Next-generation sequencing (NGS) genomic data offer valuable high-throughput genomic information for computational applications in medicine. Using genomic data to identify disease-associated genes to estimate cancer mortality risk remains challenging regarding to computational efficiency and risk integration. For determining mortality-related genes, we propose an information fusion system based on a fuzzy system to fuse the numerous deep-learning-based risk scores, consider the significance of features related to time-varying effects and risk stratifications, and interpret the directional relationship and interaction between outcome and predictors. Fuzzy rules were implemented to integrate the considerations mentioned above by merging all the risk score models to achieve advanced risk estimation. The genomic data of head and neck squamous cell carcinoma (HNSCC) were used to evaluate the performance of the proposed computational approach. The results indicated that the proposed computational approach exhibited optimal ability to identify mortality risk-related genes in HNSCC patients. The results also suggest that HNSCC mortality is associated with cancer inflammatory response, the interleukin-17A signaling pathway, stellate cell activation, and the extracellular-regulated protein kinase five signaling pathway, which might offer new therapeutic targets HNSCC through immunologic or antiangiogenic mechanisms. The proposed information fusion system can promote the determination of high-risk genes related to cancer mortality. This study contributes a valid cancer mortality risk estimate that can identify mortality-related genes.

There is substantial evidence that common genes based on single nucleotide polymorphisms (SNPs) are associated with cancer risk. Identifying disease-causing SNPs has, therefore, become an essential area of research. In this study, we used an arithmetic optimization algorithm (AOA) to select the significant genes using interactions between SNPs in breast cancer. We simultaneously analyzed several independent SNPs of genes for different combinations of interactions and their associated genotypes and calculated the variance between the case and control groups. The optimal combination of interactions was identified by determining the most significant difference in co-occurrence between the case and control groups. We also used the odds ratio (OR) to assess the effect of each combination of SNPs. Our research used a simulated breast cancer SNP dataset, which included 19 SNPs in 372 control participants and 398 breast cancer cases. The estimated OR of the best predicted SNP combination for breast cancer varied from 1.77 to 4.97 (confidence interval (CI): 1.22-13.47; p < 0.05) for specific combinations of two to six SNPs compared with their non-SNP combinations. Our AOA method accurately predicted high-risk SNP-SNP interactions for breast cancer. The proposed algorithm can potentially be adapted to study SNP-SNP interactions in various diseases and cancers.

Breast cancer (BRCA) and head and neck cancer (HNSC) represent significant global health challenges, underscoring the critical need for accurate prognosis in these patient populations. Tumor suppressor genes (TSGs) and oncogenes (OCGs) play pivotal roles in cancer progression, yet exhibit low mutation rates in affected individuals. Consequently, distinct omics patterns are necessary for estimating prognosis risk in patients harboring wild-type OCGs and TSGs. This study investigates mRNA expression of driver genes across TSG/OCG mutant subgroups and employs hierarchical clustering to identify mRNA expression patterns associated with higher prognosis risk. Data from both cancer cohorts were analyzed using agglomerative hierarchical clustering, revealing survival discrepancies between clusters in OCG/TSG-Wild subgroups. Our results emphasize the potential utilizing driver gene expression for prognostic risk estimation in BRCA and HNSC patients with wild-type OCGs and TSGs.

The aging of the world’s population is causing older farmers familiar with agricultural practices to retire, resulting in a growing shortage of related labor. The ever-increasing labor shortage in traditional agriculture has reduced the manual sorting of agricultural products during processing and packaging. Delicate products such as cherry tomatoes pose a severe problem. This study proposes an artificial intelligence of things (AIoT) intelligent production line that leverages deep learning technology, the internet of things, and data analytics for fully automated and efficient 360-degree visual defect detection and classification of cherry tomatoes. By integrating image recognition and AIoT technology, the system can prevent the occurrence of defects during packaging, ensure consistent product quality during grading, and improve the reputation of agricultural producers. We achieved an outstanding mean average precision of 97.4% in the AIoT intelligent production line actual application in defect detection. The results showed that AIoT and intelligent screening plate mechanisms were advantageous for the detection of cherry tomatoes and help promote the development of delicate agricultural products. This study contributed to the development of intelligent agriculture by reducing labor and time costs, quickly detecting defects, effectively controlling sorting quality, and improving crop management through data analysis.

In epistasis analysis, single-nucleotide polymorphism-single-nucleotide polymorphism interactions (SSIs) among genes may, alongside other environmental factors, influence the risk of multifactorial diseases. To identify SSI between cases and controls (i.e. binary traits), the score for model quality is affected by different objective functions (i.e. measurements) because of potential disease model preferences and disease complexities. Our previous study proposed a multiobjective approach-based multifactor dimensionality reduction (MOMDR), with the results indicating that two objective functions could enhance SSI identification with weak marginal effects. However, SSI identification using MOMDR remains a challenge because the optimal measure combination of objective functions has yet to be investigated. This study extended MOMDR to the many-objective version (i.e. many-objective MDR, MaODR) by integrating various disease probability measures based on a two-way contingency table to improve the identification of SSI between cases and controls. We introduced an objective function selection approach to determine the optimal measure combination in MaODR among 10 well-known measures. In total, 6 disease models with and 40 disease models without marginal effects were used to evaluate the general algorithms, namely those based on multifactor dimensionality reduction, MOMDR and MaODR. Our results revealed that the MaODR-based three objective function model, correct classification rate, likelihood ratio and normalized mutual information (MaODR-CLN) exhibited the higher 6.47% detection success rates (Accuracy) than MOMDR and higher 17.23% detection success rates than MDR through the application of an objective function selection approach. In a Wellcome Trust Case Control Consortium, MaODR-CLN successfully identified the significant SSIs (P < 0.001) associated with coronary artery disease. We performed a systematic analysis to identify the optimal measure combination in MaODR among 10 objective functions. Our combination detected SSIs-based binary traits with weak marginal effects and thus reduced spurious variables in the score model. MOAI is freely available at https://sites.google.com/view/maodr/home.

Epistasis detection is vital for understanding disease susceptibility in genetics. Multiobjective multifactor dimensionality reduction (MOMDR) was previously proposed to detect epistasis. MOMDR was performed using binary classification to distinguish the high-risk (H) and low-risk (L) groups to reduce multifactor dimensionality. However, the binary classification does not reflect the uncertainty of the H and L classification. In this study, we proposed an empirical fuzzy MOMDR (EFMOMDR) to address the limitations of binary classification using the degree of membership through an empirical fuzzy approach. The EFMOMDR can simultaneously consider two incorporated fuzzy-based measures, including correct classification rate and likelihood rate, and does not require parameter tuning. Simulation studies revealed that EFMOMDR has higher 7.14% detection success rates than MOMDR, indicating that the limitations of binary classification of MOMDR have been successfully improved by empirical fuzzy. Moreover, EFMOMDR was used to analyze coronary artery disease in the Wellcome Trust Case Control Consortium dataset.

Identifying and characterizing the interaction between risk factors for multiple outcomes (multi-outcome interaction) has been one of the greatest challenges faced by complex multifactorial diseases. However, the existing approaches have several limitations in identifying the multi-outcome interaction. To address this issue, we proposed a multi-outcome interaction identification approach called MOAI. MOAI was motivated by the limitations of estimating the interaction simultaneously occurring in multi-outcomes and by the success of Pareto set filter operator for identifying multi-outcome interaction. MOAI permits the identification for the interaction of multiple outcomes and is applicable in population-based study designs. Our experimental results exhibited that the existing approaches are not effectively used to identify the multi-outcome interaction, whereas MOAI obviously exhibited superior performance in identifying multi-outcome interaction. We applied MOAI to identify the interaction between risk factors for colorectal cancer (CRC) in both metastases and mortality prognostic outcomes. An interaction between vaspin and carcinoembryonic antigen (CEA) was found, and the interaction indicated that patients with CRC characterized by higher vaspin (≥30%) and CEA (≥5) levels could simultaneously increase both metastases and mortality risk. The immunostaining evidence revealed that determined multi-outcome interaction could effectively identify the difference between non-metastases/survived and metastases/deceased patients, which offers multi-prognostic outcome risk estimation for CRC. To our knowledge, this is the first report of a multi-outcome interaction associated with a complex multifactorial disease. MOAI is freely available at https://sites.google.com/view/moaitool/home.

Primers are critical for polymerase chain reaction (PCR) and influence PCR experimental outcomes. Designing numerous combinations of forward and reverse primers involves various primer constraints, posing a computational challenge. Most PCR primer design methods limit parameters because the available algorithms use general fitness functions. This study designed new fitness functions based on user-specified parameters and used the functions in a primer design approach based on the multiobjective particle swarm optimization (MOPSO) algorithm to address the challenge of primer design with user-specified parameters. Multicriteria evaluation was conducted simultaneously based on primer constraints. The fitness functions were evaluated using 7425 DNA sequences and compared with a predominant primer design approach based on optimization algorithms. Each DNA sequence was run 100 times to calculate the difference between the user-specified parameters and primer constraint values. The algorithms based on fitness functions with user-specified parameters outperformed the algorithms based on general fitness functions for 11 primer constraints. Moreover, MOPSO exhibited superior implementation in all experiments. Practical gel electrophoresis was conducted to verify the PCR experiments and established that MOPSO effectively designs primers based on user-specified parameters.

In the era of advanced precision medicine, next-generation genomic data are crucial to achieve breakthroughs in cancer medicine. Effective cancer mortality risk estimation for genomic data associated with cancer remains a vital challenge. The combination of machine learning algorithms and conventional survival analysis can advance the detection of high-risk missense mutation variants and candidate genes associated with cancer mortality in next-generation genomic data. In this article, a fuzzy logic system combined with machine learning algorithms and conventional survival analysis named FuzzyDeepCoxPH was proposed to identify high-risk missense mutation variants and candidate genes highly associated with cancer mortality. DL-derived abstracted weights and Cox proportional hazards (CoxPH) ratios were used to develop four model-based risk scores to consider the factor importance associated with risk stratification, time-varying effects, and individual and interaction effects among features. Fuzzy rules based on a fuzzy logic system were designed to integrate these considerations by merging four model-based risk scores to develop advanced risk estimation. The clinical features and next-generation sequencing of deoxyribonucleic acid and ribonucleic acid genomic data were used to evaluate FuzzyDeepCoxPH performance. The results indicated that FuzzyDeepCoxPH can effectively distinguish high-risk variants and candidate genes related to cancer mortality. In FuzzyDeepCoxPH, the fuzzy logic system was applied to combine DL-based and CoxPH-based models to provide a comprehensive cancer mortality risk estimation for cancer medicine.

Kidney clear cell carcinoma is commonly characterized by poor prognosis, which is associated with the function and differential expression of specific genes. The combination of a typical statistical survival model and nonparametric random forest algorithm provides a more precise estimation for the association between gene mutations and all-cause mortality risk. This study identifies mortality risk-associated gene mutations in kidney clear cell carcinoma by using a random survival forest algorithm. Of 22 candidate genes, VHL (variable importance [VIMP] = 0.097), EDIL3 (VIMP = 0.037), PBRM1 (VIMP = 0.027), PTEN (VIMP = 0.012), BAP1 (VIMP = 0.010), and HMGN5 (VIMP = 0.002) were selected and used to develop a dichotomous risk model for all-cause mortality by using the estimated risk threshold. The high-risk group exhibited a relatively poor survival rate than did the low-risk group (95.5% vs. 93.0%). In conclusion, this study provides a simple dichotomous model for mutation risk, according to the gene mutation risk threshold, by using a random survival forest model. For the gene mutation risk model, VHL, EDIL3, PBRM1, PTEN, BAP1, and HMGN5 were selected to effectively determine the effects of gene mutation on all-cause mortality from kidney clear cell carcinoma.

Studies have proven that single nucleotide polymorphism (SNP)-SNP interaction detection is helpful for understanding the susceptibility of an individual to genetic diseases. Although multifactor dimensionality reduction (MDR) is an effective SNP-SNP interaction detection algorithm, the mechanism of SNP-SNP interaction detection based on MDR contingency tables has not been widely studied. In this study, we propose a multi-objective MDR to detect SNP-SNP interactions. In the proposed multi-objective MDR, multiple measures can be simultaneously considered for detecting epistatic interactions. Then, set theory is used to select the best epistatic interactions in k-fold cross-validation to achieve high identification accuracy for SNP-SNP interactions. Two MDR parameters, namely the correct classification rate (CCR) and predictive summary index (PSI), were used for evaluating the algorithms. The results revealed that the detection success rates of multi-objective MDR were higher than those of other MDR-based algorithms in identifying epistatic interactions. Based on the CCR and PSI, our study demonstrated that the proposed multi-objective MDR can effectively detect SNP-SNP interactions.

Detecting gene-gene interactions in single-nucleotide polymorphism data is vital for understanding disease susceptibility. However, existing approaches may be limited by the sample size in case-control studies. Herein, we propose a balance approach for the multifactor dimensionality reduction (BMDR) method to increase the accuracy of estimates of the prediction error rate in small samples. BMDR explicitly selects the best model by evaluating the average of prediction error rates over k-fold cross-validation without cross-validation consistency selection. In this study, we used several epistatic models with and without marginal effects under different parameter settings (heritability and minor allele frequencies) to evaluate the performance of existing approaches. Using simulated data sets, BMDR successfully detected gene-gene interactions, particularly for data sets with small sample sizes. A large data set was obtained from the Wellcome Trust Case Control Consortium, and results indicated that BMDR could effectively detect significant gene-gene interactions.

Epistasis detection is vital to determining disease susceptibility in the human genome. With rapid advances in technology, multifactor dimensionality reduction (MDR) has become an effective algorithm for epistasis detection. Classification of high-risk (H) and low-risk (L) groups in MDR operations is a key topic, but it has not been thoroughly investigated. In this paper, we propose an improved fuzzy c-means-based entropy (FCME) approach to address the limitations of binary classification. For this approach, the degree of membership in MDR, referred to as FCMEMDR, was used. The FCME approach and MDR measure were integrated to enable more precise differentiation between similar frequencies of multifactor genotypes in the cases of possible epistasis. We used the MDR measures of correct classification rate and likelihood ratio. Numerous simulated datasets were applied, and the experimental results revealed two measures of FCMEMDR with higher detection rates than those of other MDR-based algorithms. Our analysis of binary and fuzzy classifications in MDR operations may offer insights into the problem of uncertainty in H/L classification. Two measures of FCMEMDR detected significant instances of epistasis associated with coronary artery disease in the Wellcome Trust Case Control Consortium dataset. FCMEMDR is freely available at https://gitlab.com/yudalinemail/fcmemdr.

Gene-gene interactions (GGIs) are important markers for determining susceptibility to a disease. Multifactor dimensionality reduction (MDR) is a popular algorithm for detecting GGIs and primarily adopts the correct classification rate (CCR) to assess the quality of a GGI. However, CCR measurement alone may not successfully detect certain GGIs because of potential model preferences and disease complexities. In this study, multiple-criteria decision analysis (MCDA) based on MDR was named MCDA-MDR and proposed for detecting GGIs. MCDA facilitates MDR to simultaneously adopt multiple measures within the two-way contingency table of MDR to assess GGIs; the CCR and rule utility measure were employed. Cross-validation consistency was adopted to determine the most favorable GGIs among the Pareto sets. Simulation studies were conducted to compare the detection success rates of the MDR-only-based measure and MCDA-MDR, revealing that MCDA-MDR had superior detection success rates. The Wellcome Trust Case Control Consortium dataset was analyzed using MCDA-MDR to detect GGIs associated with coronary artery disease, and MCDA-MDR successfully detected numerous significant GGIs (p < 0.001). MCDA-MDR performance assessment revealed that the applied MCDA successfully enhanced the GGI detection success rate of the MDR-based method compared with MDR alone.

Epistasis detection facilitates determining susceptibility to disease. Multifactor dimensionality reduction (MDR) and multiobjective MDR (MOMDR) were proposed for epistasis detection. However, more measures must be investigated for MOMDR. In this study, we incorporated the Youden index (YI) and correct classification rate (CCR) into MOMDR (MOMDR-YC) for epistasis detection. Simulations were conducted to compare MDR-based YI (MDR-Y), MDR-based CCR (MDR-C), and MOMDR-YC. Moreover, the detection success rates of the three approaches are presented. MOMDR-YC revealed that the YI and CCR measures can enhance the detection success rates of MDR. The simulation results revealed that epistasis could be successfully detected by incorporating YI and CCR into MOMDR.

Motivation: Single-nucleotide polymorphism (SNP)-SNP interactions (SSIs) are popular markers for understanding disease susceptibility. Multifactor dimensionality reduction (MDR) can successfully detect considerable SSIs. Currently, MDR-based methods mainly adopt a single-objective function (a single measure based on contingency tables) to detect SSIs. However, generally, a single-measure function might not yield favorable results due to potential model preferences and disease complexities. Approach: This study proposes a multiobjective MDR (MOMDR) method that is based on a contingency table of MDR as an objective function. MOMDR considers the incorporated measures, including correct classification and likelihood rates, to detect SSIs and adopts set theory to predict the most favorable SSIs with cross-validation consistency. MOMDR enables simultaneously using multiple measures to determine potential SSIs. Results: Three simulation studies were conducted to compare the detection success rates of MOMDR and single-objective MDR (SOMDR), revealing that MOMDR had higher detection success rates than SOMDR. Furthermore, the Wellcome Trust Case Control Consortium dataset was analyzed by MOMDR to detect SSIs associated with coronary artery disease. Availability and implementation: MOMDR is freely available at https://goo.gl/M8dpDg. Supplementary information: Supplementary data are available at Bioinformatics online.

MOTIVATION: Detecting epistatic interactions in genome-wide association studies (GWAS) is a computational challenge. Such huge numbers of single-nucleotide polymorphism (SNP) combinations limit the some of the powerful algorithms to be applied to detect the potential epistasis in large-scale SNP datasets. APPROACH: We propose a new algorithm which combines the differential evolution (DE) algorithm with a classification based multifactor-dimensionality reduction (CMDR), termed DECMDR. DECMDR uses the CMDR as a fitness measure to evaluate values of solutions in DE process for scanning the potential statistical epistasis in GWAS. RESULTS: The results indicated that DECMDR outperforms the existing algorithms in terms of detection success rate by the large simulation and real data obtained from the Wellcome Trust Case Control Consortium. For running time comparison, DECMDR can efficient to apply the CMDR to detect the significant association between cases and controls amongst all possible SNP combinations in GWAS. AVAILABILITY AND IMPLEMENTATION: DECMDR is freely available at https://goo.gl/p9sLuJ . CONTACT: [email protected] or [email protected]. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Identifying the epistasis models between single nucleotide polymorphisms (SNPs) in several genes can explain the susceptibility to diseases. The statistical methods have been used to identify the significant epistasis models according to the related statistical values, including odds ratio (OR), chi-square test (χ2), p-value, etc. However, the high calculations limit the statistic to identify the high-order epistasis. In this study, we proposed an lsGA algorithm, genetic algorithm based on local search algorithm, to identify the significant epistasis model amongst the large SNP combinations. Two disease models were used to simulate the large data sets considering the minor allele frequency (MAF), number of SNP, and number of sample. The 3-order epistasis models were identified by chi-square test (χ2) for evaluating the significance (P-value <; 0.05). lsGA was compared with GA to analyze the improvement in the search abilities, and results showed that lsGA provided higher chi-square test values than that of GA.

Genetic association is a challenging task for the identification and characterization of genes that increase the susceptibility to common complex multifactorial diseases. To fully execute genetic studies of complex diseases, modern geneticists face the challenge of detecting interactions between loci. A genetic algorithm (GA) is developed to detect the association of genotype frequencies of cancer cases and noncancer cases based on statistical analysis. An improved genetic algorithm (IGA) is proposed to improve the reliability of the GA method for high-dimensional SNP-SNP interactions. The strategy offers the top five results to the random population process, in which they guide the GA toward a significant search course. The IGA increases the likelihood of quickly detecting the maximum ratio difference between cancer cases and noncancer cases. The study systematically evaluates the joint effect of 23 SNP combinations of six steroid hormone metabolisms, and signaling-related genes involved in breast carcinogenesis pathways were systematically evaluated, with IGA successfully detecting significant ratio differences between breast cancer cases and noncancer cases. The possible breast cancer risks were subsequently analyzed by odds-ratio (OR) and risk-ratio analysis. The estimated OR of the best SNP barcode is significantly higher than 1 (between 1.15 and 7.01) for specific combinations of two to 13 SNPs. Analysis results support that the IGA provides higher ratio difference values than the GA between breast cancer cases and noncancer cases over 3-SNP to 13-SNP interactions. A more specific SNP-SNP interaction profile for the risk of breast cancer is also provided.

The polymerase chain reaction restriction fragment length polymorphism (PCR-RFLP) technique is often used in laboratories and many basic research studies of complex genetic diseases associated with single nucleotide polymorphisms (SNP). When performing PCR-RFLP for SNP genotyping, feasible primer pairs are subject to numerous constraints and require a restriction enzyme for discriminating the target SNP. In this study, we develop a method for natural PCR-RFLP primer design for SNP genotyping using a particle swarm optimization algorithm. The in silico simulation with SNPs of the SLC6A4 gene demonstrates that this method reliably produces designs for natural PCR-RFLP primers which best fit the common primer constraints and also identifies available restriction enzymes.