System and method for assessing the risk of schizophrenia

A method for assessing a risk of schizophrenia in a person, the method comprising:

• creating, via one or more hardware processors, a database of sensory protein sequences of a plurality of organisms, wherein the database of sensory protein sequences comprises information pertaining to the sensory proteins of all fully or partially sequenced bacterial genomes obtained from a plurality of public repositories, wherein the creation of the database of sensory protein sequences is a one-time process and created before a test microbiome sample from the person is provided for diagnosis and therapeutic purposes;
• generating, via the one or more hardware processors, sensory protein abundance profiles of case-control samples obtained from publicly available data;
• applying, via the one or more hardware processors, a random forest classifier on the generated sensory protein abundance profiles of case-control samples to generate a classification model, wherein the classification model generation comprises:
• applying a Random Forest (RF) approach on the sensory protein abundance profiles of case-control samples,
• selecting a random set of sequenced metagenomic reads comprising 90% of microbiome samples as a training set and a remaining 10% as a test set,
• performing 10 replicates on 10-fold cross-validation on the training set to build 100 cross-validation RF models,
• capturing an importance of each feature included in the cross-validation RF models in terms of a GINI index value,
• selecting a predefined number of most important features based on GINI index values from each of the 100 cross-validation RF models to obtain a feature sub-set,
• ranking each of the features in the feature sub-set, on the basis of the sum of their GINI index values,
• obtaining multiple evaluation models by cumulatively adding a next ranked feature in the feature sub-set with features of a previous evaluation model, wherein a first evaluation model is comprised of the top two features in the feature sub-set,
• assessing a performance of all the evaluation models on the basis of their added features,
• evaluating the performance of all the evaluation models on the basis of a balancing Score, followed by Matthews correlation coefficient (MCC) and Area under the curve (AUC) scores, and the evaluation model with least number of features is chosen as a final classification model, and
• validating the final classification model on the test set containing the remaining 10% of the dataset earlier kept aside as the test set, wherein a confidence probability of a binary prediction to be case or control (schizophrenic or healthy) was accounted;
• collecting a microbiome sample from a swab of the person for the assessment of the risk of schizophrenia, wherein the microbiome sample of the person comprises microbial cells;
• extracting DNA from the microbial cells;
• sequencing, via a sequencer, using the extracted DNA to get sequenced metagenomic reads;
• quantifying, via the one or more hardware processors, the abundance of a sensory protein from the sequenced metagenomic reads using the database of sensory protein sequences;
• assessing, via the one or more hardware processors, the risk of the person to be in a schizophrenia diseased state using the classification model and the quantified abundance of the sensory protein from the sequenced metagenomic reads of the person, wherein the person has the risk;
• and
• administering a therapeutic construct to the person with the risk of the schizophrenia, wherein the therapeutic construct comprises one or more non-pathogenic Healthy Therapeutic Markers (HTMs) abundant in a healthy population, a plurality of antibiotic drugs targeted against Disease Markers (DMs), prebiotics, probiotics, synbiotics, postbiotics and a fecal microbiome transplant to help the person's gut microbiome to attain a healthy equilibrium, wherein the one or more non-pathogenic HTMs comprises one or more of Acidithiobacillus caldus, Desulfovibrio aespoeensis, Desulfurivibrio alkaliphilus, Halobacterium salinarum, Halobacterium sp., Jannaschia sp, and Truepera radiovictrix,
• wherein the method is implemented for assessing and treating the schizophrenia in the person by modulating a disease microbiome towards the healthy equilibrium.