GENEvaRX: A novel AI-driven method and web tool can identify critical genes and effective drugs for Lichen Planus

Turki, Turki; Taguchi, Y-h.

doi:10.1016/j.engappai.2023.106607

Cited by 2 publications

(5 citation statements)

References 74 publications

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“…In Figure 8, we aim to get computational insights pertaining to studied learning-based models applied in the Results section. Figure 8(a) demonstrated that our method esvm leads to non-zero weights while lasso in Figure 8(b) leading to a sparser representation and thereby many zero coefficients, attributed to the L1 penalty as in [28]. In Figure 8(c), it can be seen that lasso is ∼ 6.14 × faster than esvm.…”

Section: Dataset1mentioning

confidence: 87%

“…All drug responses y i (for i = 1.. m ) encoded as a 1 × m column vector. To identify p important genes out of the n genes in which p ≪ 𝑛, we used to find arguments (i.e., w = [ w 1 … w n ] and b ∊ R) that minimize the objective function in Equation 1 subject to the linear constraints as in [28, 29]. After solving the optimization problem in Equation 1, weights in w correspond to the importance of genes in which higher weights indicate the more important these genes are.…”

Section: Methodsmentioning

confidence: 99%

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maGENEgerZ: An Efficient AI-Based Framework Can Extract More Expressed Genes and Biological Insights Underlying Breast Cancer Drug Response Mechanism

Turki,

Taguchi

2023

Preprint

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Understanding breast cancer drug response mechanism can play a crucial role in improving the treatment outcomes and survival rates. Existing bioinformatics-based approaches are far from perfect and do not adopt computational methods based on advanced artificial intelligence concepts. Therefore, we introduce a novel computational framework based on an efficient version of support vector machines (esvm) working as follows. First, we downloaded and processed three gene expression datasets related to breast cancer responding and non-responding to the treatments from the gene expression omnibus (GEO) according to the following GEO accession numbers: GSE130787, GSE140494, and GSE196093. Our method esvm is formulated as a constrained optimization problem in the dual form as a function of λ. We recover the importance of each gene as a function of λ, y, and x. Then, we select p genes out of n, provided as input to enrichment analysis tools, Enrichr and Metascape. Compared to existing baseline methods, results demonstrate superiority and efficiency of esvm achieving high performance results and having more expressed genes in (1) well-established breast cancer cell lines including MD-MB231, MCF7, and HS578T; and (2) breast tissues. Moreover, esvm is able to identify (1) various drugs including clinically approved ones (e.g., tamoxifen and erlotinib); (2) seventy-four unique genes (including tumor suppression genes such as TP53 and BRCA1); and (3) thirty-six unique TFs (including SP1 and RELA). These results have been reported to be linked to breast cancer drug response mechanism, progression, and metastasizing. Our method is available publicly in the maGENEgerZ web server at https://aibio.shinyapps.io/maGENEgerZ/.

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Section: Dataset1mentioning

confidence: 87%

Section: Methodsmentioning

confidence: 99%

maGENEgerZ: An Efficient AI-Based Framework Can Extract More Expressed Genes and Biological Insights Underlying Breast Cancer Drug Response Mechanism

Turki,

Taguchi

2023

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…In Figure 8, we aim to obtain computational insights pertaining to learning-based models studied and applied in the Results Section. Figure 8a demonstrates that our method esvm leads to non-zero weights, while lasso in Figure 8b leads to a sparser representation and thereby many zero coefficients, attributed to the L1 penalty as in [28]. In Figure 8c, it can be seen that the lasso is ~6.14 × faster than the esvm.…”

Section: Dataset3mentioning

confidence: 94%

“…. .w n ] and b ∈ R) that minimize the objective function in Equation (1) subject to the linear constraints as in [28,29]. After solving the optimization problem in Equation ( 1), weights in w correspond to the importance of genes, with higher weights indicating how important these genes are.…”

Section: Computational Frameworkmentioning

confidence: 99%

“…All drug responses yi (where i = 1, ..., m) are encoded as a 1 × m column vector. To identify p important genes out of the n genes in which p ≪ 𝑛, we used to find arguments (i.e., w = [w1…wn] and b ∊ R) that minimize the objective function in Equation ( 1) subject to the linear constraints as in [28,29]. After solving the optimization problem in Equation ( 1), weights in w correspond to the importance of genes, with higher weights indicating how important these genes are.…”

Section: Computational Frameworkmentioning

confidence: 99%

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maGENEgerZ: An Efficient Artificial Intelligence-Based Framework Can Extract More Expressed Genes and Biological Insights Underlying Breast Cancer Drug Response Mechanism

Turki,

Taguchi

2024

Mathematics

Self Cite

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Understanding breast cancer drug response mechanisms can play a crucial role in improving treatment outcomes and survival rates. Existing bioinformatics-based approaches are far from perfect and do not adopt computational methods based on advanced artificial intelligence concepts. Therefore, we introduce a novel computational framework based on an efficient support vector machine (esvm) working as follows: First, we downloaded and processed three gene expression datasets related to breast cancer responding and non-responding to treatments from the gene expression omnibus (GEO) according to the following GEO accession numbers: GSE130787, GSE140494, and GSE196093. Our method esvm is formulated as a constrained optimization problem in its dual form as a function of λ. We recover the importance of each gene as a function of λ, y, and x. Then, we select p genes out of n, which are provided as input to enrichment analysis tools, Enrichr and Metascape. Compared to existing baseline methods, including deep learning, results demonstrate the superiority and efficiency of esvm, achieving high-performance results and having more expressed genes in well-established breast cancer cell lines, including MD-MB231, MCF7, and HS578T. Moreover, esvm is able to identify (1) various drugs, including clinically approved ones (e.g., tamoxifen and erlotinib); (2) seventy-four unique genes (including tumor suppression genes such as TP53 and BRCA1); and (3) thirty-six unique TFs (including SP1 and RELA). These results have been reported to be linked to breast cancer drug response mechanisms, progression, and metastasizing. Our method is available publicly on the maGENEgerZ web server.

show abstract

GENEvaRX: A novel AI-driven method and web tool can identify critical genes and effective drugs for Lichen Planus

Cited by 2 publications

References 74 publications

maGENEgerZ: An Efficient AI-Based Framework Can Extract More Expressed Genes and Biological Insights Underlying Breast Cancer Drug Response Mechanism

maGENEgerZ: An Efficient AI-Based Framework Can Extract More Expressed Genes and Biological Insights Underlying Breast Cancer Drug Response Mechanism

maGENEgerZ: An Efficient Artificial Intelligence-Based Framework Can Extract More Expressed Genes and Biological Insights Underlying Breast Cancer Drug Response Mechanism

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