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.

In some implementations, the present solution can determine a first structural vector of a first chemical based on a chemical structure of the first chemical. The system can also determine first target vector of the first chemical based on at least one gene target for the first chemical. The system can use the structural vector and the target vector to generate a toxicity predictor score for the first chemical.

In some implementations, the present solution can determine a first structural vector of a first chemical based on a chemical structure of the first chemical. The system can also determine first target vector of the first chemical based on at least one gene target for the first chemical. The system can use the structural vector and the target vector to generate a toxicity predictor score for the first chemical.

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.

A system for generating a novel molecular structure using a protein structure is disclosed. One or more processors generate a protein voxel representation of a protein structure that includes a multichannel three-dimensional (3D) grid that includes a plurality of channels. A cavity region is detected in the protein voxel representation based on a combination of rule-based detection and a deep learning based model. A cavity voxel representation of the cavity region is generated based on upscaling of a regional voxel of the detected cavity region. A ligand voxel representation of a ligand structure is generated based on the cavity voxel representation. A 3D voxel descriptor is determined for a protein-ligand complex based on the protein voxel representation and the ligand voxel representation. A simplified molecular-input line-entry system (SMILES) of a novel molecular structure is generated using a rich 3D embedding vector, which is based on the 3D voxel descriptor.

A system for generating a novel molecular structure using a protein structure is disclosed. One or more processors generate a protein voxel representation of a protein structure that includes a multichannel three-dimensional (3D) grid that includes a plurality of channels. A cavity region is detected in the protein voxel representation based on a combination of rule-based detection and a deep learning based model. A cavity voxel representation of the cavity region is generated based on upscaling of a regional voxel of the detected cavity region. A ligand voxel representation of a ligand structure is generated based on the cavity voxel representation. A 3D voxel descriptor is determined for a protein-ligand complex based on the protein voxel representation and the ligand voxel representation. A simplified molecular-input line-entry system (SMILES) of a novel molecular structure is generated using a rich 3D embedding vector, which is based on the 3D voxel descriptor.

The present disclosure discusses a system and method for disease module detection. More particularly, a protein network and list of seed proteins are provided to the system. The system iteratively selects one or more candidate proteins for inclusion in the list of seed proteins. The system calculates a connectivity factor for each of the connections of the candidate proteins to proteins listed as seed proteins. Responsive to the calculated connectivity factors the system adds one or more of the candidate proteins to list of seed proteins. At the end of the iterative process the list of seed proteins can be indicative of the disease module.

The present disclosure discusses a system and method for disease module detection. More particularly, a protein network and list of seed proteins are provided to the system. The system iteratively selects one or more candidate proteins for inclusion in the list of seed proteins. The system calculates a connectivity factor for each of the connections of the candidate proteins to proteins listed as seed proteins. Responsive to the calculated connectivity factors the system adds one or more of the candidate proteins to list of seed proteins. At the end of the iterative process the list of seed proteins can be indicative of the disease module.