摘要
一位新闻记者-机器人与机器学习的工作人员新闻编辑每日新闻-机器学习的新研究是一篇报告的主题。根据NewsRx记者在中华民国江西的新闻报道，研究表明：“增强子是基因组DNA元件，调控对细胞分化和应激反应等生物学过程至关重要的相邻基因表达。然而，目前预测DNA增强子的机器学习方法往往没有充分利用基因序列中的隐藏特征，限制了模型的准确性。”为此，本文提出了一种基于深度学习的Enhanc预测方法——PDCNN模型，从基因序列中提取核苷酸的统计表示，识别DNA修饰物序列中核苷酸的位置分布信息，并利用卷积神经网络结构，PD CNN采用双卷积层和全连通层。交叉熵LOS S函数使用梯度下降算法迭代更新，提高了预测精度。模型参数经过微调以选择最优组合进行训练，准确率达到95%以上。与传统方法和现有模型的对比分析表明了PDCNN的鲁棒特征提取能力。
Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on Machine Learning is th e subject of a report. According to news reporting originating in Jiangxi, Peopl e's Republic of China, by NewsRx journalists, research stated, "Enhancers, genom ic DNA elements, regulate neighboring gene expression crucial for biological pro cesses like cell differentiation and stress response. However, current machine l earning methods for predicting DNA enhancers often underutilize hidden features in gene sequences, limiting model accuracy." The news reporters obtained a quote from the research from Gannan Normal Univers ity, "Hence, this article proposes the PDCNN model, a deep learning-based enhanc er prediction method. PDCNN extracts statistical nucleotide representations from gene sequences, discerning positional distribution information of nucleotides i n modifier-like DNA sequences. With a convolutional neural network structure, PD CNN employs dual convolutional and fully connected layers. The cross-entropy los s function iteratively updates using a gradient descent algorithm, enhancing pre diction accuracy. Model parameters are fine-tuned to select optimal combinations for training, achieving over 95% accuracy. Comparative analysis w ith traditional methods and existing models demonstrates PDCNN's robust feature extraction capability."
NETL
NSTL