Machine Learning help us in identifying the origin of several Medical Syndromes?

Machine learning is doing wonders currently and its latest effort has been invested in locating the roots of various medical syndromes. In a recent study, several syndromes like Chronic Fatigue Syndrome, Gulf-war Syndrome, and Post-Accutane syndrome etc. has been studied from the perspective of their genetic origins, pathways and other factors. Machine learning has been applied on the researcher data and their abstracts and with the help of natural language processing and network analysis, this conclusion has been achieved.

Several topics like Sulfation, Carboxylation, and Adrenal deficiency came up in this process, and they criss-crossed each other too at times. Vitamin K metabolism also came up in this situation and it has been treated as a foundational step in the hypotheses. The associations in this regard come handy in the network analysis further on. Then, the central nodes are identified accordingly. While there are certain areas where the connections are denser and vitamin K and Urea Cycle having major role to play in this connections, the issue of centrality has been solved. Now, the researcher has a challenge to know why these topics come centrally. Before that, the researcher is advised to understand the correlatives of such a huge input data and the importance of certain variables over others.

The first hypothesis

Before beginning with first hypothesis, it is important to note that many other pathways are also under research. For example, the pathways corresponding to Sulfation are extremely important and could also be discussed. However, for the sake of generality, the discussion is limited to vitamin K metabolism. The precedence given is not in order of some preference, but as a matter of choice.

The first hypothesis states that a specific type of network analysis and various outputs from many machine learning algorithms have helped in reaching the decision that Vitamin-K related genes are decisive in the syndromes and can have an impact on syndromes that show similar symptoms. There is a list of genes whose individual or combined contribution can affect the syndrome. They include GAS6, MGP, TYRO3, VKORC1 etc. Surely, the claim prompts a discussion and such a discussion is necessary to arrive at the second hypothesis.

The discussion of first hypothesis and the advent of the second

The necessity of Vitamin K in this matter brings one to the discussion. Often, it is noted that animals suffering from MERKT deficiency will display certain signs of automated immunity. They will go on to form certain automated serum antibodies against the collagens, DNA etc. Antibodies can also be found in case of CFS patients too in this regard. The other gene important here is VKORC1 which forms the protein disulfide bond with Endoplasmic reticulum.

However, Vitamin K requires bile salts when they require absorption proper. In fact, the acid metabolism was one of the topics that remain in Algorithms nurtured by NLP. It is here the second hypothesis arrives in the picture. The individual’s DNA determines the interventions and these interventions can surely play an important role in reducing or even better, reversing the symptoms that are currently associated with the syndromes currently mentioned.

The second hypothesis is surely the more usable one and better derived than the first. While a conclusive judgement is yet to be awarded to any of them, many medical foundations have taken up the two hypotheses and are working towards testing it. They include foundations for the specific syndromes as well as actions for syndromes. A complete evaluation is necessary before it is possible to understand the implications. However, the progress made by NLP is surely remarkable and may well pave path for more intervention of machine learning in this regard.