发起人,DNA区域短(50-1500个碱基对),在基因转录调控中发挥关键作用。许多危险的疾病,同样是癌症,心血管,和炎症性肠病,是由启动子的遗传变异引起的。因此,正确鉴定和鉴定启动子对药物的发现具有重要意义。然而,识别启动子及其优势的实验方法在成本方面具有挑战性,时间,和资源。因此,计算技术对于从未注释的基因组数据中正确表征启动子是非常理想的。这里,我们设计了一个强大的双层深度学习预测器,名为“PROCABLES”,将DNA样品分别区分为第一阶段的启动子和第二阶段的强启动子或弱启动子。所提出的方法利用了五个不同的特征,如word2vec,k间隔的核苷酸对,基于三核苷酸倾向的特征,三核苷酸组成,和电子-离子相互作用伪势,从DNA序列中提取隐藏的模式。之后,通过将卷积神经网络(CNN)与双向长短期记忆(LSTM)集成在一起,使用多视图属性来训练所提出的模型,从而形成堆叠框架。PROCABLES模型使用十倍交叉验证测试,第一层和第二层的精度为0.971和0.920,MCC0.940和0.840。分别。预测结果预计所提出的PROCABLES方案优于靶向启动子及其类型的高级计算预测因子。总之,这项研究将为识别大规模启动子和其他一般DNA问题提供有用的提示。
Promoters, which are short (50-1500 base-pair) in DNA regions, have emerged to play a critical role in the regulation of gene transcription. Numerous dangerous diseases, likewise cancer, cardiovascular, and inflammatory bowel diseases, are caused by genetic variations in promoters. Consequently, the correct identification and characterization of
promoters are significant for the discovery of drugs. However, experimental approaches to recognizing
promoters and their strengths are challenging in terms of cost, time, and resources. Therefore, computational techniques are highly desirable for the correct characterization of
promoters from unannotated genomic data. Here, we designed a powerful bi-layer deep-learning based predictor named \"PROCABLES\", which discriminates DNA samples as promoters in the first-phase and strong or weak
promoters in the second-phase respectively. The proposed method utilizes five distinct features, such as word2vec, k-spaced nucleotide pairs, trinucleotide propensity-based features, trinucleotide composition, and electron-ion interaction pseudopotentials, to extract the hidden patterns from the DNA sequence. Afterwards, a stacked framework is formed by integrating a convolutional neural network (CNN) with bidirectional long-short-term memory (LSTM) using multi-view attributes to train the proposed model. The PROCABLES model achieved an accuracy of 0.971 and 0.920 and the MCC 0.940 and 0.840 for the first and second-layer using the ten-fold cross-validation test, respectively. The predicted results anticipate that the proposed PROCABLES protocol outperformed the advanced computational predictors targeting promoters and their types. In summary, this research will provide useful hints for the recognition of large-scale promoters in particular and other DNA problems in general.