Gene-set activity toolbox (GAT): A platform for microarray-based cancer diagnosis using an integrative gene-set analysis approach

Engchuan, Worrawat; Meechai, Asawin; Tongsima, Sissades; Doungpan, Narumol; Chan, Jonathan H.

doi:10.1142/s0219720016500153

Cited by 5 publications

(2 citation statements)

References 63 publications

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“…By doing this, the evaluation was focused on inspecting the presence of the identified gene and gene-set resulting from the analysis without considering the expression value of each gene. The Jaccard-like agreement implemented in Engchuan et al [24] was used for the evaluation.…”

Section: Methodsmentioning

confidence: 99%

GSNFS: Gene subnetwork biomarker identification of lung cancer expression data

et al. 2016

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BackgroundGene expression has been used to identify disease gene biomarkers, but there are ongoing challenges. Single gene or gene-set biomarkers are inadequate to provide sufficient understanding of complex disease mechanisms and the relationship among those genes. Network-based methods have thus been considered for inferring the interaction within a group of genes to further study the disease mechanism. Recently, the Gene-Network-based Feature Set (GNFS), which is capable of handling case-control and multiclass expression for gene biomarker identification, has been proposed, partly taking into account of network topology. However, its performance relies on a greedy search for building subnetworks and thus requires further improvement. In this work, we establish a new approach named Gene Sub-Network-based Feature Selection (GSNFS) by implementing the GNFS framework with two proposed searching and scoring algorithms, namely gene-set-based (GS) search and parent-node-based (PN) search, to identify subnetworks. An additional dataset is used to validate the results.MethodsThe two proposed searching algorithms of the GSNFS method for subnetwork expansion are concerned with the degree of connectivity and the scoring scheme for building subnetworks and their topology. For each iteration of expansion, the neighbour genes of a current subnetwork, whose expression data improved the overall subnetwork score, is recruited. While the GS search calculated the subnetwork score using an activity score of a current subnetwork and the gene expression values of its neighbours, the PN search uses the expression value of the corresponding parent of each neighbour gene. Four lung cancer expression datasets were used for subnetwork identification. In addition, using pathway data and protein-protein interaction as network data in order to consider the interaction among significant genes were discussed. Classification was performed to compare the performance of the identified gene subnetworks with three subnetwork identification algorithms.ResultsThe two searching algorithms resulted in better classification and gene/gene-set agreement compared to the original greedy search of the GNFS method. The identified lung cancer subnetwork using the proposed searching algorithm resulted in an improvement of the cross-dataset validation and an increase in the consistency of findings between two independent datasets. The homogeneity measurement of the datasets was conducted to assess dataset compatibility in cross-dataset validation. The lung cancer dataset with higher homogeneity showed a better result when using the GS search while the dataset with low homogeneity showed a better result when using the PN search. The 10-fold cross-dataset validation on the independent lung cancer datasets showed higher classification performance of the proposed algorithms when compared with the greedy search in the original GNFS method.ConclusionsThe proposed searching algorithms provide a higher number of genes in the subnetwork expansion step than the greedy al...

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Section: Methodsmentioning

confidence: 99%

GSNFS: Gene subnetwork biomarker identification of lung cancer expression data

et al. 2016

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“…Some studies modify the t-test to deal with multiclass problems [ 10 , 17 , 18 ]. In most of the literature, an analysis of variance (ANOVA) F-test statistic is frequently used to solve multiclass issues [ 19 , 20 , 21 , 22 ]. Besides this, multiclass classification methods can be divided into two types [ 23 ].…”

Section: Introductionmentioning

confidence: 99%

Enhanced Directed Random Walk for the Identification of Breast Cancer Prognostic Markers from Multiclass Expression Data

Nies

Mohamad

Zakaria

et al. 2021

Entropy

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Artificial intelligence in healthcare can potentially identify the probability of contracting a particular disease more accurately. There are five common molecular subtypes of breast cancer: luminal A, luminal B, basal, ERBB2, and normal-like. Previous investigations showed that pathway-based microarray analysis could help in the identification of prognostic markers from gene expressions. For example, directed random walk (DRW) can infer a greater reproducibility power of the pathway activity between two classes of samples with a higher classification accuracy. However, most of the existing methods (including DRW) ignored the characteristics of different cancer subtypes and considered all of the pathways to contribute equally to the analysis. Therefore, an enhanced DRW (eDRW+) is proposed to identify breast cancer prognostic markers from multiclass expression data. An improved weight strategy using one-way ANOVA (F-test) and pathway selection based on the greatest reproducibility power is proposed in eDRW+. The experimental results show that the eDRW+ exceeds other methods in terms of AUC. Besides this, the eDRW+ identifies 294 gene markers and 45 pathway markers from the breast cancer datasets with better AUC. Therefore, the prognostic markers (pathway markers and gene markers) can identify drug targets and look for cancer subtypes with clinically distinct outcomes.

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Predicting Dihydroartemisinin Resistance in Plasmodium falciparum using Pathway Activity Inference

Lawford

Chan

2020

CSBio '20: Proceedings of the Eleventh International Conference on Computational Systems-Biology and Bioinformatics

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Gene-set activity toolbox (GAT): A platform for microarray-based cancer diagnosis using an integrative gene-set analysis approach

Cited by 5 publications

References 63 publications

GSNFS: Gene subnetwork biomarker identification of lung cancer expression data

GSNFS: Gene subnetwork biomarker identification of lung cancer expression data

Enhanced Directed Random Walk for the Identification of Breast Cancer Prognostic Markers from Multiclass Expression Data

Predicting Dihydroartemisinin Resistance in Plasmodium falciparum using Pathway Activity Inference

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