Machine Learning Researches On Exploration Of The H2H Gene In 3D View

The genome is organized hierarcHi-Cally in a three-dimensional(3D)space within the nucleus and displays multiple layers of functional complexity,including chromosomal regions,megabase long topological domains,and DNA loops in cisregulatory elements.A comprehensive understanding of the relationship between genome structure and function is an important but extremely difficult technical challenge.Newly developed biochemical methods(such as 3C,4C,5C,Hi-C,and ChIA-PET)have been used to explore physical interactions.Frequency of action.These physical interaction frequencies are defined as the probability of a pair of chromosomal loci interacting in a large cell population.H2 H usually has two genes that are extremely easy to co-express and co-function.H2 H plays a vital role in human disease control.Therefore,there have been a lot of researches on H2 H gene and related two-way promoters at home and abroad.However,the study of H2 H gene pairs has been stagnating from the study of twodimensional spatial information,resulting in the omission of some H2 H gene pairs.Ensemble learning has achieved remarkable results in practical applications.It is one of the most widely used algorithms for machine learning and the most powerful tool in bioinformatics research,and is being further applied to other fields of research.The basic idea of ensemble learning is to complete the target task by constructing multiple classifiers,first training through a base learner,and then adjusting the data set according to the performance of the previous learner.The learning error samples are more updated in subsequent training.Pay more attention and repeat the training until the entire model converges.This design can effectively solve more special cases in the data set.Ensemble learning makes the learning process complicated,but it is this complexity that improves the fault tolerance of the model.This paper first systematically analyzes the huge challenges faced by H2 H genes in the research status,and summarizes the characteristics of the integrated learning method itself and the applicability and development status of H2 H genes for analytical applications.Then,an ensemble learning method is proposed to study and predict H2 H gene pairs in 3D space.This method can effectively predict H2 H gene pairs in 3D space.The experimental results of XGBoost,LightGBM and Ensemble(Logistic Regression,Neural Network,SVM)are also compared and analyzed in the experiment.The results show that compared with the traditional two-dimensional analysis method,more H2 H gene pairs are found in the three-dimensional space through the ensemble learning method,wHi-Ch helps to advance the research and development of three-dimensional space H2 H.