Neural Network Gene Regulatory Relationship Detection Model
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Solution Overview
Problem
Current methods lack an effective way to detect regulatory relationships between genes, which is crucial for understanding disease mechanisms at the level of gene regulation.
Innovation Solution
A gene regulatory relationship detection model training method using a neural network model is developed, which obtains material group data of sample genes, determines predicted probabilities of regulatory relationships, and trains a model to detect regulatory relationships between target genes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a neural network model is used to detect regulatory relationships between genes, then the detection capability and understanding of disease mechanisms are improved, but the complexity of the system and computational resources required increase
Solution Approach 1:
The patent uses a neural network model as an intermediary system to detect regulatory relationships between genes. The model takes material group data of genes as input and outputs predicted probabilities of regulatory relationships, serving as a mediator between raw biological data and meaningful biological insights about disease mechanisms.
Solution Approach 2:
The patent replaces traditional mechanical or manual methods of analyzing gene regulatory relationships with a computational neural network system. This substitution enables automated detection of regulatory relationships by using machine learning algorithms to process material group data and predict gene interactions, thereby improving detection accuracy while reducing manual intervention.
2Measurement precision
If material group data of multiple sample genes is analyzed to determine predicted probabilities of regulatory relationships, then the detection accuracy is improved, but the computational time and resources increase
Solution Approach 1:
The patent performs preliminary actions by collecting and organizing material group data of multiple sample genes before conducting the actual regulatory relationship detection. The neural network model is pre-trained with this data, and the material group data is prepared in advance, which enables efficient prediction of regulatory relationships without requiring extensive computational time during the actual detection phase.
Data Source
AI summary
A gene regulatory relationship detection model training method performed by an electronic device, and relate to the field of biology technologies. The gene regulatory relationship detection model training method includes: obtaining material group data of a plurality of sample genes and an annotated regulatory relationship between at least one sample gene pair; determining a predicted probability that a regulatory relationship exists between each two sample genes among the plurality of sample genes by using a neural network model based on the material group data of the plurality of sample genes; and training the neural network model to obtain a gene regulatory relationship detection model, based on the annotated regulatory relationship between the at least one sample gene pair and the predicted probability between each two sample genes.