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.