Classifying Ten Types of Major Cancers Based on Reverse Phase Protein Array Profiles

Zhang, Peiwei; Lei, Chen; Huang, Tao; Zhang, Ning; Kong, Xiangyin; Cai, Yu-Dong

doi:10.1371/journal.pone.0123147

Cited by 49 publications

(43 citation statements)

References 70 publications

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“…Based on the top 2,448 mRMR SNPs, we constructed 2,448 classifiers and applied an Incremental Feature Selection (IFS) method [ 43 – 47 ] to identify the optimal SNP set. Candidate SNP set S i = { f 1 , f 2 , …, f i }(1 ≤ i ≤ 2, 448) included the top i SNPs.…”

Section: Methodsmentioning

confidence: 99%

Genetic differences among ethnic groups

2015

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BackgroundMany differences between different ethnic groups have been observed, such as skin color, eye color, height, susceptibility to some diseases, and response to certain drugs. However, the genetic bases of such differences have been under-investigated. Since the HapMap project, large-scale genotype data from Caucasian, African and Asian population samples have been available. The project found that these populations were located in different areas of the PCA (Principal Component Analysis) plot. However, as an unsupervised method, PCA does not measure the differences in each single nucleotide polymorphism (SNP) among populations.ResultsWe applied an advanced mutual information-based feature selection method to detect associations between SNP status and ethnic groups using the latest HapMap Phase 3 release version 3, which included more sub-populations. A total of 299 SNPs were identified, and they can accurately predicted the ethnicity of all HapMap populations. The 10-fold cross validation accuracy of the SMO (sequential minimal optimization) model on training dataset was 0.901, and the accuracy on independent test dataset was 0.895.ConclusionsIn-depth functional analysis of these SNPs and their nearby genes revealed the genetic bases of skin and eye color differences among populations.Electronic supplementary materialThe online version of this article (doi:10.1186/s12864-015-2328-0) contains supplementary material, which is available to authorized users.

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Section: Methodsmentioning

confidence: 99%

Genetic differences among ethnic groups

2015

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“…The model based on this algorithm can be constructed on a dataset with small size, whereas it can provide good generalization performances. Thus, this algorithm has been widely used in bioinformatics [ 46 – 49 ]. In the algorithm, samples in the dataset are mapped into a higher-dimensional space using the kernel trick, in which the set of positive and negative samples can easily be separated by a hyper-plane with maximum margin.…”

Section: Methodsmentioning

confidence: 99%

Prediction and analysis of essential genes using the enrichments of gene ontology and KEGG pathways

et al. 2017

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Identifying essential genes in a given organism is important for research on their fundamental roles in organism survival. Furthermore, if possible, uncovering the links between core functions or pathways with these essential genes will further help us obtain deep insight into the key roles of these genes. In this study, we investigated the essential and non-essential genes reported in a previous study and extracted gene ontology (GO) terms and biological pathways that are important for the determination of essential genes. Through the enrichment theory of GO and KEGG pathways, we encoded each essential/non-essential gene into a vector in which each component represented the relationship between the gene and one GO term or KEGG pathway. To analyze these relationships, the maximum relevance minimum redundancy (mRMR) was adopted. Then, the incremental feature selection (IFS) and support vector machine (SVM) were employed to extract important GO terms and KEGG pathways. A prediction model was built simultaneously using the extracted GO terms and KEGG pathways, which yielded nearly perfect performance, with a Matthews correlation coefficient of 0.951, for distinguishing essential and non-essential genes. To fully investigate the key factors influencing the fundamental roles of essential genes, the 21 most important GO terms and three KEGG pathways were analyzed in detail. In addition, several genes was provided in this study, which were predicted to be essential genes by our prediction model. We suggest that this study provides more functional and pathway information on the essential genes and provides a new way to investigate related problems.

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“…To extract important GO terms and KEGG pathways, the mRMR selection method [47] was employed. This method is useful for analyzing various features and identifying the most important ones, and it has been widely used by investigators to address several biological problems [48–56]. Two excellent criteria were introduced in the mRMR method: Max-Relevance and Min-Redundancy, in which the former criterion guarantees that features with high relevance with targets can receive high ranks, and the latter one guarantees that a feature with lowest redundancies to already-selected features has priority to be selected.…”

Section: Methodsmentioning

confidence: 99%

The Use of Gene Ontology Term and KEGG Pathway Enrichment for Analysis of Drug Half-Life

et al. 2016

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A drug’s biological half-life is defined as the time required for the human body to metabolize or eliminate 50% of the initial drug dosage. Correctly measuring the half-life of a given drug is helpful for the safe and accurate usage of the drug. In this study, we investigated which gene ontology (GO) terms and biological pathways were highly related to the determination of drug half-life. The investigated drugs, with known half-lives, were analyzed based on their enrichment scores for associated GO terms and KEGG pathways. These scores indicate which GO terms or KEGG pathways the drug targets. The feature selection method, minimum redundancy maximum relevance, was used to analyze these GO terms and KEGG pathways and to identify important GO terms and pathways, such as sodium-independent organic anion transmembrane transporter activity (GO:0015347), monoamine transmembrane transporter activity (GO:0008504), negative regulation of synaptic transmission (GO:0050805), neuroactive ligand-receptor interaction (hsa04080), serotonergic synapse (hsa04726), and linoleic acid metabolism (hsa00591), among others. This analysis confirmed our results and may show evidence for a new method in studying drug half-lives and building effective computational methods for the prediction of drug half-lives.

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Classifying Ten Types of Major Cancers Based on Reverse Phase Protein Array Profiles

Cited by 49 publications

References 70 publications

Genetic differences among ethnic groups

Genetic differences among ethnic groups

Prediction and analysis of essential genes using the enrichments of gene ontology and KEGG pathways

The Use of Gene Ontology Term and KEGG Pathway Enrichment for Analysis of Drug Half-Life

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