Glycation is a non-enzymatic post-translational modification which assigns sugar molecule and residues to a peptide.It is a clinically important attribute to numerous age-related,metabolic,and chronic diseases such as...Glycation is a non-enzymatic post-translational modification which assigns sugar molecule and residues to a peptide.It is a clinically important attribute to numerous age-related,metabolic,and chronic diseases such as diabetes,Alzheimer’s,renal failure,etc.Identification of a non-enzymatic reaction are quite challenging in research.Manual identification in labs is a very costly and timeconsuming process.In this research,we developed an accurate,valid,and a robust model named as Gly-LysPred to differentiate the glycated sites from non-glycated sites.Comprehensive techniques using position relative features are used for feature extraction.An algorithm named as a random forest with some preprocessing techniques and feature engineering techniques was developed to train a computational model.Various types of testing techniques such as self-consistency testing,jackknife testing,and cross-validation testing are used to evaluate the model.The overall model’s accuracy was accomplished through self-consistency,jackknife,and cross-validation testing 100%,99.92%,and 99.88%with MCC 1.00,0.99,and 0.997 respectively.In this regard,a user-friendly webserver is also urbanized to accumulate the whole procedure.These features vectorization methods suggest that they can play a critical role in other web servers which are developed to classify lysine glycation.展开更多
为解决交通道路小目标检测难度大、精度低,容易出现错检漏检的问题,提出一种基于YOLO v5(you only look once v5)算法的多尺度特征融合目标检测改进算法。首先,增加小目标检测头用于适应小目标尺寸,缓解漏检情况。然后,引入可变形卷积网...为解决交通道路小目标检测难度大、精度低,容易出现错检漏检的问题,提出一种基于YOLO v5(you only look once v5)算法的多尺度特征融合目标检测改进算法。首先,增加小目标检测头用于适应小目标尺寸,缓解漏检情况。然后,引入可变形卷积网络v2(deformable convolutional networks V2,DCN V2)提高模型对运动中小目标的学习能力;同时,增加上下文增强模块,提升对远距离小目标的识别能力。最后,在替换损失函数、提高边界框定位精度的同时,使用空间金字塔池化和上下文空间金字塔卷积分组模块,提高网络的感受野和特征表达能力。实验结果表明,所提算法在KITTI数据集小目标类别上平均识别精度达到了95.2%,相较于原始YOLO v5,算法总体平均识别精度提升了2.7%,对小目标的检测效果更佳,平均识别精度提升了3.1%,证明所提算法在道路小目标检测方面的有效性。展开更多
基金the Research Management Center,Xiamen University Malaysia under XMUM Research Program Cycle 4(Grant No.XMUMRF/2019-C4/IECE/0012).
文摘Glycation is a non-enzymatic post-translational modification which assigns sugar molecule and residues to a peptide.It is a clinically important attribute to numerous age-related,metabolic,and chronic diseases such as diabetes,Alzheimer’s,renal failure,etc.Identification of a non-enzymatic reaction are quite challenging in research.Manual identification in labs is a very costly and timeconsuming process.In this research,we developed an accurate,valid,and a robust model named as Gly-LysPred to differentiate the glycated sites from non-glycated sites.Comprehensive techniques using position relative features are used for feature extraction.An algorithm named as a random forest with some preprocessing techniques and feature engineering techniques was developed to train a computational model.Various types of testing techniques such as self-consistency testing,jackknife testing,and cross-validation testing are used to evaluate the model.The overall model’s accuracy was accomplished through self-consistency,jackknife,and cross-validation testing 100%,99.92%,and 99.88%with MCC 1.00,0.99,and 0.997 respectively.In this regard,a user-friendly webserver is also urbanized to accumulate the whole procedure.These features vectorization methods suggest that they can play a critical role in other web servers which are developed to classify lysine glycation.
