An efficiency analysis of high-order combinations of gene–gene interactions using multifactor-dimensionality reduction

Yang, Cheng‐Hong; Lin, Yu‐Da; Yang, Cheng‐San; Chuang, Li‐Yeh

doi:10.1186/s12864-015-1717-8

Cited by 23 publications

(4 citation statements)

References 34 publications

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“…For large data sets used in a GWAS, FSMOMDR has an approximate computation time of 29.14 h for 23 chromosomes. We recommend choosing from a large existing suite of computational methods, including parallel operation [44], graphics processing units-based MDR [45], the greedy search strategy [46], and differential evolution-based MDR [18], to improve FSMOMDR runtime.…”

Section: Discussionmentioning

confidence: 99%

Fuzzy Logic System Application for Detecting SNP-SNP Interaction

2020

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The identification of interactions between single-nucleotide polymorphisms (SNP-SNP interactions) is crucial for determining human genetic disease susceptibility. With rapid technological advancements, multiobjective multifactor dimensionality reduction (MOMDR) measurements have achieved high detection success rates. However, the classification of high-or low-risk groups is central to MOMDR and has yet to be extensively studied. To address limitations in binary classification, we propose an improved fuzzy sigmoid (FS) approach that uses membership degrees in MOMDR, thus denoting it as FSMOMDR. For determining the interval of membership, our improved FS approach assesses the distance between the i th multifactor class and outcome (cases and controls). Thus, the improved FS approach enables MOMDR algorithms to determine the membership degrees of high-and low-risk groups in each multifactor class because the two-element set is extended to a specified membership interval. Moreover, the improved FS approach can handle uncertain information, which thus enables the effective detection of the m-locus combinations with similar distributions. FSMOMDR measurements can also distinguish similar frequencies among genotype combinations, thus enabling the detection of more significant SNP-SNP interactions. On the basis of the classification accuracy rate of MOMDR and results obtained from the analysis of several test data sets, we determined FSMOMDR to be superior to other MDR-based methods with respect to detection success rate. The results indicate that binary and fuzzy classifications involving MOMDR can provide insight into uncertainty in risk classification. Thus, FSMOMDR could successfully detect SNP-SNP interactions in coronary artery disease in a large data set obtained from the Wellcome Trust Case Control Consortium. We could successfully reduce uncertain information in MDR and thus suggest that membership based on the improved sigmoid function can be used to identify SNP-SNP interactions as well as obtain content knowledge.

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Section: Discussionmentioning

confidence: 99%

Fuzzy Logic System Application for Detecting SNP-SNP Interaction

2020

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“…The MDR analysis was carried out by onine MDR software version 2.0 [ 61 ] producing several genotype interaction models. Amongst them, the genotype combination having the highest testing accuracy and the cross-validation consistency (CVC) is taken as the best interaction model [ 62 ]. The combined effect of the variables was calculated using LR analysis and a p -value less than 0.05 was considered to be statistically significant.…”

Section: Discussionmentioning

confidence: 99%

A Multiple Interaction Analysis Reveals ADRB3 as a Potential Candidate for Gallbladder Cancer Predisposition via a Complex Interaction with Other Candidate Gene Variations

Rai

Kim

Misra

et al. 2015

IJMS

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Gallbladder cancer is the most common and a highly aggressive biliary tract malignancy with a dismal outcome. The pathogenesis of the disease is multifactorial, comprising the combined effect of multiple genetic variations of mild consequence along with numerous dietary and environmental risk factors. Previously, we demonstrated the association of several candidate gene variations with GBC risk. In this study, we aimed to identify the combination of gene variants and their possible interactions contributing towards genetic susceptibility of GBC. Here, we performed Multifactor-Dimensionality Reduction (MDR) and Classification and Regression Tree Analysis (CRT) to investigate the gene–gene interactions and the combined effect of 14 SNPs in nine genes (DR4 (rs20576, rs6557634); FAS (rs2234767); FASL (rs763110); DCC (rs2229080, rs4078288, rs7504990, rs714); PSCA (rs2294008, rs2978974); ADRA2A (rs1801253); ADRB1 (rs1800544); ADRB3 (rs4994); CYP17 (rs2486758)) involved in various signaling pathways. Genotyping was accomplished by PCR-RFLP or Taqman allelic discrimination assays. SPSS software version 16.0 and MDR software version 2.0 were used for all the statistical analysis. Single locus investigation demonstrated significant association of DR4 (rs20576, rs6557634), DCC (rs714, rs2229080, rs4078288) and ADRB3 (rs4994) polymorphisms with GBC risk. MDR analysis revealed ADRB3 (rs4994) to be crucial candidate in GBC susceptibility that may act either alone (p < 0.0001, CVC = 10/10) or in combination with DCC (rs714 and rs2229080, p < 0.0001, CVC = 9/10). Our CRT results are in agreement with the above findings. Further, in-silico results of studied SNPs advocated their role in splicing, transcriptional and/or protein coding regulation. Overall, our result suggested complex interactions amongst the studied SNPs and ADRB3 rs4994 as candidate influencing GBC susceptibility.

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“…Dimensionality reduction techniques, such as principal component analysis (PCA) or t-SNE (t-distributed stochastic neighbor embedding), are utilized to reduce the dimensionality of high-dimensional datasets while preserving important information [33][34][35]. By visualizing the reduced dataset, researchers can identify patterns or clusters that may not be immediately apparent in the original data.…”

Section: Machine Learningmentioning

confidence: 99%

From Machine Learning to Patient Outcomes: A Comprehensive Review of AI in Pancreatic Cancer

Tripathi,

Tabari,

Mansur

et al. 2024

Diagnostics

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Pancreatic cancer is a highly aggressive and difficult-to-detect cancer with a poor prognosis. Late diagnosis is common due to a lack of early symptoms, specific markers, and the challenging location of the pancreas. Imaging technologies have improved diagnosis, but there is still room for improvement in standardizing guidelines. Biopsies and histopathological analysis are challenging due to tumor heterogeneity. Artificial Intelligence (AI) revolutionizes healthcare by improving diagnosis, treatment, and patient care. AI algorithms can analyze medical images with precision, aiding in early disease detection. AI also plays a role in personalized medicine by analyzing patient data to tailor treatment plans. It streamlines administrative tasks, such as medical coding and documentation, and provides patient assistance through AI chatbots. However, challenges include data privacy, security, and ethical considerations. This review article focuses on the potential of AI in transforming pancreatic cancer care, offering improved diagnostics, personalized treatments, and operational efficiency, leading to better patient outcomes.

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An efficiency analysis of high-order combinations of gene–gene interactions using multifactor-dimensionality reduction

Cited by 23 publications

References 34 publications

Fuzzy Logic System Application for Detecting SNP-SNP Interaction

Fuzzy Logic System Application for Detecting SNP-SNP Interaction

A Multiple Interaction Analysis Reveals ADRB3 as a Potential Candidate for Gallbladder Cancer Predisposition via a Complex Interaction with Other Candidate Gene Variations

From Machine Learning to Patient Outcomes: A Comprehensive Review of AI in Pancreatic Cancer

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