Invention Grant
- Patent Title: Multimodal cell complex neural networks for prediction of multiple drug side effects severity and frequency
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Application No.: US17950906Application Date: 2022-09-22
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Publication No.: US12051512B2Publication Date: 2024-07-30
- Inventor: Mustafa Hajij , Ghada Alzamzmi , Nina Miolane
- Applicant: Santa Clara University , University of South Florida , The Regents of the University of California
- Applicant Address: US CA Santa Clara
- Assignee: Santa Clara University,The Regents of the University of California,University of South Florida
- Current Assignee: Santa Clara University,The Regents of the University of California,University of South Florida
- Current Assignee Address: US CA Santa Clara; US CA Oakland; US FL Tampa
- Agency: LUMEN PATENT FIRM
- Main IPC: G16H70/40
- IPC: G16H70/40 ; G06N7/01 ; G16H10/60
Abstract:
A method for predicting side effects of a combination of drugs administered concurrently includes training a multi-modal cell complex neural network (MCXN) on a dataset. The MCXN includes nodes representing the drugs and proteins, pair-wise relationships between nodes representing interactions between pairs of drugs and/or proteins, and k-wise relationships between the nodes representing interactions between k drugs and/or proteins, where k¿2. The training dataset includes a list of drugs, a list of proteins, and pharmacological information about the drugs in the list of drugs and proteins in the list of proteins. A specification of the combination of at least three drugs to be administered concurrently is input to the MCXN which predicts probabilities that administering the combination of drugs concurrently results in potential side effects. It also predicts both frequencies of the potential side effects and severities of the potential side effects.
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