A method and system for analyzing a target network relative to a background network of data using machine learning. The method includes extracting a first feature matrix from an adjacency matrix representative of the target network, extracting a second feature matrix from an adjacency matrix representative of the background network, generating a projection matrix based on the first and second feature matrices using a contrastive learning algorithm, generating a first contrastive matrix representation of the target network based on the projection matrix and the first feature matrix, generating a second contrastive matrix representation of the background network based on the projection matrix and the second feature matrix, and displaying a visualization of unique features of the target network relative to the background network based on the first contrastive matrix and the second contrastive matrix.

The technology disclosed relates to determining pathogenicity of nucleotide variants. In particular, the technology disclosed relates to specifying a particular amino acid at a particular position in a protein as a gap amino acid, and specifying remaining amino acids at remaining positions in the protein as non-gap amino acids. The technology disclosed further relates to generating a gapped spatial representation of the protein that includes spatial configurations of the non-gap amino acids, and excludes a spatial configuration of the gap amino acid, and determining a pathogenicity of a nucleotide variant based at least in part on the gapped spatial representation, and a representation of an alternate amino acid created by the nucleotide variant at the particular position.

Exemplary embodiments described herein provide improved techniques for identifying and accounting for molecule variants when modeling a fragmentation of the molecule. The variants may be identified by comparing possible modifications of molecule fragments against experimental data to rank or score the possible modifications. Possible modifications may be shown in a variant interface where a modification may be selected as a candidate for comparison to experimental data.

Exemplary embodiments described herein provide improved techniques for identifying and accounting for molecule variants when modeling a fragmentation of the molecule. The variants may be identified by comparing possible modifications of molecule fragments against experimental data to rank or score the possible modifications. Possible modifications may be shown in a variant interface where a modification may be selected as a candidate for comparison to experimental data.

Systems and methods for modeling a three-dimensional protein structure are disclosed. The method includes receiving a primary amino acid sequence of a three-dimensional protein, translating the primary amino acid sequence to a first vector, determining a per-residue conformation index for each amino acid residue in the primary amino acid sequence, determining a vector set for each amino acid residue in the primary amino acid sequence, and using the per-residue interaction vector set to generate a multi-dimensional matrix for the three-dimensional protein structure. The first vector includes a unique numerical descriptor value corresponding to each amino acid residue in the primary amino acid sequence. The vector set includes a plurality per-residue interaction factors corresponding to a plurality of conformation indexes for that amino acid residue.

Systems and methods for modeling a three-dimensional protein structure are disclosed. The method includes receiving a primary amino acid sequence of a three-dimensional protein, translating the primary amino acid sequence to a first vector, determining a per-residue conformation index for each amino acid residue in the primary amino acid sequence, determining a vector set for each amino acid residue in the primary amino acid sequence, and using the per-residue interaction vector set to generate a multi-dimensional matrix for the three-dimensional protein structure. The first vector includes a unique numerical descriptor value corresponding to each amino acid residue in the primary amino acid sequence. The vector set includes a plurality per-residue interaction factors corresponding to a plurality of conformation indexes for that amino acid residue.

A stable structure search system includes one or more processors configured to acquire a plurality of degrees of structure similarity between each of a plurality of kinds of molecules and a first molecule included in a target molecule based on each interaction potential of the plurality of kinds of molecules, acquire a plurality of degrees of charge similarity between each of the plurality of kinds of molecules and the first molecule, acquire a plurality of total degrees of similarity based on sums of each of the plurality of degrees of structure similarity and each of the plurality of degrees of charge similarity, determine a second molecule whose total degree of similarly is largest among the plurality of kinds of molecules, acquire energy of a molecular structure of the target molecule based on an interaction potential of the second molecule, and determine whether the calculated energy satisfies a certain condition.

A stable structure search system includes one or more processors configured to acquire a plurality of degrees of structure similarity between each of a plurality of kinds of molecules and a first molecule included in a target molecule based on each interaction potential of the plurality of kinds of molecules, acquire a plurality of degrees of charge similarity between each of the plurality of kinds of molecules and the first molecule, acquire a plurality of total degrees of similarity based on sums of each of the plurality of degrees of structure similarity and each of the plurality of degrees of charge similarity, determine a second molecule whose total degree of similarly is largest among the plurality of kinds of molecules, acquire energy of a molecular structure of the target molecule based on an interaction potential of the second molecule, and determine whether the calculated energy satisfies a certain condition.

In an embodiment, agricultural intelligence computer system stores a digital model of nutrient content in soil which includes a plurality of values and expressions that define transformations of or relationships between the values and produce estimates of nutrient content values in soil. The agricultural intelligence computer receives nutrient content measurement values for a particular field at a particular time. The agricultural intelligence computer system uses the digital model of nutrient content to compute a nutrient content value for the particular field at the particular time. The agricultural intelligence computer system identifies a modeling uncertainty corresponding to the computed nutrient content value and a measurement uncertainty corresponding to the received measurement values. Based on the identified uncertainties, the modeled nutrient content value, and the received measurement values, the agricultural intelligence computer system computes an assimilated nutrient content value.

In an embodiment, agricultural intelligence computer system stores a digital model of nutrient content in soil which includes a plurality of values and expressions that define transformations of or relationships between the values and produce estimates of nutrient content values in soil. The agricultural intelligence computer receives nutrient content measurement values for a particular field at a particular time. The agricultural intelligence computer system uses the digital model of nutrient content to compute a nutrient content value for the particular field at the particular time. The agricultural intelligence computer system identifies a modeling uncertainty corresponding to the computed nutrient content value and a measurement uncertainty corresponding to the received measurement values. Based on the identified uncertainties, the modeled nutrient content value, and the received measurement values, the agricultural intelligence computer system computes an assimilated nutrient content value.