scholarly journals Analysis of Important Gene Ontology Terms and Biological Pathways Related to Pancreatic Cancer

2016 ◽  
Vol 2016 ◽  
pp. 1-10 ◽  
Author(s):  
Hang Yin ◽  
ShaoPeng Wang ◽  
Yu-Hang Zhang ◽  
Yu-Dong Cai ◽  
Hailin Liu
Keyword(s):  
Pancreatic Cancer ◽  
Gene Ontology ◽  
Feature Selection Method ◽  
Selection Method ◽  
Biological Processes ◽  
Kegg Pathways ◽  
Extensive Analysis ◽  
The World ◽  
Serious Disease ◽  
Go Terms

Pancreatic cancer is a serious disease that results in more than thirty thousand deaths around the world per year. To design effective treatments, many investigators have devoted themselves to the study of biological processes and mechanisms underlying this disease. However, it is far from complete. In this study, we tried to extract important gene ontology (GO) terms and KEGG pathways for pancreatic cancer by adopting some existing computational methods. Genes that have been validated to be related to pancreatic cancer and have not been validated were represented by features derived from GO terms and KEGG pathways using the enrichment theory. A popular feature selection method, minimum redundancy maximum relevance, was employed to analyze these features and extract important GO terms and KEGG pathways. An extensive analysis of the obtained GO terms and KEGG pathways was provided to confirm the correlations between them and pancreatic cancer.

Analysis of Four Types of Leukemia Using Gene Ontology Term and Kyoto Encyclopedia of Genes and Genomes Pathway Enrichment Scores

2020 ◽  
Vol 23 (4) ◽  
pp. 295-303
Author(s):  
Jing Lu ◽  
YuHang Zhang ◽  
ShaoPeng Wang ◽  
Yi Bi ◽  
Tao Huang ◽  
...  
Keyword(s):  
Gene Ontology ◽  
Lymphoblastic Leukemia ◽  
Feature Selection Method ◽  
Lymphocytic Leukemia ◽  
Myelogenous Leukemia ◽  
Ontology Term ◽  
Kegg Pathways ◽  
Different Types ◽  
First Time ◽  
Go Terms

Aim and Objective: Leukemia is the second common blood cancer after lymphoma, and its incidence rate has an increasing trend in recent years. Leukemia can be classified into four types: acute lymphoblastic leukemia (ALL), acute myeloid leukemia (AML), chronic lymphocytic leukemia (CLL), and chronic myelogenous leukemia (CML). More than forty drugs are applicable to different types of leukemia based on the discrepant pathogenesis. Therefore, the identification of specific drug-targeted biological processes and pathways is helpful to determinate the underlying pathogenesis among such four types of leukemia. Methods: In this study, the gene ontology (GO) terms and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways that were highly related to drugs for leukemia were investigated for the first time. The enrichment scores for associated GO terms and KEGG pathways were calculated to evaluate the drugs and leukemia. The feature selection method, minimum redundancy maximum relevance (mRMR), was used to analyze and identify important GO terms and KEGG pathways. Results: Twenty Go terms and two KEGG pathways with high scores have all been confirmed to effectively distinguish four types of leukemia. Conclusion: This analysis may provide a useful tool for the discrepant pathogenesis and drug design of different types of leukemia.

scholarly journals Computational Approach to Investigating Key GO Terms and KEGG Pathways Associated with CNV

2018 ◽  
Vol 2018 ◽  
pp. 1-9
Author(s):  
YuanYuan Luo ◽  
Yan Yan ◽  
Shiqi Zhang ◽  
Zhen Li
Keyword(s):  
Elderly Population ◽  
Feature Selection Method ◽  
The Elderly ◽  
Selection Method ◽  
Eye Disease ◽  
Extensive Literature ◽  
Biological Mechanisms ◽  
Computational Tools ◽  
Kegg Pathways ◽  
Go Terms

Choroidal neovascularization (CNV) is a severe eye disease that leads to blindness, especially in the elderly population. Various endogenous and exogenous regulatory factors promote its pathogenesis. However, the detailed molecular biological mechanisms of CNV have not been fully revealed. In this study, by using advanced computational tools, a number of key gene ontology (GO) terms and KEGG pathways were selected for CNV. A total of 29 validated genes associated with CNV and 17,639 nonvalidated genes were encoded based on the features derived from the GO terms and KEGG pathways by using the enrichment theory. The widely accepted feature selection method—maximum relevance and minimum redundancy (mRMR)—was applied to analyze and rank the features. An extensive literature review for the top 45 ranking features was conducted to confirm their close associations with CNV. Identifying the molecular biological mechanisms of CNV as described by the GO terms and KEGG pathways may contribute to improving the understanding of the pathogenesis of CNV.

scholarly journals Comparing the Performance of Algorithm with Relevant Features for Histological Categorization of Lung Cancer

2021 ◽  
Vol 12 (2) ◽  
pp. 357-363
Author(s):  
V. NishaJenipher , Et. al.
Keyword(s):  
Lung Cancer ◽  
Selection Process ◽  
Feature Selection Method ◽  
Wrf Model ◽  
Selection Method ◽  
Classification Algorithm ◽  
Prediction Algorithm ◽  
Long Period ◽  
The World ◽  
Performance Of Algorithm

Due to increasing cancer cases around the world, Lung cancer has become the favorite topic of research for a long period of time. The actual reason is due to the increasing rate of new cases across the globe. Therefore, many researchers used prediction or classification algorithm to identify the factors that contribute to the increase of this deadly disease. Two models were built namely WRF and RF. RF model provides the result of features selected by a predominant feature selection method whereas WRF model provides result of all features without performing any selection process. A comparison is made to inform the importance of selecting the feature for classification or prediction algorithm. The accuracy provided by WRF model is higher than RF model which highlights the importance of selecting the feature for classification algorithm.  

scholarly journals Unifying Themes in Microbial Associations with Animal and Plant Hosts Described Using the Gene Ontology

2010 ◽  
Vol 74 (4) ◽  
pp. 479-503 ◽  
Author(s):  
Trudy Torto-Alalibo ◽  
Candace W. Collmer ◽  
Michelle Gwinn-Giglio ◽  
Magdalen Lindeberg ◽  
Shaowu Meng ◽  
...  
Keyword(s):  
Gene Ontology ◽  
Effector Proteins ◽  
Biological Processes ◽  
Gene Products ◽  
Host Defenses ◽  
Host Interactions ◽  
Microbial Associations ◽  
Bioinformatic Approaches ◽  
Go Terms ◽  
Diverse Plant

SUMMARY Microbes form intimate relationships with hosts (symbioses) that range from mutualism to parasitism. Common microbial mechanisms involved in a successful host association include adhesion, entry of the microbe or its effector proteins into the host cell, mitigation of host defenses, and nutrient acquisition. Genes associated with these microbial mechanisms are known for a broad range of symbioses, revealing both divergent and convergent strategies. Effective comparisons among these symbioses, however, are hampered by inconsistent descriptive terms in the literature for functionally similar genes. Bioinformatic approaches that use homology-based tools are limited to identifying functionally similar genes based on similarities in their sequences. An effective solution to these limitations is provided by the Gene Ontology (GO), which provides a standardized language to describe gene products from all organisms. The GO comprises three ontologies that enable one to describe the molecular function(s) of gene products, the biological processes to which they contribute, and their cellular locations. Beginning in 2004, the Plant-Associated Microbe Gene Ontology (PAMGO) interest group collaborated with the GO consortium to extend the GO to accommodate terms for describing gene products associated with microbe-host interactions. Currently, over 900 terms that describe biological processes common to diverse plant- and animal-associated microbes are incorporated into the GO database. Here we review some unifying themes common to diverse host-microbe associations and illustrate how the new GO terms facilitate a standardized description of the gene products involved. We also highlight areas where new terms need to be developed, an ongoing process that should involve the whole community.

scholarly journals Gene Ontology and KEGG Enrichment Analyses of Genes Related to Age-Related Macular Degeneration

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Jian Zhang ◽  
ZhiHao Xing ◽  
Mingming Ma ◽  
Ning Wang ◽  
Yu-Dong Cai ◽  
...  
Keyword(s):  
Gene Ontology ◽  
Feature Selection ◽  
Macular Degeneration ◽  
Classification System ◽  
Underlying Disease ◽  
Age Related Macular Degeneration ◽  
Disease Genes ◽  
Kegg Pathways ◽  
Age Related ◽  
Go Terms

Identifying disease genes is one of the most important topics in biomedicine and may facilitate studies on the mechanisms underlying disease. Age-related macular degeneration (AMD) is a serious eye disease; it typically affects older adults and results in a loss of vision due to retina damage. In this study, we attempt to develop an effective method for distinguishing AMD-related genes. Gene ontology and KEGG enrichment analyses of known AMD-related genes were performed, and a classification system was established. In detail, each gene was encoded into a vector by extracting enrichment scores of the gene set, including it and its direct neighbors in STRING, and gene ontology terms or KEGG pathways. Then certain feature-selection methods, including minimum redundancy maximum relevance and incremental feature selection, were adopted to extract key features for the classification system. As a result, 720 GO terms and 11 KEGG pathways were deemed the most important factors for predicting AMD-related genes.

scholarly journals Transcriptome Analysis of Porcine PBMCs Reveals the Immune Cascade Response and Gene Ontology Terms Related to Cell Death and Fibrosis in the Progression of Liver Failure

2018 ◽  
Vol 2018 ◽  
pp. 1-8
Author(s):  
YiMin Zhang ◽  
Li Shao ◽  
Ning Zhou ◽  
JianZhou Li ◽  
Yu Chen ◽  
...  
Keyword(s):  
Cell Death ◽  
Gene Ontology ◽  
Liver Failure ◽  
Transcriptome Analysis ◽  
Receptor Interaction ◽  
Failure Group ◽  
Kegg Pathways ◽  
Gene Sets ◽  
Baseline Group ◽  
Go Terms

Background. The key gene sets involved in the progression of acute liver failure (ALF), which has a high mortality rate, remain unclear. This study aims to gain a deeper understanding of the transcriptional response of peripheral blood mononuclear cells (PBMCs) following ALF. Methods. ALF was induced by D-galactosamine (D-gal) in a porcine model. PBMCs were separated at time zero (baseline group), 36 h (failure group), and 60 h (dying group) after D-gal injection. Transcriptional profiling was performed using RNA sequencing and analysed using DAVID bioinformatics resources. Results. Compared with the baseline group, 816 and 1,845 differentially expressed genes (DEGs) were identified in the failure and dying groups, respectively. A total of five and two gene ontology (GO) term clusters were enriched in 107 GO terms in the failure group and 154 GO terms in the dying group. These GO clusters were primarily immune-related, including genes regulating the inflammasome complex and toll-like receptor signalling pathways. Specifically, GO terms related to cell death, including apoptosis, pyroptosis, and autophagy, and those related to fibrosis, coagulation dysfunction, and hepatic encephalopathy were enriched. Seven Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways, cytokine-cytokine receptor interaction, hematopoietic cell lineage, lysosome, rheumatoid arthritis, malaria, and phagosome and pertussis pathways were mapped for DEGs in the failure group. All of these seven KEGG pathways were involved in the 19 KEGG pathways mapped in the dying group. Conclusion. We found that the dramatic PBMC transcriptome changes triggered by ALF progression was predominantly related to immune responses. The enriched GO terms related to cell death, fibrosis, and so on, as indicated by PBMC transcriptome analysis, seem to be useful in elucidating potential key gene sets in the progression of ALF. A better understanding of these gene sets might be of preventive or therapeutic interest.

Performance Analysis of Classification Techniques With Feature Selection Method for Prediction of Chronic Kidney Disease

2021 ◽  
pp. 220-244
Author(s):  
Noopur Goel
Keyword(s):  
Chronic Kidney Disease ◽  
Feature Selection ◽  
Kidney Disease ◽  
Feature Selection Method ◽  
Selection Method ◽  
Target Feature ◽  
Feature Importance ◽  
The World ◽  
Independent Features

Chronic kidney disease has become a very prevalent problem worldwide and almost 10% of the population is suffering and millions of people are dying every year because of chronic kidney disease. Numerous machine learning and data mining techniques are applied by many researchers around the world to diagnose the presence of chronic kidney disease, so that the patients of chronic kidney disease may get benefited in terms of getting proper healthcare follow-up. In this chapter, Experiment 1 is conducted by implementing different five different classifiers on the original chronic kidney disease dataset. In Experiment 2, feature selection using feature importance method is used to reduce the chronic kidney disease dataset. A subset of 15 independent features and one target feature ‘class' is obtained. Again, the same steps are implemented but on the newly obtained reduced dataset. The results of both the Experiments 1 and 2 are compared, and it is observed that the accuracy of classifiers with feature selection is far better than the accuracy of classifiers without feature selection.

scholarly journals Prediction and Analysis of Retinoblastoma Related Genes through Gene Ontology and KEGG

2013 ◽  
Vol 2013 ◽  
pp. 1-8 ◽  
Author(s):  
Zhen Li ◽  
Bi-Qing Li ◽  
Min Jiang ◽  
Lei Chen ◽  
Jian Zhang ◽  
...  
Keyword(s):  
Gene Ontology ◽  
Disease Free Survival ◽  
Computational Method ◽  
The Other ◽  
Free Survival ◽  
Kegg Pathways ◽  
Incremental Feature Selection ◽  
Optimal Feature ◽  
Go Terms ◽  
Disease Free

One of the most important and challenging problems in biomedicine is how to predict the cancer related genes. Retinoblastoma (RB) is the most common primary intraocular malignancy usually occurring in childhood. Early detection of RB could reduce the morbidity and promote the probability of disease-free survival. Therefore, it is of great importance to identify RB genes. In this study, we developed a computational method to predict RB related genes based on Dagging, with the maximum relevance minimum redundancy (mRMR) method followed by incremental feature selection (IFS). 119 RB genes were compiled from two previous RB related studies, while 5,500 non-RB genes were randomly selected from Ensemble genes. Ten datasets were constructed based on all these RB and non-RB genes. Each gene was encoded with a 13,126-dimensional vector including 12,887 Gene Ontology enrichment scores and 239 KEGG enrichment scores. Finally, an optimal feature set including 1061 GO terms and 8 KEGG pathways was obtained. Analysis showed that these features were closely related to RB. It is anticipated that the method can be applied to predict the other cancer related genes as well.

scholarly journals Analysis of Protein–Protein Functional Associations by Using Gene Ontology and KEGG Pathway

2019 ◽  
Vol 2019 ◽  
pp. 1-10 ◽  
Author(s):  
Fei Yuan ◽  
Xiaoyong Pan ◽  
Lei Chen ◽  
Yu-Hang Zhang ◽  
Tao Huang ◽  
...  
Keyword(s):  
Gene Ontology ◽  
Kegg Pathway ◽  
Final Analysis ◽  
Biological Pathways ◽  
Extended Version ◽  
Multiple Sources ◽  
Protein Protein Interaction ◽  
Kegg Pathways ◽  
The Difference ◽  
Go Terms

Protein–protein interaction (PPI) plays an extremely remarkable role in the growth, reproduction, and metabolism of all lives. A thorough investigation of PPI can uncover the mechanism of how proteins express their functions. In this study, we used gene ontology (GO) terms and biological pathways to study an extended version of PPI (protein–protein functional associations) and subsequently identify some essential GO terms and pathways that can indicate the difference between two proteins with and without functional associations. The protein–protein functional associations validated by experiments were retrieved from STRING, a well-known database on collected associations between proteins from multiple sources, and they were termed as positive samples. The negative samples were constructed by randomly pairing two proteins. Each sample was represented by several features based on GO and KEGG pathway information of two proteins. Then, the mutual information was adopted to evaluate the importance of all features and some important ones could be accessed, from which a number of essential GO terms or KEGG pathways were identified. The final analysis of some important GO terms and one KEGG pathway can partly uncover the difference between proteins with and without functional associations.

Awareness about Covid 19 Pandemic and its Social Impacts on Normal Human Lifestyle Among Chennai Population

2020 ◽  
Vol 11 (SPL1) ◽  
pp. 659-666
Author(s):  
Anu Iswarya Jaisankar ◽  
Raghu Nandhakumar ◽  
Ezhilarasan D
Keyword(s):  
Psychological Stress ◽  
Economic Burden ◽  
Economic Impacts ◽  
Functional Limitations ◽  
Social Impacts ◽  
Biological Processes ◽  
The People ◽  
The World ◽  
Normal Human

Covid 19 pandemic is a terrible ongoing pandemic that has spread worldwide. Covid 19 Pandemic has infected more than 188 countries and territories across the globe. The basic biological processes and functional limitations that govern the development and survival of the particular behaviors of the virus continue to be elucidated. On that note, Prevention is the only cure. The World is facing a great economic turmoil. People suffer from Psychological stress and Economic burden combined. Here assessing the Psychological, Physical, Social, Financial and Economic impacts of the Pandemic on the people becomes really very important in analysing the mindset of the people and in evaluating the significance of implemented changes and in implementing new changes. The current study aims at analysing the various impacts of Covid 19 on the people residing at the Greater Chennai corporation circle.

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