Gene set enrichment and topological analyses based on interaction networks in pediatric acute lymphoblastic leukemia

Sui, Shuxiang; Wang, Xin; Zheng, Hua; Guo, Hua; Chen, Tong; Ji, Dongmei

doi:10.3892/ol.2015.3761

Cited by 6 publications

(6 citation statements)

References 46 publications

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“…We implemented KEGG pathway enrichment by using DAVID [ 20 ], and found 24 pathways were significantly enriched ( p -value < 0.01) (Table 1 , Figure 2A ). The hematopoietic cell lineage (hsa04640) pathway was the most significantly enriched pathway, and it is significantly associated with pediatric acute lymphoblastic leukemia [ 21 ]. The primary immunodeficiency (hsa05340) pathway was also a significantly enriched pathway, which might be associated with acute myeloid leukemia (AML) development [ 22 ].…”

Section: Resultsmentioning

confidence: 99%

Global view of a drug-sensitivity gene network

et al. 2017

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An important challenge in drug development is to gain insight into the mechanism of drug sensitivity. Looking for insights into the global relationships between drugs and their sensitivity genes would be expected to reveal mechanism of drug sensitivity. Here we constructed a drug-sensitivity gene network (DSGN) based on the relationships between drugs and their sensitivity genes, using drug screened genomic data from the NCI-60 cell line panel, including 181 drugs and 1057 sensitivity genes, and 1646 associations between them. Through network analysis, we found that two drugs that share the same sensitivity genes tend to share the same Anatomical Therapeutic Chemical classification and side effects. We then found that the sensitivity genes of same drugs tend to cluster together in the human interactome and participate in the same biological function modules (pathways). Finally, we noticed that the sensitivity genes and target genes of the same drug have a significant dense distance in the human interactome network and they were functionally related. For example, target genes such as epidermal growth factor receptor gene can activate downstream sensitivity genes of the same drug in the PI3K/Akt pathway. Thus, the DSGN would provide great insights into the mechanism of drug sensitivity.

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Section: Resultsmentioning

confidence: 99%

Global view of a drug-sensitivity gene network

et al. 2017

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“…The non-invasive detection of new significant genes for the replication of HCV and its outcomes, such as Hepatocellular carcinoma using machine learning techniques, has been recently addressed in a diverse array of studies. Studying the early stages of HCV infection and detecting the host genes involved in the HCV life cycle was discussed in [8]. They utilized the same dataset applied in this research.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Simple statistical approaches for predicting informative genes from microarrays such as T-test and F-test can indicate the variance in gene expression in different data sets [8]. However, univariate and multivariate machine learning techniques are more advanced methods [9].…”

Section: Introductionmentioning

confidence: 99%

Classical and Deep Learning Paradigms for Detection and Validation of Key Genes of Risky Outcomes of HCV

Samee

2020

Algorithms

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Hepatitis C virus (HCV) is one of the most dangerous viruses worldwide. It is the foremost cause of the hepatic cirrhosis, and hepatocellular carcinoma, HCC. Detecting new key genes that play a role in the growth of HCC in HCV patients using machine learning techniques paves the way for producing accurate antivirals. In this work, there are two phases: detecting the up/downregulated genes using classical univariate and multivariate feature selection methods, and validating the retrieved list of genes using Insilico classifiers. However, the classification algorithms in the medical domain frequently suffer from a deficiency of training cases. Therefore, a deep neural network approach is proposed here to validate the significance of the retrieved genes in classifying the HCV-infected samples from the disinfected ones. The validation model is based on the artificial generation of new examples from the retrieved genes’ expressions using sparse autoencoders. Subsequently, the generated genes’ expressions data are used to train conventional classifiers. Our results in the first phase yielded a better retrieval of significant genes using Principal Component Analysis (PCA), a multivariate approach. The retrieved list of genes using PCA had a higher number of HCC biomarkers compared to the ones retrieved from the univariate methods. In the second phase, the classification accuracy can reveal the relevance of the extracted key genes in classifying the HCV-infected and disinfected samples.

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“…Much of the recent work on effects biomarkers has focused on the classification of genes into ontology groups that can then be used to predict a biological effect (27–30). These efforts can be broken down into two different approaches.…”

Section: Introductionmentioning

confidence: 99%

“…There has been significant research on the use of gene expression data to identify people with diseases (disease biomarkers), to monitor exposure to chemicals (exposure biomarkers), and to predict effects from exposure to chemical agents (effects biomarkers). Much of the recent work on effects biomarkers has focused on the classification of genes into ontology groups that can then be used to predict a biological effect ( 27 – 30 ). These efforts can be broken down into two different approaches.…”

Section: Introductionmentioning

confidence: 99%

The Use of Signal-Transduction and Metabolic Pathways to Predict Human Disease Targets from Electric and Magnetic Fields Using in vitro Data in Human Cell Lines

Parham

Portier²,

Chang

et al. 2016

Front. Public Health

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Using in vitro data in human cell lines, several research groups have investigated changes in gene expression in cellular systems following exposure to extremely low frequency (ELF) and radiofrequency (RF) electromagnetic fields (EMF). For ELF EMF, we obtained five studies with complete microarray data and three studies with only lists of significantly altered genes. Likewise, for RF EMF, we obtained 13 complete microarray datasets and 5 limited datasets. Plausible linkages between exposure to ELF and RF EMF and human diseases were identified using a three-step process: (a) linking genes associated with classes of human diseases to molecular pathways, (b) linking pathways to ELF and RF EMF microarray data, and (c) identifying associations between human disease and EMF exposures where the pathways are significantly similar. A total of 60 pathways were associated with human diseases, mostly focused on basic cellular functions like JAK–STAT signaling or metabolic functions like xenobiotic metabolism by cytochrome P450 enzymes. ELF EMF datasets were sporadically linked to human diseases, but no clear pattern emerged. Individual datasets showed some linkage to cancer, chemical dependency, metabolic disorders, and neurological disorders. RF EMF datasets were not strongly linked to any disorders but strongly linked to changes in several pathways. Based on these analyses, the most promising area for further research would be to focus on EMF and neurological function and disorders.

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Gene set enrichment and topological analyses based on interaction networks in pediatric acute lymphoblastic leukemia

Cited by 6 publications

References 46 publications

Global view of a drug-sensitivity gene network

Global view of a drug-sensitivity gene network

Classical and Deep Learning Paradigms for Detection and Validation of Key Genes of Risky Outcomes of HCV

The Use of Signal-Transduction and Metabolic Pathways to Predict Human Disease Targets from Electric and Magnetic Fields Using in vitro Data in Human Cell Lines

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