A method of informed selection of prescriptive therapies, the method comprising: receiving by a computing device compositional training data wherein the compositional training data contains a first plurality of unclassified data entries; retrieving by the computing device a user biological profile wherein the user biological profile further comprises at least a biological extraction and a prescriptive therapy indicator, wherein the biological extraction includes at least an element of user biological data; generating by the computing device an unsupervised machine-learning model using a clustering algorithm, wherein the unsupervised machine-learning model is trained using the compositional training data, wherein the unsupervised machine-learning model is configured to receive the user biological profile as an input and output a therapy response label, wherein the therapy response label describes contents of a cluster produced by the clustering algorithm of the unsupervised machine learning model, wherein the contents of the cluster include data entries contained within the cluster that share an attribute with the user biological profile; and generating, by the computing device, a therapy response machine-learning model configured to receive the user biological profile and a proposed prescriptive therapy as inputs, and output a prescriptive therapy label for the proposed prescriptive therapy, wherein the prescriptive therapy label provides an indicator as to whether or not the proposed prescriptive therapy is compatible with the body of the user, the generating of the therapy response machine-learning model comprising: training the therapy response machine-learning model according to the therapy response label by using the data entries contained within the cluster that share an attribute with the user biological profile, and according to contraindication training data, wherein the contraindication training data includes a second plurality of data entries including prescriptive therapies data and correlated contraindications data, wherein correlated contraindications data is indicative of a reason for not administering the prescriptive therapy of the prescriptive therapies data to which it is correlated; receiving, from a remote device, a proposed prescriptive therapy; inputting the user biological profile and the proposed prescriptive therapy to the therapy response machine-learning model; and outputting, by the therapy response machine-learning model, the prescriptive therapy label for the proposed prescriptive therapy. receiving by a computing device compositional training data wherein the compositional training data contains a first plurality of unclassified data entries; retrieving by the computing device a user biological profile wherein the user biological profile further comprises at least a biological extraction and a prescriptive therapy indicator, wherein the biological extraction includes at least an element of user biological data; generating by the computing device an unsupervised machine-learning model using a clustering algorithm, wherein the unsupervised machine-learning model is trained using the compositional training data, wherein the unsupervised machine-learning model is configured to receive the user biological profile as an input and output a therapy response label, wherein the therapy response label describes contents of a cluster produced by the clustering algorithm of the unsupervised machine learning model, wherein the contents of the cluster include data entries contained within the cluster that share an attribute with the user biological profile; and generating, by the computing device, a therapy response machine-learning model configured to receive the user biological profile and a proposed prescriptive therapy as inputs, and output a prescriptive therapy label for the proposed prescriptive therapy, wherein the prescriptive therapy label provides an indicator as to whether or not the proposed prescriptive therapy is compatible with the body of the user, the generating of the therapy response machine-learning model comprising: training the therapy response machine-learning model according to the therapy response label by using the data entries contained within the cluster that share an attribute with the user biological profile, and according to contraindication training data, wherein the contraindication training data includes a second plurality of data entries including prescriptive therapies data and correlated contraindications data, wherein correlated contraindications data is indicative of a reason for not administering the prescriptive therapy of the prescriptive therapies data to which it is correlated; receiving, from a remote device, a proposed prescriptive therapy; inputting the user biological profile and the proposed prescriptive therapy to the therapy response machine-learning model; and outputting, by the therapy response machine-learning model, the prescriptive therapy label for the proposed prescriptive therapy.