Personalized Probiotics Using AI

Predicting host organism properties using microbiome composition data and metabolome interactions. The method involves analyzing a host's microbiome to obtain frequency values of different taxa. It preprocesses the microbiome by grouping and normalizing taxa based on classification. An FCNN embeds the preprocessed microbiome into a latent space representing microbiome-metabolome interactions. This latent space is used to predict host properties, like metabolite concentrations, by approximating the microbiome-metabolite relationship. This leverages the complex, non-linear microbiome-metabolome interactions using the latent representation.

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A method for screening personalized probiotics, prebiotics, foods, health functional foods, and drugs using fecal samples and a proprietary medium composition. The method involves treating fecal samples with candidate materials, culturing, and analyzing the resulting microbiota and metabolites to identify effective personalized microbiota-improving agents. The composition includes L-cysteine, sodium chloride, sodium carbonate, potassium chloride, and hemin, which are combined with fecal samples to create a controlled in vitro environment that mimics the human gut. The method enables rapid screening of personalized microbiota-improving agents using fecal samples and special media, and can be used to diagnose diseases caused by intestinal disorders.

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Using machine learning to design bacterial communities for specific tasks like suppressing pathogens or degrading contaminants. The method involves training a machine learning model to predict community performance based on bacterial strain composition. The training uses real experimental data on actual communities. The model is then used to identify communities with high predicted performance for a task. This allows efficient screening of large numbers of possible communities. The training strategy involves generating data with strains and task scores, and prioritizing genetically diverse communities to reduce redundancy.

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A system and method for determining a personalized probiotic therapeutic regimen based on monitoring gut microbiota composition and dietary preferences. The system employs metabolic simulations to predict the efficacy of probiotic interventions on varying gut microbiota compositions and dietary preferences, using metrics such as net-effect and sustainability to quantify the benefits of probiotic supplementation. The system receives a biological sample from the subject, extracts DNA, determines microbial abundance, and creates genome-scale metabolic models of the subject's microbiota and potential probiotic organisms. It then performs metabolic simulations to predict the growth of each organism in mono- and co-culture, and computes the net-effect and sustainability of each probiotic organism. The system selects the most efficacious probiotic organism based on these metrics and provides a personalized treatment recommendation.

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Device for providing customized probiotic recommendations based on microbial analysis information, comprising: an information acquisition unit for acquiring personal and analysis information; a health information generation unit that generates health information based on the acquired information; and a recommendation information generation unit that generates probiotic recommendation information suitable for the subject based on the generated health information.

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System and method for modifying nutrition requirements based on user attributes and real-time monitoring data, enabling personalized dietary recommendations that enhance specific health attributes. The system integrates user input, monitoring device data, and AI-driven analysis to generate tailored nutrition requirements, which are then matched to optimal food choices that fulfill those needs.

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Methods and systems for altering the microbiome composition of a subject through diet modification. The methods involve providing a subject with a personalized diet guidance program based on their microbiome analysis, and modifying their food intake accordingly. The diet guidance program is developed using machine learning algorithms that correlate food consumption with microbiome changes in a large database of individuals. By following the personalized diet guidance program, subjects can increase the presence of beneficial microbes and decrease the presence of pathogenic microbes in their gut microbiome.

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A personalized probiotic formulation that utilizes the patient's own microbiome to treat various health conditions. The formulation is produced through a targeted genetic analysis of the patient's microbiome, followed by the selection of genetically safe strains that are then grown and purified. This approach eliminates the risks associated with traditional probiotics, which often contain foreign microorganisms. The formulation can be administered orally, topically, or intravaginally, and can be customized based on the patient's specific microbiome composition.

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An AI system generates educational responses to nutrition-related inquiries based on user biological data. The system identifies a user's nutritional needs from their physiological data, selects a machine learning model trained on biological data and nutrition resources, and generates a response tailored to the user's needs.

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A system for providing personalized product recommendations using biologic data and machine learning. The system integrates multi-omic data, including genetic, environmental, and lifestyle factors, to predict user preferences and product suitability. A neural network architecture processes the biologic data to identify essential biomarkers and interpret complex biological datasets, enabling actionable insights for personalized decision-making. The system's application extends to industries such as healthcare and consumer products, where it can transform product recommendations and decision-making processes.

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A method for predicting the composition of mixed microbiota samples using a machine learning model that accounts for interactions between microorganisms. The method involves training a model on reference mix profiles to predict the composition of new mixes, and then using the trained model to optimize the selection of initial samples for a target mix profile. The model incorporates an interaction matrix that represents the positive and negative interactions between microorganisms, allowing for more accurate predictions of mix compositions compared to traditional linear models.

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A method and device for predicting microbial species using artificial intelligence. The method involves learning a specialized AI model for microbial species prediction, parsing whole genome sequences into short segments, and inputting the parsed sequences into the learned model to predict species. The AI model is trained on a vast DNA database using a novel tokenizer and pre-trained language model optimized for DNA sequence analysis.

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Personalized nutritional supplements tailored to individual health needs. The method involves ranking ingredients based on overall benefit for a subject's suboptimal health conditions. The ranked ingredients are then filled into packages until they reach capacity. This allows customization by selecting the most beneficial ingredients for the subject's specific needs.

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Method for modeling an intestinal microbiome of a person without metagenomic data, using phenotypic, medical, and lifestyle data to generate a personalized microbiome model. The method involves constructing a composite model from multiple individuals with similar characteristics, simulating diet effects on the microbiome, and identifying dietary recommendations to modify the target individual's phenotypic and medical characteristics.

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A method for identifying disease-specific microbial consortia using machine learning. The method involves preprocessing gut microbiota data, generating candidate consortia through clustering, and training a machine learning model to select the most predictive consortia. The model is then used to identify disease-relevant microbial consortia that can be used to develop effective probiotics for specific diseases.

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A system for designing personalized microbiome therapeutics and diet based on functions of an individual's microbiome. The system analyzes gut microbiome composition and function to identify dysbiotic states and perturbed metabolic pathways. It generates personalized therapeutic recommendations, including probiotics, prebiotics, and dietary interventions, based on the individual's microbiome profile and functional deficits.

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Using a digital twin to generate personalized health care plans for individuals based on their specific biomarker profiles. The digital twin is a computational model that mimics physiological processes using machine learning and equations libraries. It allows predicting health states, disease progression, and treatment outcomes by capturing multiomic interactions from genomic, transcriptomic, metabolomic, epigenetic, and microbiome data. The digital twin can also simulate the effects of exogenous factors like drugs or mutations on the body.

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Personalized recommendations for maintaining or improving gut microbiome health based on predicting microbiome status using questionnaires and biomarkers. The method involves determining microbiome status through questionnaires, biomarkers, and anthropometric measures. It then provides customized recommendations for diet, supplements, menus, and recipes to improve microbiome diversity. The recommendations consider factors like antibiotics usage, lifestyle choices, and biomarkers to tailor advice for each individual.

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A computer-implemented method for intervening on a bacterial population through metagenomics, comprising acquiring information on an analyzed bacterial population, selecting a plurality of enzymes of interest, identifying major and minor bacterial contributors, and producing an intervention plan to re-establish a correct population of the bacterial population. The method uses enzyme abundance and metabolic potential to identify contributors and develop personalized dietary interventions based on scientific literature correlations.

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Reducing the risk of injury to a pet by providing personalized pet product recommendations based on a microbiome result and pet metadata to a user. The recommendations include receiving, by one or more processors, pet metadata from a user, analyzing the pet fecal sample to determine a microbiome result, processing the pet metadata based on the microbiome result to determine one or more pet product recommendations, and displaying the pet product recommendations to the user.

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