Methods, systems and non-transitory machine-readable storage medium are provided to mitigate insertion errors and deletion errors in STR sequences and improve accuracy in determination of the number of repeats. A method includes determining one or more optimum clusters for a set of flow space signal measurements, wherein at least one of the optimum clusters is associated with a homopolymer length, modifying a base call at the position in the repeat region sequence to the homopolymer length associated with the optimum cluster to produce a corrected repeat region sequence, thereby correcting an insertion error or a deletion error. The method may further include detecting variations in the flanks associating those variations with the length of the STR.

Methods, systems and non-transitory machine-readable storage medium are provided to mitigate insertion errors and deletion errors in STR sequences and improve accuracy in determination of the number of repeats. A method includes determining one or more optimum clusters for a set of flow space signal measurements, wherein at least one of the optimum clusters is associated with a homopolymer length, modifying a base call at the position in the repeat region sequence to the homopolymer length associated with the optimum cluster to produce a corrected repeat region sequence, thereby correcting an insertion error or a deletion error. The method may further include detecting variations in the flanks associating those variations with the length of the STR.

Many microbes are capable of synthesizing anti-cancer products, however existing state of art is limited by focus on industrial production of the said products. A method and system for preparing a knowledgebase of microbes and microbial functions to identify good and bad microbes have been provided. The present disclosure therefore further describes methods and compositions for the risk assessment, prevention and management of various forms of cancer by using microbes, microbial products utilizing the knowledgebase of microbes and microbial function. The method is configured to priming the microbes inside the host for boosting the immune response against cancer initiation, progression, recurrence and associated side effects. The use of microbes and microbial products can be provided in the form of probiotics, supplements, and prebiotics etc. along with creation of right sets of nutrition conditions in the host for the proper functioning of the microbes and microbial products.

Many microbes are capable of synthesizing anti-cancer products, however existing state of art is limited by focus on industrial production of the said products. A method and system for preparing a knowledgebase of microbes and microbial functions to identify good and bad microbes have been provided. The present disclosure therefore further describes methods and compositions for the risk assessment, prevention and management of various forms of cancer by using microbes, microbial products utilizing the knowledgebase of microbes and microbial function. The method is configured to priming the microbes inside the host for boosting the immune response against cancer initiation, progression, recurrence and associated side effects. The use of microbes and microbial products can be provided in the form of probiotics, supplements, and prebiotics etc. along with creation of right sets of nutrition conditions in the host for the proper functioning of the microbes and microbial products.

Embodiments of the present technology include a method for testing a blood sample for sepsis. The method may include receiving a blood sample from an individual. The method may also include executing an instruction to analyze the blood sample for sepsis. In addition, the method may include measuring values of a set of characteristics in the blood sample. The set of characteristics being determined prior to measuring the values. The method may further include analyzing the values of the set of characteristics to produce a representation of a suspicion of sepsis. In addition, the method may include displaying the representation. Embodiments also include systems for testing blood sample for sepsis.

Embodiments of the present technology include a method for testing a blood sample for sepsis. The method may include receiving a blood sample from an individual. The method may also include executing an instruction to analyze the blood sample for sepsis. In addition, the method may include measuring values of a set of characteristics in the blood sample. The set of characteristics being determined prior to measuring the values. The method may further include analyzing the values of the set of characteristics to produce a representation of a suspicion of sepsis. In addition, the method may include displaying the representation. Embodiments also include systems for testing blood sample for sepsis.

The present disclosure relates to in vitro experiments and in silico computation and machine-learning based techniques to iteratively improve a process for identifying binders that can bind any given molecular target. Particularly, aspects of the present disclosure are directed to obtaining initial sequence data for aptamers that bind to a target, measuring a first signal to noise ratio within the initial sequence data, provisioning, based on the first signal to noise ratio, a first machine-learning system, generating, by the first machine-learning system, a first set of aptamer sequences, obtaining subsequent sequence data for aptamers that bind to the target, measuring a second signal to noise ratio within the subsequent sequence data, provisioning, based on the second signal to noise ratio, a second machine-learning system, generating, by the second machine-learning system, a second set of aptamer sequences, and outputting the second set of aptamer sequences.

The present disclosure relates to in vitro experiments and in silico computation and machine-learning based techniques to iteratively improve a process for identifying binders that can bind any given molecular target. Particularly, aspects of the present disclosure are directed to obtaining initial sequence data for aptamers that bind to a target, measuring a first signal to noise ratio within the initial sequence data, provisioning, based on the first signal to noise ratio, a first machine-learning system, generating, by the first machine-learning system, a first set of aptamer sequences, obtaining subsequent sequence data for aptamers that bind to the target, measuring a second signal to noise ratio within the subsequent sequence data, provisioning, based on the second signal to noise ratio, a second machine-learning system, generating, by the second machine-learning system, a second set of aptamer sequences, and outputting the second set of aptamer sequences.

Mechanisms are provided for performing automated monitoring and retraining of infectious disease computer models. A trained infectious disease computer model is executed on case report data for a target region to generate prediction results predicting a state of an infectious disease spread within the target region for a given time. The prediction results generated by the trained infectious disease computer model are automatically compared to ground truth data to determine a deviation between the prediction results and the ground truth data. The ground truth data comprises at least one of actual case report data collected and reported by source computing systems for the given time, or a previous prediction result generated by the trained infectious disease computer model. Statistical test(s) are applied to the deviation to determine if it is statistically significant, and if so, re-training of the trained infectious disease computer model is automatically initiated.

Mechanisms are provided for performing automated monitoring and retraining of infectious disease computer models. A trained infectious disease computer model is executed on case report data for a target region to generate prediction results predicting a state of an infectious disease spread within the target region for a given time. The prediction results generated by the trained infectious disease computer model are automatically compared to ground truth data to determine a deviation between the prediction results and the ground truth data. The ground truth data comprises at least one of actual case report data collected and reported by source computing systems for the given time, or a previous prediction result generated by the trained infectious disease computer model. Statistical test(s) are applied to the deviation to determine if it is statistically significant, and if so, re-training of the trained infectious disease computer model is automatically initiated.