Case Characterization,Clinical Features and Risk Factors in Drug-Induced Liver Injury

Idiosyncratic drug-induced liver injury (DILI) caused by xenobiotics (drugs, herbals and dietary supplements) presents with a range of both phenotypes and severity, from acute hepatitis indistinguishable of viral hepatitis to autoimmune syndromes, steatosis or rare chronic vascular syndromes, and from asymptomatic liver test abnormalities to acute liver failure. DILI pathogenesis is complex, depending on the interaction of drug physicochemical properties and host factors. The awareness of risk factors for DILI is arising from the analysis of large databases of DILI cases included in Registries and Consortia networks around the world. These networks are also enabling in-depth phenotyping with the identification of predictors for severe outcome, including acute liver failure and mortality/liver transplantation. Genome wide association studies taking advantage of these large cohorts have identified several alleles from the major histocompatibility complex system indicating a fundamental role of the adaptive immune system in DILI pathogenesis. Correct case definition and characterization is crucial for appropriate phenotyping, which in turn will strengthen sample collection for genotypic and future biomarkers studies.     

Immune-Mediated Drug-Induced Liver Injury: Immunogenetics and Experimental Models

Drug-induced liver injury (DILI) is a challenging clinical event in medicine, particularly because of its ability to present with a variety of phenotypes including that of autoimmune hepatitis or other immune mediated liver injuries. Limited diagnostic and therapeutic tools are available, mostly because its pathogenesis has remained poorly understood for decades. The recent scientific and technological advancements in genomics and immunology are paving the way for a better understanding of the molecular aspects of DILI. This review provides an updated overview of the genetic predisposition and immunological mechanisms behind the pathogenesis of DILI and presents the state-of-the-art experimental models to study DILI at the pre-clinical level.     

The eTOX Data-Sharing Project to Advance in Silico Drug-Induced Toxicity Prediction

The high-quality in vivo preclinical safety data produced by the pharmaceutical industry during drug development, which follows numerous strict guidelines, are mostly not available in the public domain. These safety data are sometimes published as a condensed summary for the few compounds that reach the market, but the majority of studies are never made public and are often difficult to access in an automated way, even sometimes within the owning company itself. It is evident from many academic and industrial examples, that useful data mining and model development requires large and representative data sets and careful curation of the collected data. In 2010, under the auspices of the Innovative Medicines Initiative, the eTOX project started with the objective of extracting and sharing preclinical study data from paper or pdf archives of toxicology departments of the 13 participating pharmaceutical companies and using such data for establishing a detailed, well-curated database, which could then serve as source for read-across approaches (early assessment of the potential toxicity of a drug candidate by comparison of similar structure and/or effects) and training of predictive models. The paper describes the efforts undertaken to allow effective data sharing intellectual property (IP) protection and set up of adequate controlled vocabularies) and to establish the database (currently with over 4000 studies contributed by the pharma companies corresponding to more than 1400 compounds). In addition, the status of predictive models building and some specific features of the eTOX predictive system (eTOXsys) are presented as decision support knowledge-based tools for drug development process at an early stage.     

Challenges and Future of Drug-Induced Liver Injury Research—Laboratory Tests

Drug-induced liver injury (DILI) is a rare but potentially severe adverse drug event, which is also a major cause of study cessation and market withdrawal during drug development. Since no acknowledged diagnostic tests are available, DILI diagnosis poses a major challenge both in clinical practice as well as in pharmacovigilance. Differentiation from other liver diseases and the identification of the causative agent in the case of polymedication are the main issues that clinicians and drug developers face in this regard. Thus, efforts have been made to establish diagnostic testing methods and biomarkers in order to safely diagnose DILI and ensure a distinguishment from alternative liver pathologies. This review provides an overview of the diagnostic methods used in differential diagnosis, especially with regards to autoimmune hepatitis (AIH) and drug-induced autoimmune hepatitis (DI-AIH), in vitro causality methods using individual blood samples, biomarkers for diagnosis and severity prediction, as well as experimental predictive models utilized in pre-clinical settings during drug development regimes.     

基于随机森林和支持向量机的高性能混凝土抗渗性预测研究

为了实现对混凝土抗渗性快速而精确地预测,提出了一种基于随机森林(RF)和支持向量机(SVM)的RF-SVM预测模型。首先以氯离子渗透系数为抗渗性评价指标,基于原材料配比确定了混凝土抗渗性的初始指标体系,然后利用随机森林算法结合后向剔除法进行指标筛选,剔除了冗余指标,得到了用于支持向量机建模的最优指标集,最后在此基础上建立了基于支持向量机的混凝土抗渗性预测模型,并研发了RF-SVM算法。以东北某高速公路项目为背景进行应用分析,结果表明,所提出的RF-SVM模型能够有效筛除冗余因素,得到精度较高的预测结果,且预测结果满足工程实践的要求,能够为混凝土抗渗性预测提供一种快速有效的方法。     

A Fragrance Prediction Model for Molecules Using Rough Set-based Machine Learning

In this work, a novel machine learning based methodology was developed to predict fragrance from the molecular structure and the effect of the subjects attributes on odour perception. As fragrance is linked to the molecular structure and interactions, topological indices are used to develop a predictive model. Rough set-based machine learning is used to generate rule-based models that link the topology of fragrant molecules and dilution to their respective odour characteristics. The results show that the generated models are effective in determining the odour characteristic of molecules.     

果蝇优化SVM模型对有机化合物熔点的预测

为了有效解决支持向量机(SVM)模型在参数选择上的盲目性问题,进而提高该模型的学习性能和泛化能力,将果蝇优化算法(FOA)引入该领域,提出了一种基于果蝇优化的SVM方法。该方法首先运用果蝇优化优化算法选择全局最优的SVM惩罚因子和核函数参数,从而建立SVM分类模型,进而基于该模型对实际问题进行应用。将该模型应用于对有机化合物的熔点预测问题中,实验结果表明,基于果蝇优化的SVM模型效率高,实际应用效果好。     

基于支持向量机的氨含量预测模型

氨合成反应器出口氨含量与其影响因素间存在较强的非线性关系,为其建模,可预报氨含量,进而指导生产、优化反应器的操作.本文运用具有较强的非线性拟合能力和基于结构风险最小化原则的支持向量机,建立了氨含量的预测模型,验证表明,该模型具有较强的拟合和预测能力.     

C10Pred: A First Machine Learning Based Tool to Predict C10 Family Cysteine Peptidases Using Sequence-Derived Features

Streptococcus pyogenes, or group A Streptococcus (GAS), a gram-positive bacterium, is implicated in a wide range of clinical manifestations and life-threatening diseases. One of the key virulence factors of GAS is streptopain, a C10 family cysteine peptidase. Since its discovery, various homologs of streptopain have been reported from other bacterial species. With the increased affordability of sequencing, a significant increase in the number of potential C10 family-like sequences in the public databases is anticipated, posing a challenge in classifying such sequences. Sequence-similarity-based tools are the methods of choice to identify such streptopain-like sequences. However, these methods depend on some level of sequence similarity between the existing C10 family and the target sequences. Therefore, in this work, we propose a novel predictor, C10Pred, for the prediction of C10 peptidases using sequence-derived optimal features. C10Pred is a support vector machine (SVM) based model which is efficient in predicting C10 enzymes with an overall accuracy of 92.7% and Matthews’ correlation coefficient (MCC) value of 0.855 when tested on an independent dataset. We anticipate that C10Pred will serve as a handy tool to classify novel streptopain-like proteins belonging to the C10 family and offer essential information.     

Idiosyncratic Drug-Induced Liver Injury: Mechanistic and Clinical Challenges

Idiosyncratic drug-induced liver injury (IDILI) remains a significant problem for patients and drug development. The idiosyncratic nature of IDILI makes mechanistic studies difficult, and little is known of its pathogenesis for certain. Circumstantial evidence suggests that most, but not all, IDILI is caused by reactive metabolites of drugs that are bioactivated by cytochromes P450 and other enzymes in the liver. Additionally, there is overwhelming evidence that most IDILI is mediated by the adaptive immune system; one example being the association of IDILI caused by specific drugs with specific human leukocyte antigen (HLA) haplotypes, and this may in part explain the idiosyncratic nature of these reactions. The T cell receptor repertoire likely also contributes to the idiosyncratic nature. Although most of the liver injury is likely mediated by the adaptive immune system, specifically cytotoxic CD8+ T cells, adaptive immune activation first requires an innate immune response to activate antigen presenting cells and produce cytokines required for T cell proliferation. This innate response is likely caused by either a reactive metabolite or some form of cell stress that is clinically silent but not idiosyncratic. If this is true it would make it possible to study the early steps in the immune response that in some patients can lead to IDILI. Other hypotheses have been proposed, such as mitochondrial injury, inhibition of the bile salt export pump, unfolded protein response, and oxidative stress although, in most cases, it is likely that they are also involved in the initiation of an immune response rather than representing a completely separate mechanism. Using the clinical manifestations of liver injury from a number of examples of IDILI-associated drugs, this review aims to summarize and illustrate these mechanistic hypotheses.     

SVM算法预测发动机润滑油的高温清洁性

考察了发动机润滑油功能元素与其高温清净性能的相关性,用支持向量机算法和偏最小二乘算法定量预测了发动机润滑油高温清净性能。结果表明,利用发动机润滑油的功能元素能够预测其高温清净性。支持向量机法的预测结果要优于偏最小二乘法的结果。     

Accelerated Recursive Feature Elimination Based on Support Vector Machine for Key Variable Identification

Key variable identification for classifications is related to many trouble-shooting problems in process industries. Recursive feature elimination based on support vector machine （SVM-RFE） has been proposed recently in application for feature selection in cancer diagnosis. In this paper, SVM-RFE is used to the key variable selection in fault diagnosis, and an accelerated SVM-RFE procedure based on heuristic criterion is proposed. The data from Tennessee Eastman process （TEP） simulator is used to evaluate the effectiveness of the key variable selection using accelerated SVM-RFE （A-SVM-RFE）. A-SVM-RFE integrates computational rate and algorithm effectiveness into a consistent framework. It not only can correctly identify the key variables, but also has very good computational rate. In comparison with contribution charts combined with principal component aralysis （PCA） and other two SVM-RFE algorithms, A-SVM-RFE performs better. It is more fitting for industrial application.     

三元可燃性液体水溶液闪点的理论预测

为了预测三元互溶可燃性液体水溶液的闪点,分析确定了影响混合溶液闪点的主要因素,如沸点、相对分子质量、相对密度、饱和蒸汽压、表面张力等。将这些因素作为输入变量,应用支持向量机算法对混合溶液闪点与其对应理化参数之间的内在定量相关性进行了研究,建立了三元互溶可燃性液体水溶液闪点的理论预测模型。对预测模型进行了验证,讨论了模型的有效性和可靠性;解释了模型反映的机制,明确了混合溶液闪点的主要影响因素及其重要程度。     

Efficient Prediction of Progesterone Receptor Interactome Using a Support Vector Machine Model

Protein-protein interaction (PPI) is essential for almost all cellular processes and identification of PPI is a crucial task for biomedical researchers. So far, most computational studies of PPI are intended for pair-wise prediction. Theoretically, predicting protein partners for a single protein is likely a simpler problem. Given enough data for a particular protein, the results can be more accurate than general PPI predictors. In the present study, we assessed the potential of using the support vector machine (SVM) model with selected features centered on a particular protein for PPI prediction. As a proof-of-concept study, we applied this method to identify the interactome of progesterone receptor (PR), a protein which is essential for coordinating female reproduction in mammals by mediating the actions of ovarian progesterone. We achieved an accuracy of 91.9%, sensitivity of 92.8% and specificity of 91.2%. Our method is generally applicable to any other proteins and therefore may be of help in guiding biomedical experiments.     

iBitter-Fuse: A Novel Sequence-Based Bitter Peptide Predictor by Fusing Multi-View Features

Accurate identification of bitter peptides is of great importance for better understanding their biochemical and biophysical properties. To date, machine learning-based methods have become effective approaches for providing a good avenue for identifying potential bitter peptides from large-scale protein datasets. Although few machine learning-based predictors have been developed for identifying the bitterness of peptides, their prediction performances could be improved. In this study, we developed a new predictor (named iBitter-Fuse) for achieving more accurate identification of bitter peptides. In the proposed iBitter-Fuse, we have integrated a variety of feature encoding schemes for providing sufficient information from different aspects, namely consisting of compositional information and physicochemical properties. To enhance the predictive performance, the customized genetic algorithm utilizing self-assessment-report (GA-SAR) was employed for identifying informative features followed by inputting optimal ones into a support vector machine (SVM)-based classifier for developing the final model (iBitter-Fuse). Benchmarking experiments based on both 10-fold cross-validation and independent tests indicated that the iBitter-Fuse was able to achieve more accurate performance as compared to state-of-the-art methods. To facilitate the high-throughput identification of bitter peptides, the iBitter-Fuse web server was established and made freely available online. It is anticipated that the iBitter-Fuse will be a useful tool for aiding the discovery and de novo design of bitter peptides.     

基于最小二乘支持向量机的蜡沉积速率预测

考虑蜡沉积影响因素的复杂性和最小二乘支持向量机在小样本预测方面的优势,基于最小二乘支持向量机预测的原理,通过优化最小二乘支持向量机的参数,建立了蜡沉积速率的预测模型,并对蜡沉积速率进行了预测。结果表明：该方法在样本数量较小时仍具有较高的精度,蜡沉积速率的预测值和实验值的吻合程度较好;最小二乘支持向量机建模时可以得到直观的函数表达式,而神经网络方法却不能得到模型的显式表达式,因此该方法具有明显的优势;应用径向基核（RBF）作为核函数时,不同初值的正则化参数?和核函数宽度?对预测结果具有较大影响,使用时应合理选择。     

基于Hilbert-Huang变换和支持向量机的油水两相流流型识别

针对油水两相流的测量难题，利用文丘里管对水平管内油水两相流流型进行了研究。基于差压波动信号，提出了Hilbert-Huang变换与支持向量机相结合的流型识别方法。首先计算差压波动信号的均方根，并对其进行归一化处理后作为表征流型的特征向量之一；然后对差压信号进行Hilbert-Huang变换，利用经验模态分解后的多分辨率特征，提取第一层和第二层的能量比作为表征流型的另外两个特征向量；最后利用训练好的支持向量机进行流型识别，对分层流型及分散流型取得了较好的流型识别效果。如果将流型识别与推导得到的文丘里油水两相流流量测量模型相结合，可以较好地实现油水两相流的流量测量。     

基于SVM的高效煤粉锅炉爆燃保护策略研究

为了提高高效煤粉锅炉的燃烧稳定性,提出了一种基于支持向量机(SVM)的爆燃保护控制策略。提取锅炉关键参数构建特征向量,采用SVM对系统历史数据进行离线训练,应用径向基函数、网格搜索算法生成系统状态分类器,并引入氧含量因子校正训练模型。锅炉运行时,分类器通过在线数据预测系统预爆燃状态并控制PLC模块执行保护程序。测试结果表明,氧含量因子取0.4时,分类器的最高交叉验证匹配率大于97%,最高预测准确率大于95%,失配率小于10%。保护策略能够有效地识别锅炉预爆燃状态,同时在锅炉正常工作状态下保持低误判率,增加了系统运行的鲁棒性。     

PredNTS: Improved and Robust Prediction of Nitrotyrosine Sites by Integrating Multiple Sequence Features

Nitrotyrosine, which is generated by numerous reactive nitrogen species, is a type of protein post-translational modification. Identification of site-specific nitration modification on tyrosine is a prerequisite to understanding the molecular function of nitrated proteins. Thanks to the progress of machine learning, computational prediction can play a vital role before the biological experimentation. Herein, we developed a computational predictor PredNTS by integrating multiple sequence features including K-mer, composition of k-spaced amino acid pairs (CKSAAP), AAindex, and binary encoding schemes. The important features were selected by the recursive feature elimination approach using a random forest classifier. Finally, we linearly combined the successive random forest (RF) probability scores generated by the different, single encoding-employing RF models. The resultant PredNTS predictor achieved an area under a curve (AUC) of 0.910 using five-fold cross validation. It outperformed the existing predictors on a comprehensive and independent dataset. Furthermore, we investigated several machine learning algorithms to demonstrate the superiority of the employed RF algorithm. The PredNTS is a useful computational resource for the prediction of nitrotyrosine sites. The web-application with the curated datasets of the PredNTS is publicly available.