A computer-implemented method incudes calculating, by a processor, based on sequence data for a tumor from a subject at a plurality of time points, a mutation frequency for each of a plurality of SSVs at each of the time points to provide a plurality of time-resolved mutation frequencies (between 0 and 1) for each of the plurality of SSVs, the sequence data including a plurality of simple somatic variations (SSVs) at each of the time points; binning, by the processor, the plurality of time-resolved mutation frequencies for each SSV at each of the time points to provide a matrix of SSVs and time points; converting, by the processor, the matrix cells to pseudo-clones; and constructing, by the processor, a time-series tumor evolution tree from the pseudo-clones, wherein each time point in the time-series evolution tree represents an event in the subject's cancer treatment.

Approaches provide for machine learning or training algorithms that apply modifications to models based on a type of data obtained, including, for example, producer-specific management practice data, genetic data, among other such data. The animal-centric models can be configured to, for example, quantify gas emissions (e.g., greenhouse gas emissions) that an animal may be expected to emit over a period of time, including, for example, over the animal's lifetime. The emissions in certain embodiments can further enable the certification of emissions for individual animals.

The invention relates to a computer-implemented method comprising: receiving (102) a set of pedigree scores (300, 512) of pairs of plant breeding units over two or more generations; receiving (104) an incomplete set of similarity scores (200, 510) of the pairs of the plant breeding unit pairs; aligning (106) the pedigree scores and the similarity scores of identical plant breeding unit pairs; automatically analyzing (108) the aligned pedigree scores and similarity scores for computing a predictive model (508) based on associations of the similarity scores and of the pedigree scores; using the predictive model for creating (112) a complete set of similarity scores (400, 518); and using (114) the complete set of similarity scores for computationally predicting a feature (522) of a plant breeding unit or of an offspring thereof.

There are provided methods, systems and processes for the utilization of microbial and related genetic information for use in the exploration, determination, production and recovery of natural resources, including energy sources, and the monitoring, control and analysis of processes and activities.

There are provided methods, systems and processes for the utilization of microbial and related genetic information for use in the exploration, determination, production and recovery of natural resources, including energy sources, and the monitoring, control and analysis of processes and activities.

There are provided methods, systems and processes for the utilization of microbial and related genetic information for use in industrial settings, such as the exploration, determination, and recovery of natural resources, minerals, and energy sources, the monitoring and analysis of processes, activities, and materials transmission.

There are provided methods, systems and processes for the utilization of microbial and related genetic information for use in industrial settings, such as the exploration, determination, and recovery of natural resources, minerals, and energy sources, the monitoring and analysis of processes, activities, and materials transmission.

The disclosure relates to quantitative analysis of proteins in different species, including plant species. Disclosed are methods that utilize conserved peptides across species to be used as isotope labeled internal standards, which are then used for absolute quantification of proteins. For example, a method for quantitative protein analysis of two or more species is disclosed, the method including determining a set of common peptides that are common for the two or more species, creating a set of isotope-labeled peptides out of the set of common peptides, adding a predefined amount of the labeled peptides to a sample from one of the two or more species, performing mass spectrometry to create first intensity values for a group of peptides from the sample and second intensity values for the labeled peptides, and calculating a quantitative amount of the group of peptides based on the first intensity values and the second intensity values.

An aspect of the disclosed embodiments includes a system for analysis. The system includes a processor and a memory. The memory includes instructions that, when executed by the processor, cause the processor to receive first initial DNA profile data from a first computing device, receive second initial DNA profile data from a second computing device and compile the first initial DNA profile data and second initial DNA profile data into a first combined DNA profile data file.

An aspect of the disclosed embodiments includes a system for analysis. The system includes a processor and a memory. The memory includes instructions that, when executed by the processor, cause the processor to receive first initial DNA profile data from a first computing device, receive second initial DNA profile data from a second computing device and compile the first initial DNA profile data and second initial DNA profile data into a first combined DNA profile data file.