Parturition:
The onset of labor can lead to complications when this occurs before or after the ideal time frame, such as preterm or post-term pregnancy. To get a better understanding, Mason used gene array profiling of myometrial events during guinea pig pregnancy to achieve a better comprehension of the molecular mechanisms that regulate labor [16]. In his study, he used AI technology to help him develop diagrams composed of gene circuits. This helped him extract the pertinent information about myometrial activation from a considerable amount of data. However, this study was done in the myometrium of guinea pigs, and there is yet to be a study illustrating this in humans. As Norwitz describes in his article about AI, and whether computers can help solve the problems of parturition; further studies need to be done to elucidate a more comprehensive understanding of the genes involved in human parturition [17,18]. Additionally, we need to consider variables such as the complications of pregnancy, and how these factors can alter myometrial gene expression. This study gave an overview in our understanding of how genes that are activated during labor can potentially lead to effective medical interventions when necessary, thus helping to treat some of the disorders of parturition, such as preterm labor and post-term pregnancy, and decrease the associated perinatal morbidity and mortality involved with these complications.
An example of a novel program that can aid with the task of understanding gene expression in the myometrium is programmed such as the Meta Core program [19]. It consists of a knowledge database and software that can analyze data and gene lists. Its limitations are that it needs prior knowledge about the problem or a predefined algorithm. Reasons for preterm or post-term labor are multifactorial, and not all factors are known or well understood [17,18,19]. Nonetheless, other programs that do not require prior knowledge or utilize a defined algorithm, such as neural networks, can overcome these aforementioned limitations. Meta Core is also able to analyze large amounts of data, such as genomes and their variables, and analyze it nonlinearly. A disadvantage is that it requires a significant amount of time and research because all simulated computer predictions need to be tested in humans to confirm their accuracy and practicability.