Techniques related to the detection or identification of biological cells or substances.

This disclosure relates generally to methods and systems for designing a personalized composition that can counteract or reduce skin malodor. Conventional techniques that prepare compositions to treat specific malodorants are limited and such compositions cannot be personalized to an individual according to the type of malodorant produced on skin of the individual. The present disclosure provides a method for designing a personalized composition for degrading the malodorant present in skin of the subject. First, a biological sample from the subject is collected, and metabolite composition of the sample is determined. next, one or more malodorant in the sample are identified based on the metabolite composition. Further, a combination of bacteria capable of degrading the identified malodorant are identified using the microbe-malodorant microbial knowledge base and lastly the personalized composition of the identified bacteria is designed using the combination of bacteria.

This disclosure relates generally to methods and systems for designing a personalized composition that can counteract or reduce skin malodor. Conventional techniques that prepare compositions to treat specific malodorants are limited and such compositions cannot be personalized to an individual according to the type of malodorant produced on skin of the individual. The present disclosure provides a method for designing a personalized composition for degrading the malodorant present in skin of the subject. First, a biological sample from the subject is collected, and metabolite composition of the sample is determined. next, one or more malodorant in the sample are identified based on the metabolite composition. Further, a combination of bacteria capable of degrading the identified malodorant are identified using the microbe-malodorant microbial knowledge base and lastly the personalized composition of the identified bacteria is designed using the combination of bacteria.

A method that provides a graphical indication of whether a patient will have cancer recurrence uses univariate and bivariate prognostic features that were generated as part of a minimal spanning tree (MST). The method determines the values of first and second features. A first value is measured by detecting objects in an image of tissue from the cancer patient stained with a protein-specific IHC biomarker. A second value is measured using objects marked with an mRNA-specific probe biomarker detected in the tissue. The first feature is the univariate prognostic feature for cancer recurrence in a cohort of cancer patients. A combination of the first and second features is the bivariate prognostic feature for cancer recurrence in the cohort. The first and second features are elements of the MST. Nodes of the MST represent the univariate features, edges represent the bivariate features, and edge weights represent prognostic significance of bivariate features.

A method that provides a graphical indication of whether a patient will have cancer recurrence uses univariate and bivariate prognostic features that were generated as part of a minimal spanning tree (MST). The method determines the values of first and second features. A first value is measured by detecting objects in an image of tissue from the cancer patient stained with a protein-specific IHC biomarker. A second value is measured using objects marked with an mRNA-specific probe biomarker detected in the tissue. The first feature is the univariate prognostic feature for cancer recurrence in a cohort of cancer patients. A combination of the first and second features is the bivariate prognostic feature for cancer recurrence in the cohort. The first and second features are elements of the MST. Nodes of the MST represent the univariate features, edges represent the bivariate features, and edge weights represent prognostic significance of bivariate features.

A method, including identifying, in a nucleotide string, at least two exons, at least one acceptor, at least one donor, and at least one intron between the at least two exons, is provided. The method includes identifying, in the nucleotide string, a cryptic splice site comprising a sequence of nucleotides based on a similarity score with at least one of the acceptor or the donor, and graphically marking, in a display for a user, the nucleotide string at a location indicative of an exon, an intron, a true splice site, and optionally a cryptic splice site when the similarity score is higher than a pre-selected threshold. A system and a non-transitory, computer-readable medium including instructions to cause the system to perform the method are also provided.

A method, including identifying, in a nucleotide string, at least two exons, at least one acceptor, at least one donor, and at least one intron between the at least two exons, is provided. The method includes identifying, in the nucleotide string, a cryptic splice site comprising a sequence of nucleotides based on a similarity score with at least one of the acceptor or the donor, and graphically marking, in a display for a user, the nucleotide string at a location indicative of an exon, an intron, a true splice site, and optionally a cryptic splice site when the similarity score is higher than a pre-selected threshold. A system and a non-transitory, computer-readable medium including instructions to cause the system to perform the method are also provided.

A genome system for displaying an interactive genome dashboard is provided herein. The genome system includes processing device having a processor configured to perform machine learning and performing a matching function between phenotypes and gene variants to create gene matches based upon multiple text inputs and genome sequences introduced through the interactive genome dashboard. The processing device includes memory wherein previously generated matches are tagged and stored based upon the multiple text inputs, the genome sequence, and subsequent receipt of user interaction with the generated matches.

A genome system for displaying an interactive genome dashboard is provided herein. The genome system includes processing device having a processor configured to perform machine learning and performing a matching function between phenotypes and gene variants to create gene matches based upon multiple text inputs and genome sequences introduced through the interactive genome dashboard. The processing device includes memory wherein previously generated matches are tagged and stored based upon the multiple text inputs, the genome sequence, and subsequent receipt of user interaction with the generated matches.

A method includes obtaining, from one or more sequencing devices, raw data detected from luminescent labels associated with nucleotides during nucleotide incorporation events; and processing the raw data to perform a comparison of base calls produced by a learning enabled, automatic base calling module of the one or more sequencing devices with actual values associated with the raw data, wherein the base calls identify one or more individual nucleotides from the raw data. Based on the comparison, an update to the learning enabled, automatic base calling module is created using at least some of the obtained raw data, and the update is made available to the one or more sequencing devices.