Provided are methods, system and software for diagnosis, prediction and prognosis of a cancer patient based on the quantitative level of a set of biomarkers. Also provided is a database for the purpose of recording the quantitative level of a set of biomarkers.

Provided herein are methods of identifying gene expression levels in specific cell types based on bulk gene expression levels measured in tissue samples comprising a plurality of cell types.

Provided herein are methods of identifying gene expression levels in specific cell types based on bulk gene expression levels measured in tissue samples comprising a plurality of cell types.

Methods of treatment based on a breast cancer's biomolecule response to targeted treatment are provided. Expression levels of various biomolecules or histological assessment of infiltrating immune cells after initiation of human epidermal growth factor receptor 2 (HER2) targeted treatment can be used to determine whether a breast cancer will achieve a pathologic complete response. Based on likelihood of a pathologic complete response, a breast cancer can be treated accordingly.

Methods of treatment based on a breast cancer's biomolecule response to targeted treatment are provided. Expression levels of various biomolecules or histological assessment of infiltrating immune cells after initiation of human epidermal growth factor receptor 2 (HER2) targeted treatment can be used to determine whether a breast cancer will achieve a pathologic complete response. Based on likelihood of a pathologic complete response, a breast cancer can be treated accordingly.

A method for predicting the cell spatial relation based on single-cell transcriptome sequencing data includes the steps of obtaining a probability matrix P of a cell-cell interaction strength matrix A based on single-cell transcriptome sequencing data; reconstructing, according to the obtained probability matrix P of the cell-cell interaction strength matrix A, a three-dimensional spatial structure in which cells interact with each other; and for each cell in the reconstructed three-dimensional spatial structure in which cells interact with each other, determining the intercellular distance threshold for each cell to interact with h cells on average to obtain an intercellular interaction network. The method requires only the single-cell transcriptome sequencing data to predict the interaction of the cells in three-dimensional space, which breaks the limitation of the existing technology that needs to obtain the spatial relationship of cells through imaging.

A method comprising receiving, for each of a plurality of subjects having a specified type of disease and receiving a specified therapy for treating the disease, a first biological signature obtained pre-treatment and a second biological signature obtained on-treatment; calculating, for each of the plurality of subjects, a set of values representing a ratio between the first and second biological signatures associated with the respective subject; at a training stage, training a machine learning model on a training set comprising: (i) the calculated sets of values, and (ii) labels associated with an outcome of the specified therapy in each of the subjects; to generate a classifier suitable for predicting a response in a target patient to said specified therapy.

A method comprising receiving, for each of a plurality of subjects having a specified type of disease and receiving a specified therapy for treating the disease, a first biological signature obtained pre-treatment and a second biological signature obtained on-treatment; calculating, for each of the plurality of subjects, a set of values representing a ratio between the first and second biological signatures associated with the respective subject; at a training stage, training a machine learning model on a training set comprising: (i) the calculated sets of values, and (ii) labels associated with an outcome of the specified therapy in each of the subjects; to generate a classifier suitable for predicting a response in a target patient to said specified therapy.

The technology described herein is directed to methods of treating and diagnosing bronchial premalignant lesions, e.g. by determining the lesion subtype using one or more biomarkers described herein.

A disease prediction method, apparatus, and computer program are provided. A disease prediction method according to several embodiments of the present disclosure can comprise the steps of: constructing a disease prediction model by learning learning data including ribosome data and disease information for learning, acquiring test ribosome data of an examinee; and predicting disease information about the examinee form the test ribosome data by using the disease prediction model. The disease prediction model can accurately predict disease information about the examinee by detecting and learning the characteristics of ribosome data, which vary according to disease information.