SABO-ILSTSVR: a genomic prediction method based on improved least squares twin support vector regression.

In modern breeding practices, genomic prediction (GP) uses high-density single nucleotide polymorphisms (SNPs) markers to predict genomic estimated breeding values (GEBVs) for crucial phenotypes, thereby speeding up selection breeding process and shortening generation intervals. However, due to the characteristic of genotype data typically having far fewer sample numbers than SNPs markers, overfitting commonly arise during model training. To address this, the present study builds upon the Least Squares Twin Support Vector Regression (LSTSVR) model by incorporating a Lasso regularization term named ILSTSVR. Because of the complexity of parameter tuning for different datasets, subtraction average based optimizer (SABO) is further introduced to optimize ILSTSVR, and then obtain the GP model named SABO-ILSTSVR. Experiments conducted on four different crop datasets demonstrate that SABO-ILSTSVR outperforms or is equivalent in efficiency to widely-used genomic prediction methods. Source codes and data are available at: https://github.com/MLBreeding/SABO-ILSTSVR.

MSXFGP: combining improved sparrow search algorithm with XGBoost for enhanced genomic prediction.

BACKGROUND: With the significant reduction in the cost of high-throughput sequencing technology, genomic selection technology has been rapidly developed in the field of plant breeding. Although numerous genomic selection methods have been proposed by researchers, the existing genomic selection methods still face the problem of poor prediction accuracy in practical applications. RESULTS: This paper proposes a genome prediction method MSXFGP based on a multi-strategy improved sparrow search algorithm (SSA) to optimize XGBoost parameters and feature selection. Firstly, logistic chaos mapping, elite learning, adaptive parameter adjustment, Levy flight, and an early stop strategy are incorporated into the SSA. This integration serves to enhance the global and local search capabilities of the algorithm, thereby improving its convergence accuracy and stability. Subsequently, the improved SSA is utilized to concurrently optimize XGBoost parameters and feature selection, leading to the establishment of a new genomic selection method, MSXFGP. Utilizing both the coefficient of determination R2 and the Pearson correlation coefficient as evaluation metrics, MSXFGP was evaluated against six existing genomic selection models across six datasets. The findings reveal that MSXFGP prediction accuracy is comparable or better than existing widely used genomic selection methods, and it exhibits better accuracy when R2 is utilized as an assessment metric. Additionally, this research provides a user-friendly Python utility designed to aid breeders in the effective application of this innovative method. MSXFGP is accessible at https://github.com/DIBreeding/MSXFGP . CONCLUSIONS: The experimental results show that the prediction accuracy of MSXFGP is comparable or better than existing genome selection methods, providing a new approach for plant genome selection.

BLAB2CancerKD: a knowledge graph database focusing on the association between lactic acid bacteria and cancer, but beyond.

In a broad sense, lactic acid bacteria (LAB) is a general term for Gram-positive bacteria that can produce lactic acid by utilizing fermentable carbohydrates. It is widely used in essential fields such as industry, agriculture, animal husbandry and medicine. At the same time, LAB are closely related to human health. They can regulate human intestinal flora and improve gastrointestinal function and body immunity. Cancer, a disease in which some cells grow out of control and spread to other body parts, is one of the leading causes of human death worldwide. In recent years, the potential of LAB in cancer treatment has attracted attention. Mining knowledge from the scientific literature significantly accelerates its application in cancer treatment. Using 7794 literature studies of LAB cancer as source data, we have processed 16 543 biomedical concepts and 23 091 associations by using automatic text mining tools combined with manual curation of domain experts. An ontology containing 31 434 pieces of structured data is constructed. Finally, based on ontology, a knowledge graph (KG) database, which is called Beyond 'Lactic acid bacteria to Cancer Knowledge graph Database' (BLAB2CancerKD), is constructed by using KG and web technology. BLAB2CancerKD presents all the relevant knowledge intuitively and clearly in various data presentation forms, and the interactive system function also makes it more efficient. BLAB2CancerKD will be continuously updated to advance the research and application of LAB in cancer therapy. Researchers can visit BLAB2CancerKD at. Database URL http://110.40.139.2:18095/.