灰色数列预测模型在去卵巢大鼠体重研究中的应用

目的 探讨去卵巢大鼠的体重变化规律。方法 运用灰色GM(1，1)模型，对去卵巢大鼠的体重变化进行分析和预测。结果 灰色GM(1，1)模型对各组大鼠体重变化的预测精度高，结果可靠。结论 去卵巢大鼠的体重变化可用灰色GM(1，1）模型进行预测。     

灰色数列预测模型在骨髓基质细胞体外培养中的应用

目的:探讨体外培养骨髓基质细胞(MSCs)传代时及其成骨诱导后碱性磷酸酶(ALP)活性的变化规律。方法:运用灰色GM(1,1)模型,对体外培养MSCs传代时及其成骨诱导后ALP活性进行分析和预测。结果:灰色GM(1,1)模型对体外培养MSCs传代时及其成骨诱导后ALP的活性变化的预测精度高,结果可靠。结论:体外培养的MSCs传代时及其成骨诱导后ALP的活性值的变化可用灰色GM(1,1)模型进行预测。     

狗皮膏中铅体外吸收数据的灰色GM(1,1)拟合研究

目的:对传统制剂狗皮膏中铅透皮吸收数据进行灰色GM(1,1)模型拟合并与相关模型进行比较。方法:采用Franz扩散池进行体外透皮实验,通过累加生成建立透皮吸收数据的灰色GM(1,1)模型。结果:以平均相对误差和灰色绝对关联度为评价标准进行拟合精度的比较,以预测值与实测值的相对误差作预测精度的比较。结论:灰色GM(1,1)模型的拟合精度较高,可进行短期预测。     

灰色系统GM(1,1)模型在隐性梅毒发病预测中的应用

目的:探讨应用灰色系统一阶一个变量的微分方程型模型(GM(1,1)模型)预测隐性梅毒发病率的可行性。方法:应用灰色GM(1,1)模型对广西南宁市2004～2010年隐性梅毒年发病率数据进行建模拟合,并进行外推预测。结果:建立的广西南宁市隐性梅毒年发病率GM(1,1)预测模型,拟合精度高(C≈0.311,P=1),可用于外推预测。结论:灰色系统一阶模型可以很好地模拟和预测隐性梅毒发病率在时间序列上的变化趋势,将其应用于隐性梅毒发病预测是可行的。     

应用灰色GM(1,1)模型预测乙肝发病率

目的:探讨灰色GM(1,1)模型在乙肝发病率预测中的应用,对比常规GM(1,1)模型与动态GM(1,1)模型的预测效果。方法:利用2000～2009年全国乙肝年发病率数据,建立GM(1,1)模型以及动态GM(1,1)模型,检验各模型的拟合效果。结果:全国乙肝年发病率GM(1,1)模型预测方程为^x(1)(t+1)=-876.554+941.464e(0.686981e-t),模型拟合效果好,平均相对误差为0.0577;5维动态GM(1,1)模型为^x(1)(t+1)=-7982.71+8074.13e(0.128060e-t),模型拟合效果更佳,平均相对误差仅为0.0114。结论:5维动态GM(1,1)模型预测乙肝的发病率比传统GM(1,1)模型更为准确。     

运用灰色系统GM(1,1)模型预测安徽省肺结核发病趋势

目的建立肺结核灰色预测模型GM(1,1),应用于安徽省肺结核发病趋势的预测。方法根据公共卫生科学数据中心中2005~2012年安徽省肺结核发病报告资料建立肺结核发病率灰色预测模型GM(1,1),同时采用该模型对安徽省2013~2015年的肺结核发病率进行预测分析。结果通过灰色预测模型GM(1,1)而建立一个Y(t)=-1433.7369e-0.0654(t-1)+1531.5997预测模型,利用建立的模型预测安徽省2013~2015年安徽省肺结核发病率分别为57.4372/10万、53.7996/10万和50.3923/10万。结论通过实践应用证实,GM(1,1)模型预测安徽省肺结核发病率符合目前发展趋势,结果具有较高的参考应用价值。     

基于灰色新陈代谢模型的南宁市AIDS发病长期预测研究

目的:探讨应用灰色系统新陈代谢模型预测AIDS(艾滋病)发病率的可行性。方法:应用灰色新陈代谢模型对广西南宁市2003～2008年、2004～2009年AIDS年发病率数据进行建模拟合,并进行外推预测研究。结果:建立的广西南宁市2003～2008年及2004～2009年AIDS年发病率GM(1,1)新陈代谢预测模型,拟合精度高(C≈0.1881,P=1;C≈0.0947,P=1),可用于外推预测。结论:灰色系统一阶新陈代谢GM(1,1)模型可以很好地模拟和预测AIDS发病率在时间序列上的变化趋势,将其应用于AIDS发病预测是可行的。     

用Matlab实现灰色数列模型GM(1,1)的预测

在 Matlab平台上实现了灰色数列模型 GM( 1,1)函数的编制 ,并通过此函数对广东医学院附属二院门诊进行了预测 ,为灰色数列模型 GM( 1,1)的应用提供了一种运算简便的方法     

基于灰色新息模型的南宁市梅毒发病中长期预测研究

目的:探讨应用灰色系统新息模型持续预测梅毒发病率的可行性。方法:应用灰色新息模型对广西南宁市2005～2009年、2005～2010年梅毒年发病率数据进行建模拟合,并进行外推预测研究。结果:建立的广西南宁市2005～2009年及2005～2010年梅毒年发病率GM(1,1)新息预测模型,拟合精度高(C≈0.3652,P=1;C≈0.2812,P=1),可用于外推预测。结论:灰色系统一阶新息GM(1,1)模型可以很好地模拟和预测梅毒发病率在时间序列上的变化趋势,将其应用于梅毒发病持续预测是可行的。     

某医院出院人数的GM(1,1)灰色预测及SPSS程序实现

目的:预测某医院2012年出院人数。方法:根据医院2002～2011年的出院人数,采用GM(1,1)灰色预测模型,通过SPSS编程实现对某医院出院人数的预测。结果:该医院2012年预测的出院人数为55952人。结论:通过SPSS程序可以方便地进行GM(1,1)灰色预测模型的运算,较准确地实现医院工作量的预测。     

Prediction of neutropenia

Newer treatment strategies for the management of febrile neutropenic patients are being developed. These include: (a) hospital based oral therapy; (b) early discharge after initial stabilization in-hospital, and (c) out-patient therapy. All strategies are likely to be more successful in patients with short-lived neutropenia (< or = 7 days) than in those with more prolonged neutropenia. Although risk-prediction rules and clinical criteria can help clinicians identify 'low-risk' neutropenic patients, the overall ability of clinicians to accurately predict the subsequent duration of neutropenia once a neutropenic patient becomes febrile needs to be improved upon considerably. One attempt to do so is the survey being conducted by the Infection Study Section of the Multinational Association of Supportive Care in Cancer (MASCC). Development of an accurate rule for the prediction of neutropenia will enable more appropriate, risk-based therapy to be administered to febrile neutropenic patients, and will represent a significant advance in the management of such patients.     

Prediction of 'drug-likeness'

Recent developments in combinatorial chemistry and high-throughput screening have dramatically increased the scale on which drug discovery programs are carried out. Along with these advances has come a need for automated methods of determining which compounds from a library should be synthesized and screened. These methods range from simple counting schemes to sophisticated machine learning techniques such as neural networks. While many of these methods have performed well in validation studies, the field is still in its formative stage. This paper reviews a number of computational techniques for identifying drug-like molecules and examines challenges facing the field.     

Prediction of drug toxicity

The use of computer-aided methods to predict the toxicity of drugs is described. These methods can assist in the identification of toxic compounds early in the drug development process. Thus, there is potential for these methods to be combined with combinatorial synthesis and library design. Quantitative structure-activity relationships allow for the prediction of individual endpoints, usually for restricted groups of compounds. Expert systems for toxicity prediction are based on a number of methodologies, each with its own strengths and weaknesses. The relative merit of each individual technique and methodology is described. However, more toxicity data are required, both to produce and to validate expert systems. Potential sources of new data include the use of high-throughput screening and microarrays for toxicology.     

Prediction of neuroleptic-induced dystonia

For patients receiving neuroleptics, age, sex, neuroleptic potency, and dose all influence the likelihood of a dystonic reaction. Little is known, however, of the relative importance of these factors or of the feasibility of predicting dystonia in individual patients. We reviewed 135 charts of psychotic inpatients to examine these factors and their usefulness in predicting dystonia. Age, sex, neuroleptic type, dose, and occurrence of dystonia were recorded for the first 4 days of drug treatment and were used to construct a linear discriminant function that classified the cases as to whether dystonia was expected. Internal cross-validation was performed, and the error rate of this classification procedure was calculated. Forty-nine (36%) of the patients had dystonia. A younger age was the most powerful predictor of dystonia. Male gender was second in predictive power with minor effects from neuroleptic dose and potency. The overall error rate (false-positive and false-negative errors combined) of the discriminant function was 30%. These results suggest the possibility of predicting dystonia in individual patients but should be regarded with caution since the predictive procedure has not been tested prospectively. If confirmed, these data may allow treatment strategies that protect patients from dystonia while sparing patients not at risk unnecessary treatment with antiparkinson agents.     

Prediction of T-cell epitope

The prevailing methods to predict T-cell epitopes are reviewed. Motif matching, matrix, support vector machine (SVM), and empirical scoring function methods are mainly reviewed; and the thermodynamic integration (TI) method using all-atom molecular dynamics (MD) simulation is mentioned briefly. The motif matching method appeared first and developed with the increased understanding of the characteristic structure of MHC-peptide complexes, that is, pockets aligned in the groove and corresponding residues fitting on them. This method is now becoming outdated due to the insufficiency and inaccuracy of information. The matrix method, the generalization of interaction between pockets of MHC and residues of bound peptide to all the positions in the groove, is the most prevalent one. Efficiency of calculation makes this method appropriate to scan for candidates of T-cell epitopes within whole expressed proteins in an organ or even in a body. A large amount of experimental binding data is necessary to determine a matrix. SVM is a relative of the artificial neural network, especially direct generalization of a linear Perceptron. By incorporating non-binder data and adopting encoding that reflects the physical properties of amino acids, its performance becomes quite high. Empirical scoring functions apparently seem to be founded on a physical basis. However, the estimates directly derived from the method using only structural data are far from practical use. Through regression with binding data of a series of ligands and receptors, this method predicts binding affinity with appropriate accuracy. The TI method using MD requires only structural data and a general atomic parameter, that is, force field, and hence theoretically most consistent; however, the extent of perturbation, inaccuracy of the force field, the necessity of an immense amount of calculations, and continued difficulty of sampling an adequate structure hamper the application of this method in practical use.