Catfish Taguchi-Based Binary Differential Evolution Algorithm for Analyzing Single Nucleotide Polymorphism Interactions in Chronic Dialysis

A Novel Detection Method for High-Order SNP Epistatic Interactions Based on Explicit-Encoding-Based Multitasking Harmony Search

To elucidate the genetic basis of complex diseases, it is crucial to discover the single-nucleotide polymorphisms (SNPs) contributing to disease susceptibility. This is particularly challenging for high-order SNP epistatic interactions (HEIs), which exhibit small individual effects but potentially large joint effects. These interactions are difficult to detect due to the vast search space, encompassing billions of possible combinations, and the computational complexity of evaluating them. This study proposes a novel explicit-encoding-based multitasking harmony search algorithm (MTHS-EE-DHEI) specifically designed to address this challenge. The algorithm operates in three stages. First, a harmony search algorithm is employed, utilizing four lightweight evaluation functions, such as Bayesian network and entropy, to efficiently explore potential SNP combinations related to disease status. Second, a G-test statistical method is applied to filter out insignificant SNP combinations. Finally, two machine learning-based methods, multifactor dimensionality reduction (MDR) as well as random forest (RF), are employed to validate the classification performance of the remaining significant SNP combinations. This research aims to demonstrate the effectiveness of MTHS-EE-DHEI in identifying HEIs compared to existing methods, potentially providing valuable insights into the genetic architecture of complex diseases. The performance of MTHS-EE-DHEI was evaluated on twenty simulated disease datasets and three real-world datasets encompassing age-related macular degeneration (AMD), rheumatoid arthritis (RA), and breast cancer (BC). The results demonstrably indicate that MTHS-EE-DHEI outperforms four state-of-the-art algorithms in terms of both detection power and computational efficiency. The source code is available at https://github.com/shouhengtuo/MTHS-EE-DHEI.git .

MTHSA-DHEI: multitasking harmony search algorithm for detecting high-order SNP epistatic interactions

Genome-wide association studies have succeeded in identifying genetic variants associated with complex diseases, but the findings have not been well interpreted biologically. Although it is widely accepted that epistatic interactions of high-order single nucleotide polymorphisms (SNPs) [(1) Single nucleotide polymorphisms (SNP) are mainly deoxyribonucleic acid (DNA) sequence polymorphisms caused by variants at a single nucleotide at the genome level. They are the most common type of heritable variation in humans.] are important causes of complex diseases, the combinatorial explosion of millions of SNPs and multiple tests impose a large computational burden. Moreover, it is extremely challenging to correctly distinguish high-order SNP epistatic interactions from other high-order SNP combinations due to small sample sizes. In this study, a multitasking harmony search algorithm (MTHSA-DHEI) is proposed for detecting high-order epistatic interactions [(2) In classical genetics, if genes X1 and X2 are mutated and each mutation by itself produces a unique disease status (phenotype) but the mutations together cause the same disease status as the gene X1 mutation, gene X1 is epistatic and gene X2 is hypostatic, and gene X1 has an epistatic effect (main effect) on disease status. In this work, a high-order epistatic interaction occurs when two or more SNP loci have a joint influence on disease status.], with the goal of simultaneously detecting multiple types of high-order (k1-order, k2-order, …, kn-order) SNP epistatic interactions. Unified coding is adopted for multiple tasks, and four complementary association evaluation functions are employed to improve the capability of discriminating the high-order SNP epistatic interactions. We compare the proposed MTHSA-DHEI method with four excellent methods for detecting high-order SNP interactions for 8 high-orderepistatic interaction models with no marginal effect (EINMEs) and 12 epistatic interaction models with marginal effects (EIMEs) (*) and implement the MTHSA-DHEI algorithm with a real dataset: age-related macular degeneration (AMD). The experimental results indicate that MTHSA-DHEI has power and an F1-score exceeding 90% for all EIMEs and five EINMEs and reduces the computational time by more than 90%. It can efficiently perform multiple high-order detection tasks for high-order epistatic interactions and improve the discrimination ability for diverse epistasis models.

A Secure High-Order Gene Interaction Detecting Method for Infectious Diseases

Infectious diseases pose a serious threat to human life, the Genome Wide Association Studies (GWAS) can analyze susceptibility genes of infectious diseases from the genetic level and carry out targeted prevention and treatment. The susceptibility genes for infectious diseases often act in combination with multiple susceptibility sites; therefore, high-order epistasis detection has become an important means. However, due to intensive computational burden and diversity of disease models, existing methods have drawbacks on low detection power, high computation cost, and preference for some types of disease models. Furthermore, these methods are exposed to repeated query and model inversion attacks in the process of iterative optimization, which may disclose Single Nucleotide Polymorphism (SNP) information associated with individual privacy. Therefore, in order to solve these problems, this paper proposed a safe harmony search algorithm for high-order gene interaction detection, termed as HS-DP. Firstly, the linear weighting method was used to integrate 5 objective functions to screen out high-order SNP sets with high correlation, including K2-Score, JS divergence, logistic regression, mutual information, and Gini. Then, based on the Differential Privacy (DP) theory, the function disturbance mechanism was introduced to protect the security of individual privacy information associated with the objective function, and we proved the rationality of the disturbance mechanism theoretically. Finally, the practicability and superiority of the algorithm were verified by experiments. Experimental results showed that the algorithm proposed in this paper could improve the detection accuracy to the greatest extent while guaranteeing privacy.

Identifying the Association of Time-Averaged Serum Albumin Levels with Clinical Factors among Patients on Hemodialysis Using Whale Optimization Algorithm

Time-averaged serum albumin (TSA) is commonly associated with clinical outcomes in hemodialysis (HD) patients and considered as a surrogate indicator of nutritional status. The whale optimization algorithm-based feature selection (WOFS) model could address the complex association between the clinical factors, and could further combine with regression models for application. The present study aimed to demonstrate an optimal multifactor TSA-associated model, in order to interpret the complex association between TSA and clinical factors among HD patients. A total of 829 HD patients who met the inclusion criteria were selected for analysis. Monthly serum albumin data tracked from January 2009 to December 2013 were converted into TSA categories based on a critical value of 3.5 g/dL. Multivariate logistic regression was used to analyze the association between TSA categories and multiple clinical factors using three types of feature selection models, namely the fully adjusted, stepwise, and WOFS models. Five features, albumin, age, creatinine, potassium, and HD adequacy index (Kt/V level), were selected from fifteen clinical factors by the WOFS model, which is the minimum number of selected features required in multivariate regression models for optimal multifactor model construction. The WOFS model yielded the lowest Akaike information criterion (AIC) value, which indicated that the WOFS model could achieve superior performance in the multifactor analysis of TSA for HD patients. In conclusion, the application of the optimal multifactor TSA-associated model could facilitate nutritional status monitoring in HD patients.

The Value of CT Angiography Based on Intelligent Segmentation Algorithm for Survival of Hemodialysis Patients

This study was to explore the application value for central venous stenosis and occlusion in hemodialysis patients under the CT angiography based on intelligent segmentation algorithm, so that patients can survive better. Spiral CT was used to examine upper limb swelling in 62 uremic hemodialysis patients at a speed of 3.8 mL/s. Nonionic iodine contrast agent was injected around the contralateral limb. The total dosage of 90-102 mL, it was scanned by intelligent trigger technology. The trigger scanning threshold was set. The monitoring point was located in the superior vena cava. CT with convolutional neural network intelligent segmentation algorithm was used to process image data. Finally, the quality of life and related biochemical levels of patients before and after hemodialysis were detected. Under the CT angiography of intelligent segmentation algorithm, 77 stenoses were found in 62 uremic patients, including 48 stenoses of the brachial vein and 17 stenoses of the superior vena cava. The correlation coefficient between CT angiography and digital subtraction angiography (DSA) imaging results of intelligent segmentation algorithm was 0.411. Segmentation effect of the algorithm in this study: automatic segmentation accuracy was greater than 79%. After hemodialysis treatment, the scores of physical fitness, pain, social function, and energy status of patients were significantly increased compared with those before treatment, and the levels of albumin, serum phosphorus, and parathyroid hormone were significantly decreased (P < 0.05). In summary, CT angiography with intelligent segmentation algorithm can obtain clear, intuitive, and complete vascular walking images, and better display subclavian vein, brachiocephalic vein, and superior vena cava. It can provide more valuable support for surgical intervention and has certain application value for better survival of hemodialysis patients.