A system for determining user taste changes using a plurality of biological extraction data and artificial intelligence includes at least a computing device, wherein the computing device is designed and configured to receive, from a user, at least a first element of biological extraction data, calculate at least a first taste index of the user, wherein calculating further comprises training a first machine learning process as a function of training data correlating biological extraction data with taste indices, calculating the at least a first taste index as a function of the first machine learning process and the at least a first element of biological extraction data, generate a taste profile using the first taste index, and determine, using at least a second element of biological extraction data and a second machine learning process, at least a change in user taste profile.

A system for determining user taste changes using a plurality of biological extraction data and artificial intelligence includes at least a computing device, wherein the computing device is designed and configured to receive, from a user, at least a first element of biological extraction data, calculate at least a first taste index of the user, wherein calculating further comprises training a first machine learning process as a function of training data correlating biological extraction data with taste indices, calculating the at least a first taste index as a function of the first machine learning process and the at least a first element of biological extraction data, generate a taste profile using the first taste index, and determine, using at least a second element of biological extraction data and a second machine learning process, at least a change in user taste profile.

Analysis of genetic disease progression may be provided. Data about a set of molecular status may be received. A dynamic prediction model of molecular interactions may be provided over time. The molecular statuses of the set over time may be determined using the dynamic prediction model. The determined molecular statuses may be clustered by applying an interaction-aware metric for the analysis of the genetic disease progression.

Analysis of genetic disease progression may be provided. Data about a set of molecular status may be received. A dynamic prediction model of molecular interactions may be provided over time. The molecular statuses of the set over time may be determined using the dynamic prediction model. The determined molecular statuses may be clustered by applying an interaction-aware metric for the analysis of the genetic disease progression.

A method and system for generating a protein sequence is implemented using a computer-implemented neural network. An empty or partially filed sequence of node elements, representing amino acid positions of the protein sequence, and an edge index, having edge elements defining physical interaction between amino acid positions, are received. The computer-implemented neural network operates to determine enhanced edge attribute values for edge elements of the edge index and enhanced amino acid values for node elements of the sequence. Amino acid values are generated for elements of the partially filed sequence having missing values.

Systems and methods are described for genetic analysis. In certain embodiments, the system reads a plurality of input parameters, where the input parameters include a file-path to a mutation input file and storing the mutation input file. The mutation input file is comprised of a mutated genetic sequence, and the sorting mutations in the mutation input file are based on starting position. The computer then receives data identifying chromosome location, start position, reference allele and mutated allele for each mutation within the mutation input file and loads a standardized reference genome. In certain embodiments, the GTF file identifies the location of mutated genes in the input standardized reference genome. The system then compares the mutation input file to the standardized reference genome and generates a mutation index file. The mutation index file identifies mutated nucleotides in the customized reference genome and can be used to quantify allelic expression to diagnose a genetic condition like cancer and improve therapeutic options for cancer patients.

Systems and methods are described for genetic analysis. In certain embodiments, the system reads a plurality of input parameters, where the input parameters include a file-path to a mutation input file and storing the mutation input file. The mutation input file is comprised of a mutated genetic sequence, and the sorting mutations in the mutation input file are based on starting position. The computer then receives data identifying chromosome location, start position, reference allele and mutated allele for each mutation within the mutation input file and loads a standardized reference genome. In certain embodiments, the GTF file identifies the location of mutated genes in the input standardized reference genome. The system then compares the mutation input file to the standardized reference genome and generates a mutation index file. The mutation index file identifies mutated nucleotides in the customized reference genome and can be used to quantify allelic expression to diagnose a genetic condition like cancer and improve therapeutic options for cancer patients.

Methods are provided for genome editing. On example method includes editing a genome sequence of an organism with multiple edits simultaneously without precise knowledge of a phenotypic effect of each individual one of the multiple edits, wherein the multiple edits are selected based on a prediction of an aggregate phenotypic effect of the multiple edits on a phenotypic trait. The method also includes aggregating the multiple edits into multi-dimensional pools, whereby phenotypic effects of contrasting pools of edits are compared to ascertain which of the multiple edits are most likely to be causing large phenotypic effects while eliminating need to evaluate each edit separately. The organism may include one of: maize, soybean, wheat, sorghum, rice, cotton, rapeseed, sunflower, bean, tomato, squash, cucumber, melon, pepper, watermelon, eggplant, okra, pea, chickpea, lentil, peanut, onion, carrot, celery, beet, cauliflower, broccoli, cabbage, Brussels sprout, radish, black-eyed pea, potato, sweet-potato, sugar cane, cassava, and banana.

Methods are provided for genome editing. On example method includes editing a genome sequence of an organism with multiple edits simultaneously without precise knowledge of a phenotypic effect of each individual one of the multiple edits, wherein the multiple edits are selected based on a prediction of an aggregate phenotypic effect of the multiple edits on a phenotypic trait. The method also includes aggregating the multiple edits into multi-dimensional pools, whereby phenotypic effects of contrasting pools of edits are compared to ascertain which of the multiple edits are most likely to be causing large phenotypic effects while eliminating need to evaluate each edit separately. The organism may include one of: maize, soybean, wheat, sorghum, rice, cotton, rapeseed, sunflower, bean, tomato, squash, cucumber, melon, pepper, watermelon, eggplant, okra, pea, chickpea, lentil, peanut, onion, carrot, celery, beet, cauliflower, broccoli, cabbage, Brussels sprout, radish, black-eyed pea, potato, sweet-potato, sugar cane, cassava, and banana.

The invention generally relates to the field of protein sequences and of generation of functional protein sequences. More particularly, the invention concerns a method for generating functional protein sequences with generative adversarial networks. The described method for functional sequence generation comprises plurality of steps, each of which is crucial to ensure the high percentage of functional sequences in the final sequence set: selecting a plurality of existing protein sequences to define the approximate sequence space for the later generated synthetic sequences, processing the selected protein sequences, approximating the unknown true distribution of amino acids of the pre-processed sequences using a variation of generative adversarial networks, obtaining protein sequences from the approximated distribution, processing of the obtained protein sequences. The described method provides a resource (e.g. time, cost) efficient way of producing synthetic protein sequences which have a high probability of being functional experimentally.