A predicting method of cell deconvolution based on a convolutional neural network is provided. The convolutional neural network technology is used to speculate the cell type composition proportion of a tissue from single-cell RNA sequencing data. Compared with a traditional cell deconvolution algorithm, the predicting method of cell deconvolution based on a convolutional neural network overcomes the defects that the traditional cell deconvolution algorithm needs to carry out complex data preprocessing and needs to design a mathematical algorithm to standardize the single-cell sequencing data. According to the convolutional neural network designed by the present disclosure, hidden features can be extracted from the single-cell RNA sequencing data, network nodes have very high robustness to noise and errors of the data, and internal relations among various genes are fully mined, so that the cell deconvolution performance is improved. Meanwhile, the model of the present disclosure is established based on the neural network.

The technology disclosed generates a reference array of variant data for locations that are shared between read results which are to be compared, and generates hashes over a selected pattern length of positions in the reference array to independently produce non-unique window hashes for base patterns in the read results. It then selects for comparison window hashes that occur less than a ceiling number of times and compares the selected window hashes to identify common window hashes between the read results. It then determines a similarity measure for the read results based on the common window hashes.

The technology disclosed generates a reference array of variant data for locations that are shared between read results which are to be compared, and generates hashes over a selected pattern length of positions in the reference array to independently produce non-unique window hashes for base patterns in the read results. It then selects for comparison window hashes that occur less than a ceiling number of times and compares the selected window hashes to identify common window hashes between the read results. It then determines a similarity measure for the read results based on the common window hashes.

Provided is a method for predicting protein-protein interaction. Also provided are an electronic device and a non-transitory computer readable storage medium.

Provided is a method for predicting protein-protein interaction. Also provided are an electronic device and a non-transitory computer readable storage medium.

Methods for identifying biosynthetic gene clusters that include genes for producing compounds that interact with specific target proteins are disclosed. Some methods relate to bioinformatics methods for identifying and/or prioritizing biosynthetic gene clusters. Related systems, components, and tools for the identification and expression of such gene clusters are also disclosed.

An apparatus includes at least one processor configured to obtain multiple spatiotemporal population projection models. Different spatiotemporal population projection models are associated with different pests, diseases, or biocontrol agents. Each spatiotemporal population projection model defines how the associated pest, disease, or biocontrol agent spreads and contracts in a growing area over time. The at least one processor is also configured to receive information associated with an actual presence of a specific pest, disease, or biocontrol agent at one or more monitored spatial locations in the growing area. Different monitored spatial locations in the growing area are associated with different plants. The at least one processor is further configured to project a future presence of the specific pest, disease, or biocontrol agent in the growing area using the spatiotemporal population projection model associated with the specific pest, disease, or biocontrol agent.

An apparatus includes at least one processor configured to obtain multiple spatiotemporal population projection models. Different spatiotemporal population projection models are associated with different pests, diseases, or biocontrol agents. Each spatiotemporal population projection model defines how the associated pest, disease, or biocontrol agent spreads and contracts in a growing area over time. The at least one processor is also configured to receive information associated with an actual presence of a specific pest, disease, or biocontrol agent at one or more monitored spatial locations in the growing area. Different monitored spatial locations in the growing area are associated with different plants. The at least one processor is further configured to project a future presence of the specific pest, disease, or biocontrol agent in the growing area using the spatiotemporal population projection model associated with the specific pest, disease, or biocontrol agent.

The technology disclosed generates a reference array of variant data for locations that are shared between read results which are to be compared, and generates hashes over a selected pattern length of positions in the reference array to independently produce non-unique window hashes for base patterns in the read results. It then selects for comparison window hashes that occur less than a ceiling number of times and compares the selected window hashes to identify common window hashes between the read results. It then determines a similarity measure for the read results based on the common window hashes.

The technology disclosed generates a reference array of variant data for locations that are shared between read results which are to be compared, and generates hashes over a selected pattern length of positions in the reference array to independently produce non-unique window hashes for base patterns in the read results. It then selects for comparison window hashes that occur less than a ceiling number of times and compares the selected window hashes to identify common window hashes between the read results. It then determines a similarity measure for the read results based on the common window hashes.