粗糙集理论研究C-10位取代青蒿素衍生物的构效关系

利用粗糙集（RS）理论研究了C-10位取代青蒿素衍生物的构效关系（SAR）．粗糙集理论是处理模糊、不确定知识的数学系统,具有很强的非线性,对生命作用过程中的非线性数据的挖掘,具有很广阔的应用前景．本文首先用量子化学方法得到C-10位取代青蒿素衍生物的量化参数,由量化参数和活性数据构成了粗糙集的信息表．通过粗糙集方法,得到属性集的核和约简,提取出相应的决策规则,分析药物的构效关系,得出了一系列有意义的结论．作为一种非线性系统,粗糙集方法能够提取出数据中特别的关系,与MLR,PCA和PLS等方法相比,具有非线性功能,与ANN等方法相比,其结果又具有明确的物理意义．本文的结果对氟喹诺酮药物作用过程和耐药机制研究及新药的开发均有一定的意义．     

欧盟化学品分类及标签法案即将出台

随着欧盟即将对欧盟的化学物质及制剂贸易商实施《化学品注册、评估及许可法规》（简称REACH法规）,有关化学品分类及标签方法的法规将会出台。新法规将与联合国的全球化学品分类及标签协调制度一致。欧委会已通过一项关于化学物质和混合剂分类、标签及包装的法规议案,并已纳入了联合国认可的分类准则及标签规则。     

一种模式分类降维策略及其在复杂化学信息处理中的应用

采用分类相关分析方法处理复杂化学信息的模式分类问题。从高维模式中提取的分类相关成分,它们相互独立,并集中了原有模式的分类信息。提出由相关量份额选择分类相关成分,计算简便,用以替代原模式,可以显著地降维,使问题简化,分类效果良好。该方法应用于天然留兰香油多类判别,结果良好。     

化学反应处理的计算模型

介绍了一种将同类反应上升为合成反应知识和在计算机上实现反合成分析的方法,反合成分析是合成设计中最关键的一步,在本工作中采用了基于谋略键寻找的合成设计方法。它有逻辑宜于在计算机上实现的优点。为了实现这个方法,我们首次提出了一种能中肯地描述合成反应的计算模型—反应知识的分类模型。这一模型由三条规则定义：规则A-反应类型;规则B-发生反应的外部条件;规则C-不适宜采用这个反应的情况;这种计算模型能够将海量反应数据中最重要最基本的信息提炼出来,转换成计算机能处理的知识。它也包含有反应适用范围的信息,从而提高了析分过程的外推能力。     

用Delta—Bar—Delta规则神经网络对肺癌患者的判别

采用Ｄｅｌｔａ规则和Ｄｅｌｔａ－Ｂａｒ－Ｄｅｌｔａ规则的神经网络，用于根据头发和血清样品中金属含量对正常人与癌症患者的分类判别研究，对有关参数影响作了讨论，结果表明，采用Ｄｅｌｔａ－Ｂａｒ－Ｄｅｌｔａ规则的神经网络训练速度明显快于Ｄｅｌｔａ规则的神经网络。     

软硬酸碱规则与体内微量元素

阐述了ＳＨＡＢ规则,分类,酸碱软硬度的定量标度和理论解释。根据ＳＨＡＢ规则把体内微量元素分为Ｌｅｗｉｓ硬酸,软酸与交界酸和硬碱,软碱与交界碱,体内不同体液和器官含有丰富的软硬配体,与各类软硬酸（金属离子）结合成不同稳定性的配合物,发挥其生物活性作用。     

二维相关荧光光谱技术

从发展历史、计算方程、一般规则和特有性质等方面系统地介绍了近年来在二维相关荧光光谱技术方面的方法探索和应用进展。以不同的外扰方式,如浓度、激发波长、猝灭以及其他如pH等分类,举例阐述了二维荧光相关光谱的可操作性及其应用,并与普通一维荧光光谱比较,说明了二维荧光相关光谱技术的优势。     

裂解气相色谱讲座第四讲在生物医学等方面的应用

PGC法除了在高分子领域中的应用外,另一个重要应用领域,就是生物医学方面。1963年Oyama首先提出用PGC法探索火星上的生命物质和分类微生物。1965年Garner、Reiner等应用该法鉴别分类微生物获得了良好的结果。之后随着方法的不断改进,应用上日益得到发展。在生物和微生物方面,从最初的细菌分类,逐步扩展到病毒、蛋     

基于色谱和光谱数据融合的不同植物源食用油判别方法与模型

利用气相色谱和近红外光谱技术对不同植物源的4种食用油(葵花籽油、大豆油、玉米油和花生油)进行表征分析,基于表征数据分别建立了偏最小二乘判别分析(PLS-DA)模型,并在此基础上探究了数据级数据融合方法,构建了基于色谱和光谱数据融合的不同植物源食用油判别方法与模型。主成分分析(PCA)结果显示,气相色谱判别分析主要是依据脂肪酸组成信息,近红外光谱主要是基于样本中含氢化学键的表征进行分类。数据融合模型的灵敏度和特异度均为1000,分类误差为0000,降低了交互验证的平均分类误差,模型具有良好的稳健性。与基于单一数据的模型结果相比,数据融合分析策略提高了模型的分类精度和鲁棒性。     

欧盟化学品分类标签议案通过 出口面临考验

继欧盟化学品新规REACH之后,我国产品出口又将面临新一波“绿色考验”。不久前,欧盟已通过一项关于化学物质和混合剂分类、标签及包装的法规议案,并已纳入了联合国认可的分类准则及标签规则。新法规对化学物质的分类及标签的准则或责任做出规定,填补了REACH法规具体化学物质分类及标签内容的缺失,并与其相辅相成,共同构筑欧盟抵御危险化学品的绿色壁垒。为此,输欧化学物质或混合剂制造商和贸易商须引起特别注意。新法令重点对化学品分类的方法、要求及时间限定等进行了解读。据悉,议案法规旨在为企业设立一个自行分类制度,要求输入物质或混合剂的贸易商必须按照以下方法进行分类:(1)识别及阐述其危害;(2)评估有关危害的资料;(3)将有关资料与法规议案所载的危害准则进行比较。标签方面,须提供供应商的名称、地址和电话,可以识别物质或混合剂的资料、危害标志、危害声明、提防声明,以及有关危害的补充资料。议案生效后,物质重新分类的限期为2010年11月30日,混合剂为2015年5月31日。现时有关分类、标签及包装的指令将于2015年6月1日撤销。欧委会希望法规议案可于今年底获欧洲议会和理事会最后通过,并会就议案的应用拟定指南。可见,欧盟次调整化...     

基于预处理的决策树在化学数据挖掘中的应用

化学数据挖掘可从海量数据中提取蕴含的知识,决策树方法是一种重要的挖掘工具。鉴于决策树在处理连续数据上的局限性,本研究提出先进行预处理,将连续属性离散化,通过特征选择删除其冗余量,以此为基础构建决策树。该方法可防止决策树模型“过细”,使之具有良好的预报性能。将此方法应用于两个化学样品分类实例,效果良好。与贝叶斯分析和单一的决策树方法相比,其预报正确率有显著提高,且表达形式直观明确,易于理解和分析,适用于化学分类知识模式的挖掘。     

Accurate and interpretable computational modeling of chemical mutagenicity

We describe a method for modeling chemical mutagenicity in terms of simple rules based on molecular features. A classification model was built using a rule-based ensemble method called RuleFit, developed by Friedman and Popescu. We show how performance compares favorably against literature methods. Performance was measured through the use of cross-validation and testing on external test sets. All data sets used are publicly available. The method automatically generated transparent rules in terms of molecular structure that agree well with known toxicology. While we have focused on chemical mutagenicity in demonstrating this method, we anticipate that it may be more generally useful in modeling other molecular properties such as other types of chemical toxicity.     

Study on the quantitative structureactivity relation-ship of C-10 substituted artemisinin (QHS)’s derivatives using rough set theory

The structure-activity relationship study of C-10 substituted artemisinin (QHS) derivatives that are used as antimalarial was performed with the RS (rough sets) method. An RS process is a concise nonlinear process, and it has broad application foreground in the data mining of nonlinear life courses. In this work, initially the parameters of C-10 substituted QHS’s derivatives were computed with the quantum chemistry method, and the information table was constructed from the parameters (condition attributes) and biological activity (decision attributes). Based on the analysis of rough set theory, the core and reduction of attributes sets were obtained. Then the decision rules were extracted and the struc-ture-activity relationship was analyzed. As a nonlinear system, RS theory can extract the special rela-tion in the database. It has the advantage of being nonlinear over multiple linear regression (MLR), principal component analysis (PCA), partial least square (PLS), etc., and the advantage of obtaining results with unambiguous physical meanings over artificial neuron networks (ANNs), etc. The result obtained in this study is instructive to the study of pharmacodynamics, resistance mechanism of QHS and development of QHS’s derivatives.     

基于正则化网络-遗传算法的属性筛选及其在化学模式识别中的应用

采用贝叶斯正则化方法训练，以得到推广性优良的神经网络，并提出启发性的遗传算法。通过灵敏度分析对正则化网络实施剪枝，从而在高维模式中筛选出能代表其分类特性的最小最优属性特征子集。此方法应用于高维留兰香模式的属性筛选与模式分类，效果良好，明显优于其它方法。     

RCARM: Reaction classification using automated reaction mapping

Detailed chemical kinetic modeling of gas‐phase reactions can result in automatically generated mechanisms that contain thousands of reactions. In this paper, we describe the development of a rule‐based expert system tool that organizes these reactions into classes such as hydrogen abstraction and beta scission. We have developed 29 simple classification rules, 20 complex (well‐skipping) classification rules, and four second‐stage classification rules. This greatly simplifies the task of the chemical kineticist who wishes to verify, analyze, and gain insights into the reactions comprising the mechanism. This system, which is based on the automated identification of the bonds that break and form in a chemical reaction (the reaction mapping problem), is used to classify reactions in three different mechanisms. © 2012 Wiley Periodicals, Inc. Int J Chem Kinet 45: 125–139, 2013     

The scaffold tree--visualization of the scaffold universe by hierarchical scaffold classification

A hierarchical classification of chemical scaffolds (molecular framework, which is obtained by pruning all terminal side chains) has been introduced. The molecular frameworks form the leaf nodes in the hierarchy trees. By an iterative removal of rings, scaffolds forming the higher levels in the hierarchy tree are obtained. Prioritization rules ensure that less characteristic, peripheral rings are removed first. All scaffolds in the hierarchy tree are well-defined chemical entities making the classification chemically intuitive. The classification is deterministic, data-set-independent, and scales linearly with the number of compounds included in the data set. The application of the classification is demonstrated on two data sets extracted from the PubChem database, namely, pyruvate kinase binders and a collection of pesticides. The examples shown demonstrate that the classification procedure handles robustly synthetic structures and natural products.     

Computational intelligence techniques in bioinformatics

Computational intelligence (CI) is a well-established paradigm with current systems having many of the characteristics of biological computers and capable of performing a variety of tasks that are difficult to do using conventional techniques. It is a methodology involving adaptive mechanisms and/or an ability to learn that facilitate intelligent behavior in complex and changing environments, such that the system is perceived to possess one or more attributes of reason, such as generalization, discovery, association and abstraction. The objective of this article is to present to the CI and bioinformatics research communities some of the state-of-the-art in CI applications to bioinformatics and motivate research in new trend-setting directions. In this article, we present an overview of the CI techniques in bioinformatics. We will show how CI techniques including neural networks, restricted Boltzmann machine, deep belief network, fuzzy logic, rough sets, evolutionary algorithms (EA), genetic algorithms (GA), swarm intelligence, artificial immune systems and support vector machines, could be successfully employed to tackle various problems such as gene expression clustering and classification, protein sequence classification, gene selection, DNA fragment assembly, multiple sequence alignment, and protein function prediction and its structure. We discuss some representative methods to provide inspiring examples to illustrate how CI can be utilized to address these problems and how bioinformatics data can be characterized by CI. Challenges to be addressed and future directions of research are also presented and an extensive bibliography is included.     

Don’t Overweight Weights: Evaluation of Weighting Strategies for Multi-Task Bioactivity Classification Models

Machine learning models predicting the bioactivity of chemical compounds belong nowadays to the standard tools of cheminformaticians and computational medicinal chemists. Multi-task and federated learning are promising machine learning approaches that allow privacy-preserving usage of large amounts of data from diverse sources, which is crucial for achieving good generalization and high-performance results. Using large, real world data sets from six pharmaceutical companies, here we investigate different strategies for averaging weighted task loss functions to train multi-task bioactivity classification models. The weighting strategies shall be suitable for federated learning and ensure that learning efforts are well distributed even if data are diverse. Comparing several approaches using weights that depend on the number of sub-tasks per assay, task size, and class balance, respectively, we find that a simple sub-task weighting approach leads to robust model performance for all investigated data sets and is especially suited for federated learning.     

Two-dimensional wavelet analysis based classification of gas chromatogram differential mobility spectrometry signals

This study introduces two-dimensional (2-D) wavelet analysis to the classification of gas chromatogram differential mobility spectrometry (GC/DMS) data which are composed of retention time, compensation voltage, and corresponding intensities. One reported method to process such large data sets is to convert 2-D signals to 1-D signals by summing intensities either across retention time or compensation voltage, but it can lose important signal information in one data dimension. A 2-D wavelet analysis approach keeps the 2-D structure of original signals, while significantly reducing data size. We applied this feature extraction method to 2-D GC/DMS signals measured from control and disordered fruit and then employed two typical classification algorithms to testify the effects of the resultant features on chemical pattern recognition. Yielding a 93.3% accuracy of separating data from control and disordered fruit samples, 2-D wavelet analysis not only proves its feasibility to extract feature from original 2-D signals but also shows its superiority over the conventional feature extraction methods including converting 2-D to 1-D and selecting distinguishable pixels from training set. Furthermore, this process does not require coupling with specific pattern recognition methods, which may help ensure wide applications of this method to 2-D spectrometry data.