A computer-implemented method of prioritising biological targets is disclosed. The method comprises: receiving a selection of classes of one or more categories; and, for each of a plurality of biological targets, determining an extent of alignment of the biological target to each selected class. The method also comprises prioritising the biological targets based on the extents of alignment; and outputting a representation of one or more prioritised biological targets.

A biological function estimation device includes a processor configured to predict a drug reaction in a patient based on an evaluation value obtained by evaluating a biological function of the patient from a test result before drug administration and data on a drug to be administered to the patient, and configured to correct the evaluation value according to a comparison result between an obtained prediction value and a measurement value obtained by measuring the drug reaction after the drug administration.

A biological function estimation device includes a processor configured to predict a drug reaction in a patient based on an evaluation value obtained by evaluating a biological function of the patient from a test result before drug administration and data on a drug to be administered to the patient, and configured to correct the evaluation value according to a comparison result between an obtained prediction value and a measurement value obtained by measuring the drug reaction after the drug administration.

Embodiments include a system for determining cardiovascular information for a patient. The system may include at least one computer system configured to receive patient-specific data regarding a geometry of the patient's heart, and create a three-dimensional model representing at least a portion of the patient's heart based on the patient-specific data. The at least one computer system may be further configured to create a physics-based model relating to a blood flow characteristic of the patient's heart and determine a fractional flow reserve within the patient's heart based on the three-dimensional model and the physics-based model.

Embodiments include a system for determining cardiovascular information for a patient. The system may include at least one computer system configured to receive patient-specific data regarding a geometry of the patient's heart, and create a three-dimensional model representing at least a portion of the patient's heart based on the patient-specific data. The at least one computer system may be further configured to create a physics-based model relating to a blood flow characteristic of the patient's heart and determine a fractional flow reserve within the patient's heart based on the three-dimensional model and the physics-based model.

Based on the three-dimensional structure of albumin, the inventors have designed variant polypeptides (muteins) which have one or more cysteine residues with a free thiol group (hereinafter referred to as “thio-albumin”). The variant polypeptide may be conjugated through the sulphur atom of the cysteine residue to a conjugation partner such as a bioactive compound.

Based on the three-dimensional structure of albumin, the inventors have designed variant polypeptides (muteins) which have one or more cysteine residues with a free thiol group (hereinafter referred to as “thio-albumin”). The variant polypeptide may be conjugated through the sulphur atom of the cysteine residue to a conjugation partner such as a bioactive compound.

An apparatus for analyzing an in vivo component is provided. The apparatus for analyzing an in vivo component may include an impedance sensor including a first electrode and a second electrode configured to contact a fluid channel of a fluid to be analyzed. The apparatus may include an impedance measurement device configured to apply a current to the first electrode and the second electrode, measure a voltage between the first electrode and the second electrode based on applying the current, and measure an impedance of the fluid based on the measured voltage. The apparatus may include a processor configured to model the measured impedance using an equivalent circuit; and analyze the in vivo component based on modeling the measured impedance using the equivalent circuit.

An apparatus for analyzing an in vivo component is provided. The apparatus for analyzing an in vivo component may include an impedance sensor including a first electrode and a second electrode configured to contact a fluid channel of a fluid to be analyzed. The apparatus may include an impedance measurement device configured to apply a current to the first electrode and the second electrode, measure a voltage between the first electrode and the second electrode based on applying the current, and measure an impedance of the fluid based on the measured voltage. The apparatus may include a processor configured to model the measured impedance using an equivalent circuit; and analyze the in vivo component based on modeling the measured impedance using the equivalent circuit.

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.