Roark Dennis E
Department of Computer Science and Mathematics, University of Sioux Falls, 1101 W. 22nd Street, Sioux Falls, SD 57105, USA.
Biophys Chem. 2004 Mar 1;108(1-3):121-6. doi: 10.1016/j.bpc.2003.10.014.
Biophysical chemistry experiments, such as sedimentation-equilibrium analyses, require computational techniques to reduce the effects of random errors of the measurement process. The existing approaches have primarily relied on assumption of polynomial models and least-squares approximation. Such models by constraining the data to remove random fluctuations may distort the data and cause loss of information. The better the removal of random errors the greater is the likely introduction of systematic errors through the constraining fit itself. An alternative technique, reverse smoothing, is suggested that makes use of a more model-free approach of exponential smoothing of the first derivative. Exponential smoothing approaches have been generally unsatisfactory because they introduce significant data lag. The approaches given here compensates for the lag defect and appears promising for the smoothing of many experimental data sequences, including the macromolecular concentration data generated by sedimentation-equilibria experiments. Test results on simulated sedimentation-equilibrium data indicate that a 4-fold reduction in error may be typical over standard analyses techniques.
生物物理化学实验，如沉降平衡分析，需要计算技术来减少测量过程中随机误差的影响。现有的方法主要依赖于多项式模型假设和最小二乘近似。这种通过约束数据以消除随机波动的模型可能会扭曲数据并导致信息丢失。随机误差去除得越好，通过约束拟合本身引入系统误差的可能性就越大。本文提出了一种替代技术——反向平滑，它采用了一种更无模型的一阶导数指数平滑方法。指数平滑方法通常并不令人满意，因为它们会引入显著的数据滞后。这里给出的方法弥补了滞后缺陷，对于平滑许多实验数据序列，包括沉降平衡实验产生的大分子浓度数据，似乎很有前景。对模拟沉降平衡数据的测试结果表明，与标准分析技术相比，误差可能会典型地降低四倍。
