利用深度学习和自然语言处理预测蛋白质序列中的ATP结合位点
Predicting ATP binding sites in protein sequences using Deep Learning and Natural Language Processing
AI总结：
本文利用深度学习和NLP方法，以PSSM和词嵌入为特征，采用2D CNN和LightGBM等算法预测ATP-蛋白质结合位点，实验性能优于现有基准。
摘要：
预测基因中的ATP-蛋白质结合位点在生物学和医学领域具有重大意义。该领域的大多数研究都是通过实验室中耗时且资源密集的“湿实验”进行的。多年来，研究人员一直在探索计算方法，利用先进的深度学习和NLP算法的优势来实现同样的目标。在本文中，我们提出开发方法来分类ATP-蛋白质结合位点。我们主要使用PSSM（位置特异性评分矩阵）和几种词嵌入作为特征进行了各种实验。我们使用2D CNN（二维卷积神经网络）和LightGBM分类器作为主要深度学习算法。MP3Vec和BERT模型也在我们的研究中接受了测试。我们的实验结果表明，相比最先进的基准，性能有所提升。
英文摘要：
Predicting ATP-Protein Binding sites in genes is of great significance in the field of Biology and Medicine. The majority of research in this field has been conducted through time- and resource-intensive 'wet experiments' in laboratories. Over the years, researchers have been investigating computational methods computational methods to accomplish the same goals, utilising the strength of advanced Deep Learning and NLP algorithms. In this paper, we propose to develop methods to classify ATP-Protein binding sites. We conducted various experiments mainly using PSSMs and several word embeddings as features. We used 2D CNNs and LightGBM classifiers as our chief Deep Learning Algorithms. The MP3Vec and BERT models have also been subjected to testing in our study. The outcomes of our experiments demonstrated improvement over the state-of-the-art benchmarks.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
