Of Gene Expression ◽  
Regulation Of Transcription ◽  
Stepping Stones ◽  
Eukaryotic Transcription

Gene expression is an intricately regulated process that is at the basis of cell differentiation, the maintenance of cell identity and the cellular responses to environmental changes. Alternative splicing, the process by which multiple functionally distinct transcripts are generated from a single gene, is one of the main mechanisms that contribute to expand the coding capacity of genomes and help explain the level of complexity achieved by higher organisms. Eukaryotic transcription is subject to multiple layers of regulation both intrinsic — such as promoter structure — and dynamic, allowing the cell to respond to internal and external signals. Similarly, alternative splicing choices are affected by all of these aspects, mainly through the regulation of transcription elongation, making it a regulatory knob on a par with the regulation of gene expression levels. This review aims to recapitulate some of the history and stepping-stones that led to the paradigms held today about transcription and splicing regulation, with major focus on transcription elongation and its effect on alternative splicing.

154: Integration of TPSA and High-Throughput Mass Spectrometry Data Improves Prostate Cancer Prediction

2007 ◽  
Vol 177 (4S) ◽  
pp. 52-53
Author(s):  
Stefano Ongarello ◽  
Eberhard Steiner ◽  
Regina Achleitner ◽  
Isabel Feuerstein ◽  
Birgit Stenzel ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Mass Spectrometry ◽  
High Throughput ◽  
Mass Spectrometry Data ◽  
Cancer Prediction

An update on HLA alleles as pharmacogenetic markers for antiepileptic drug-induced cutaneous adverse reaction

2019 ◽  
Vol 2 (2) ◽  
pp. 1-17
Author(s):  
Sue-Mian Then ◽  
Azman Ali Raymond
Keyword(s):  
Single Gene ◽  
Hypersensitivity Syndrome ◽  
Allelic Frequency ◽  
Stevens Johnson Syndrome ◽  
Case Control Studies ◽  
Drug Induced ◽  
Hla Alleles ◽  
Exfoliative Dermatitis ◽  
Johnson Syndrome ◽  
Systemic Symptoms

Epilepsy is a common neurological disorder affecting approximately 50 million people worldwide. Antiepileptic drugs (AEDs) are commonly used to treat the disease depending, mainly on the type of seizure. However, the use of AEDs may also lead to cutaneous adverse drug reactions (cADR) such as toxic epidermal necrolysis (TEN), Stevens–Johnson syndrome (SJS), exfoliative dermatitis (ED) and drug‐induced hypersensitivity syndrome/drug reaction with eosinophilia and systemic symptoms (DIHS/DRESS), which are unwanted comorbidities in epilepsy. It was first discovered that the HLA-B*15:02 allele was strongly associated with carbamazepine (CBZ)-induced SJS/TEN among Han Chinese and this led to the discovery of other HLA alleles and cytochrome P450 (CYP) genes that were significantly associated with various AED-induced cADRs across various populations.  This mini review is an update on the latest findings of the involvement of various HLA alleles and CYP alleles in cADRs caused by CBZ, phenytoin (PHT), oxcarbazepine (OXC) and lamitrogine (LTG) in different case-control studies around the world. From our review, we found that CBZ- and PHT-induced cADRs were more commonly reported than the other AEDs. Therefore, there were more robust pharmacogenetics studies related to these AEDs. OXC- and LTG-induced cADRs were less commonly reported, and so more studies are needed to validate the reported association of the newer reported HLA alleles with these AEDs. It is also important to take into account the allelic frequency within a given population before drawing conclusions about the use of these alleles as genetic markers to prevent AED-induced cADR. Overall, the current body of research point to a combination of alleles as a better pharmacogenetic marker compared to the use of a single gene as a genetic marker for AED-induced cADR.

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