scholarly journals Examining applying high performance genetic data feature selection and classification algorithms for colon cancer diagnosis

2017 ◽  
Vol 146 ◽  
pp. 11-24 ◽  
Author(s):  
Murad Al-Rajab ◽  
Joan Lu ◽  
Qiang Xu
Keyword(s):  
Colon Cancer ◽  
Feature Selection ◽  
Cancer Diagnosis ◽  
High Performance ◽  
Genetic Data ◽  
Classification Algorithms

Research Approach With Machine Learning Underpinned

2021 ◽  
pp. 63-97
Keyword(s):  
Machine Learning ◽  
Colon Cancer ◽  
Feature Selection ◽  
High Performance ◽  
Classification Algorithms ◽  
Research Approach ◽  
Research Questions ◽  
Selection Algorithms ◽  
Gene Feature

This chapter describes several methodologies and proposed models used to examine the accuracy and efficiency of high-performance colon-cancer feature selection and classification algorithms to solve the problems identified in Chapter 2. An elaboration of the diverse methods of gene/feature selection algorithms and the related classification algorithms implemented throughout this study are presented. A prototypical methodology blueprint for each experiment is developed to answer the research questions in Chapter 1. Each system model is also presented, and the measures used to validate the performance of the model's outcome are discussed.

scholarly journals Long-term weight loss after colorectal cancer diagnosis is associated with lower survival: The Colon Cancer Family Registry

Cancer ◽  
2017 ◽  
Vol 123 (23) ◽  
pp. 4701-4708 ◽  
Author(s):  
Jonathan M. Kocarnik ◽  
Xinwei Hua ◽  
Sheetal Hardikar ◽  
Jamaica Robinson ◽  
Noralane M. Lindor ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Colon Cancer ◽  
Weight Loss ◽  
Cancer Diagnosis ◽  
Cancer Family ◽  
Lower Survival ◽  
Colon Cancer Family Registry

scholarly journals Evaluation of the antitumor effects of PP242 in a colon cancer xenograft mouse model using comprehensive metabolomics and lipidomics

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Md Mamunur Rashid ◽  
Hyunbeom Lee ◽  
Byung Hwa Jung
Keyword(s):  
Colon Cancer ◽  
Mouse Model ◽  
High Performance ◽  
Tca Cycle ◽  
Treated Group ◽  
Metabolic Changes ◽  
Antitumor Effects ◽  
Xenograft Mouse Model ◽  
Metabolic Mechanism ◽  
Comprehensive Metabolomics

Abstract PP242, an inhibitor of mechanistic target of rapamycin (mTOR), displays potent anticancer effects against various cancer types. However, the underlying metabolic mechanism associated with the PP242 effects is not clearly understood. In this study, comprehensive metabolomics and lipidomics investigations were performed using ultra-high-performance chromatography-Orbitrap-mass spectrometry (UHPLC-Orbitrap-MS) in plasma and tumor tissue to reveal the metabolic mechanism of PP242 in an LS174T cell-induced colon cancer xenograft mouse model. After 3 weeks of PP242 treatment, a reduction in tumor size and weight was observed without any critical toxicities. According to results, metabolic changes due to the effects of PP242 were not significant in plasma. In contrast, metabolic changes in tumor tissues were very significant in the PP242-treated group compared to the xenograft control (XC) group, and revealed that energy and lipid metabolism were mainly altered by PP242 treatment like other cancer inhibitors. Additionally, in this study, it was discovered that not only TCA cycle but also fatty acid β-oxidation (β-FAO) for energy metabolism was inhibited and clear reduction in glycerophospholipid was observed. This study reveals new insights into the underlying anticancer mechanism of the dual mTOR inhibitor PP242, and could help further to facilitate the understanding of PP242 effects in the clinical application.

scholarly journals Transgelin interacts with PARP1 and affects Rho signaling pathway in human colon cancer cells

2020 ◽  
Author(s):  
Zhen-xian Lew ◽  
Hui-min Zhou ◽  
Yuan-yuan Fang ◽  
Zhen Ye ◽  
Wa Zhong ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Colon Cancer ◽  
Transcription Factor ◽  
High Performance Liquid Chromatography ◽  
Cancer Cells ◽  
Signaling Pathway ◽  
High Performance ◽  
Colon Cancer Cells ◽  
Over Expression ◽  
Key Genes

Abstract Background: Transgelin, an actin-binding protein, is associated with the cytoskeleton remodeling. Our previous studies found that transgelin was up-regulated in node-positive colorectal cancer versus in node-negative disease. Over-expression of TAGLN affected the expression of 256 downstream transcripts and increased the metastatic potential of colon cancer cells in vitro and in vivo. This study aims to explore the mechanisms that transgelin participates in the metastasis of colon cancer cells.Methods: Immunofluorescence and immunoblotting analysis were used to determine the cellular localization of the endogenous and exogenous transgelin in colon cancer cells. Co-immunoprecipitation and subsequent high performance liquid chromatography/tandem mass spectrometry were performed to identify the proteins potentially interacting with transgelin. Bioinformatics methods were used to analyze the 256 downstream transcripts regulated by transgelin to discriminate the specific key genes and signaling pathways. By analyzing the promoter region of these key genes, GCBI tools were used to predict the potential transcription factor(s) for these genes. The predicted transcription factors were matching to the proteins that have been identified to potentially interact with transgelin. The interaction between transgelin and these transcription factors was verified by co-immunoprecipitation and immunoblotting.Results: Transgelin was found to localize both in the cytoplasm and the nucleus of colon cancer cells. 297 proteins have been identified to interact with transgelin by co-immunoprecipitation and subsequent high performance liquid chromatography/mass spectrometry. Over-expression of TAGLN could lead to differential expression of 184 downstream genes. By constructing the network of gene-encoded proteins, 7 genes (CALM1, MYO1F, NCKIPSD, PLK4, RAC1, WAS and WIPF1) have been discriminated as key genes using network topology analysis. They are mostly involved in the Rho signaling pathway. Poly ADP-ribose polymerase-1 (PARP1) was predicted as the unique transcription factor for the key genes and concurrently matching to the DNA-binding proteins potentially interacting with transgelin. Immunoprecipitation validated that PARP1 interacted with transgelin in human RKO colon cancer cells.Conclusions: The results of this study suggest that transgelin binds to PARP1 and regulates the expression of the downstream key genes mainly involving Rho signaling pathway, thus participates in the metastasis of colon cancer.

scholarly journals Alteration of Ornithine Metabolic Pathway in Colon Cancer and Multivariate Data Modelling for Cancer Diagnosis

ONCOLOGIE ◽  
2021 ◽  
Vol 23 (2) ◽  
pp. 203-217
Author(s):  
Xin Hu ◽  
Fangyu Jing ◽  
Qingjun Wang ◽  
Linyang Shi ◽  
Yunfeng Cao ◽  
...  
Keyword(s):  
Colon Cancer ◽  
Cancer Diagnosis ◽  
Metabolic Pathway ◽  
Multivariate Data ◽  
Data Modelling ◽  
Multivariate Data Modelling

scholarly journals A Survey on Phishing Detection and The Importance of Feature Selection In Data Mining Classification Algorithms

2020 ◽  
pp. 11-18
Keyword(s):  
Data Mining ◽  
Feature Selection ◽  
Support Vector ◽  
Classification Algorithms ◽  
End User ◽  
Preparation Methods ◽  
Survey Paper ◽  
Vector Machines ◽  
Feature Selection Techniques ◽  
Phishing Detection

: In this era of Internet, the issue of security of information is at its peak. One of the main threats in this cyber world is phishing attacks which is an email or website fraud method that targets the genuine webpage or an email and hacks it without the consent of the end user. There are various techniques which help to classify whether the website or an email is legitimate or fake. The major contributors in the process of detection of these phishing frauds include the classification algorithms, feature selection techniques or dataset preparation methods and the feature extraction that plays an important role in detection as well as in prevention of these attacks. This Survey Paper studies the effect of all these contributors and the approaches that are applied in the study conducted on the recent papers. Some of the classification algorithms that are implemented includes Decision tree, Random Forest , Support Vector Machines, Logistic Regression , Lazy K Star, Naive Bayes and J48 etc.

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