Integrating Multiple Database Resources to Elucidate the Gene Flow in Southeast Asian Pig Populations.

The domestic pig (Sus scrofa) and its subfamilies have experienced long-term and extensive gene flow, particularly in Southeast Asia. Here, we analyzed 236 pigs, focusing on Yunnan indigenous, European commercial, East Asian, and Southeast Asian breeds, using the Pig Genomics Reference Panel (PGRP v1) of Pig Genotype-Tissue Expression (PigGTEx) to investigate gene flow and associated complex traits by integrating multiple database resources. In this study, we discovered evidence of admixtures from European pigs into the genome of Yunnan indigenous pigs. Additionally, we hypothesized that a potential conceptual gene flow route that may have contributed to the genetic composition of the Diannan small-ear pig is a gene exchange from the Vietnamese pig. Based on the most stringent gene introgression scan using the fd statistic, we identified three specific loci on chromosome 8, ranging from 51.65 to 52.45 Mb, which exhibited strong signatures of selection and harbored the NAF1, NPY1R, and NPY5R genes. These genes are associated with complex traits, such as fat mass, immunity, and litter weight, in pigs, as supported by multiple bio-functionalization databases. We utilized multiple databases to explore the potential dynamics of genetic exchange in Southeast Asian pig populations and elucidated specific gene functionalities.

Role of Artificial Intelligence in Drug Discovery and Target Identification in Cancer.

Drug discovery and development (DDD) is a highly complex process that necessitates precise monitoring and extensive data analysis at each stage. Furthermore, the DDD process is both time-consuming and costly. To tackle these concerns, artificial intelligence (AI) technology can be used, which facilitates rapid and precise analysis of extensive datasets within a limited timeframe. The pathophysiology of cancer disease is complicated and requires extensive research for novel drug discovery and development. The first stage in the process of drug discovery and development involves identifying targets. Cell structure and molecular functioning are complex due to the vast number of molecules that function constantly, performing various roles. Furthermore, scientists are continually discovering novel cellular mechanisms and molecules, expanding the range of potential targets. Accurately identifying the correct target is a crucial step in the preparation of a treatment strategy. Various forms of AI, such as machine learning, neural-based learning, deep learning, and network-based learning, are currently being utilised in applications, online services, and databases. These technologies facilitate the identification and validation of targets, ultimately contributing to the success of projects. This review focuses on the different types and subcategories of AI databases utilised in the field of drug discovery and target identification for cancer.