Identification of estrogen receptor α ligands with virtual screening techniques

Utilization of computer-aided molecular discovery methods in virtual screening (VS) is a cost-effective approach to identify novel bioactive small molecules. Unfortunately, no universal VS strategy can guarantee high hit rates for all biological targets, but each target requires distinct, fine-tuned solutions. Here, we have studied in retrospective manner the effectiveness and usefulness of common pharmacophore hypothesis, molecular docking and negative image-based screening as potential VS tools for a widely applied drug discovery target, estrogen receptor α (ERα). The comparison of the methods helps to demonstrate the differences in their ability to identify active molecules. For example, structure-based methods identified an already known active ligand from the widely-used bechmarking decoy molecule set. Although prospective VS against one commercially available database with around 100,000 drug-like molecules did not retrieve many testworthy hits, one novel hit molecule with pIC₅₀ value of 6.6, was identified. Furthermore, our small in-house compound collection of easy-to-synthesize molecules was virtually screened against ERα, yielding to five hit candidates, which were found to be active in vitro having pIC₅₀ values from 5.5 to 6.5.     

HYBP_PSSP: a hybrid back propagation method for predicting protein secondary structure

The prediction of secondary structure is an important topic in the field of bioinformatics, even if the methods have matured, and development of the algorithms is a far less active area than a decade ago. Accurate prediction is very useful to biologists in its own right, but it is worth pointing out that it is also an essential component of tertiary structure prediction, which in contrast is far from solved and continues to be a highly active area of research. In addition, sequence comparison methods have more recently incorporated local structure tracks. The extra information utilized by the new methods has led to considerable improvements in fold recognition and alignment accuracy. In this paper, a novel method for protein secondary structure prediction is presented. Using evolutionary information contained in amino acid’s physicochemical properties, position-specific scoring matrix generated by PSI-BLAST and HMMER3 profiles as input to hybrid back propagation system, secondary structure can be predicted at significantly increased accuracy. Based on knowledge discovery theory based on inner cognitive mechanism (KDTICM) theory, we have constructed a compound pyramid model approach, which is composed of four layers of the intelligent interface and integrated in several ways, such as hybrid back propagation method (HBP), modified knowledge discovery in databases (KDD*), hybrid SVM method (HSVM) and so on. Experiments on three standard datasets (RS126, CB513 and CASP8) show that CPM is capable of producing the higher Q ₃ and SOV scores than that achieved by existing widely used schemes such as PSIPRED, PHD, Predator, as well as previously developed prediction methods. On the RS126 and CB513 datasets, it achieves a Q ₃ and SOV99 score are considerably higher than the best reported scores, respectively. It is also tested on target proteins of critical assessment of protein structure prediction experiment (CASP8) and achieves better results than the traditional methods, including the popular PSIPRED method over overall prediction accuracy. Available: .     

Molecular modeling of sigma 1 receptor ligands: a model of binding conformational and electrostatic considerations

We have performed molecular modeling studies on several sigma 1 specific ligands, including PD144418, spipethiane, haloperidol, pentazocine, and others to develop a pharmacophore for sigma 1 receptor-ligand binding, under the assumption that all the compounds interact at the same receptor binding site. The modeling studies have investigated the conformational and electrostatic properties of the ligands. Superposition of active molecules gave the coordinates of the hypothetical 5-point sigma 1 pharmacophore, as follows: R1 (0.85, 7.26, 0.30); R2 (5.47, 2.40, -1.51); R3 (-2.57, 4.82, -7.10); N (-0.71, 3.29, -6.40); carbon centroid (3.16, 4.83, -0.60), where R1, R2 were constructed onto the aromatic ring of each compound to represent hydrophobic interactions with the receptor; and R3 represents a hydrogen bond between the nitrogen atom and the receptor. Additional analyses were used to describe secondary binding sites to electronegative groups such as oxygen or sulfur atom. Those coordinates are (2.34, 5.08, -4.18). The model was verified by fitting other sigma 1 receptor ligands. This model may be used to search conformational databases for other possibly active ligands. In conjunction with rational drug design techniques the model may be useful in design and synthesis of novel sigma 1 ligands of high selectivity and potency. Calculations were performed using Sybyl 6.5.     

Does segmentation always improve model performance in credit scoring?

Credit scoring allows for the credit risk assessment of bank customers. A single scoring model (scorecard) can be developed for the entire customer population, e.g. using logistic regression. However, it is often expected that segmentation, i.e. dividing the population into several groups and building separate scorecards for them, will improve the model performance. The most common statistical methods for segmentation are the two-step approaches, where logistic regression follows Classification and Regression Trees (CART) or Chi-squared Automatic Interaction Detection (CHAID) trees etc. In this research, the two-step approaches are applied as well as a new, simultaneous method, in which both segmentation and scorecards are optimised at the same time: Logistic Trees with Unbiased Selection (LOTUS). For reference purposes, a single-scorecard model is used. The above-mentioned methods are applied to the data provided by two of the major UK banks and one of the European credit bureaus. The model performance measures are then compared to examine whether there is improvement due to the segmentation methods used. It is found that segmentation does not always improve model performance in credit scoring: for none of the analysed real-world datasets, the multi-scorecard models perform considerably better than the single-scorecard ones. Moreover, in this application, there is no difference in performance between the two-step and simultaneous approaches.     

PKRank: a novel learning-to-rank method for ligand-based virtual screening using pairwise kernel and RankSVM

The development of a new drug takes over 10 years and costs approximately US $2.6 billion. Virtual compound screening (VS) is a part of efforts to reduce this cost. Learning-to-rank is a machine learning technique in information retrieval that was recently introduced to VS. It works well because the application of VS requires the ranking of compounds. Moreover, learning-to-rank can treat multiple heterogeneous experimental data because it is trained using only the order of activity of compounds. In this study, we propose PKRank, a novel learning-to-rank method for ligand-based VS that uses a pairwise kernel and RankSVM. PKRank is a general case of the method proposed by Zhang et al. with the advantage of extensibility in terms of kernel selection. In comparisons of predictive accuracy, PKRank yielded a more accurate model than the previous method.     

Accurate 3D motion tracking by combining image alignment and feature matching

We presents a novel method to improve the accuracy of 3D motion tacking. In contrast to the state-of-the-art tracking approaches, where the 3D structure of target is commonly approximated by a CAD model, the proposed method establishes the target model by an online improved Structure-from-Motion technique. Furthermore, the tracking is implemented by three sequential trackers (feature-based tracker, image-alignment-based tracker and Particle Filter), which continually refine the tracking results. This coarse-to-fine method increases the accuracy of tracking. Moreover, our approach uses keyframe strategy to prevent tracking drift, the new keyframe insertion is determined by a criterion which can ensure a correct update. Thorough evaluations are performed on two public databases, the Biwi Head Pose dataset and the UPNA Head Pose Database. Comparisons illustrate that the proposed method achieves better performance with respect to other state-of-the-art tracking approaches.

     

T-STAR: 一种基于关键词的关系数据库时态信息检索方法*

将时态信息融入到信息检索技术中可以有效提高检索效果,时态信息检索已有较多的研究,而现有数据库信息检索方法还缺乏对时态信息有效利用。针对这一研究问题,提出关系数据库上基于时态语义的关键词检索方法,引入时态信息构建时态数据图,设计时态相关性评分机制,在时态图搜索过程中引入时态语义约束,设计基于关键词的时态检索算法。实验验证了该方法可以有效提高数据库信息检索效果,而检索性能并没有降低。     

基于原型与正交投影学习的图像集分类算法

为了利用图像集中的集合信息来提高图像识别精度以及对图像变化的鲁棒性,从而大幅降低诸如姿态、光照、遮挡和未对齐等因素对识别精度的影响,提出了一种用于图像集分类的图像集原型与投影学习算法（LPSOP）。该算法针对每个图像集学习有代表性的点（原型）以及一个正交的全局投影矩阵,使得在目标子空间的每个图像集可以被最优地分类到同类的最近原型集中。用学习到的原型来代表该图像集,既能降低冗余图像干扰,又能减少存储和计算开销,学习到的投影矩阵则能够大幅提高分类精度与噪声鲁棒性。在UCSD/Honda、CMU MoBo和YouTube celebrities这三个数据集上的实验结果表明,LPSOP比目前流行的图像集分类算法具有更高的识别精度和更好的鲁棒性。     

Docking studies of matrix metalloproteinase inhibitors: zinc parameter optimization to improve the binding free energy prediction

Docking of metalloproteinase inhibitors remains a challenge due to the zinc multiple coordination geometries and the lack of appropriate force field parameters to model the metal/ligand interactions. In this study, we explore the docking accuracy and scoring reliability for the docking of matrix metalloproteinase (MMP) inhibitors using AutoDock 3.0. Potential problems associated with zinc ion were investigated by docking 16 matrix metalloproteinase ligands to their crystal structures. A good coordination between the zinc binding group (ZBG) and the zinc was shown to be a prerequisite for the ligand to fit the binding site. A simplex optimization of zinc parameters, including zinc radius, well depth, and zinc charges, was performed utilizing the 14 MMP complexes with good docking. The use of optimized zinc parameters (zinc radius: 0.87 A; well depth: 0.35 kcal/mol; and zinc charges: +0.95 e) shows improvement in both docking accuracy at the zinc binding site and the prediction of binding free energies. Although further improvement in the docking procedure, particularly the scoring function is needed, optimization of zinc parameters provides an efficient way to improve the performance of AutoDock as a drug discovery tool.     

An approach for automatic sleep stage scoring and apnea-hypopnea detection

In this article we present an application of data mining to the medical domain sleep research, an approach for automatic sleep stage scoring and apnea-hypopnea detection. By several combined techniques (Fourier and wavelet transform, derivative dynamic time warping, and waveform recognition), our approach extracts meaningful features (frequencies and special patterns like k-complexes and sleep spindles) from physiological recordings containing EEG, ECG, EOG and EMG data. Based on these pieces of information, an ensemble of decision trees is constructed using the principle of bagging, which classifies sleep epochs in their sleep stages according to the rules by Rechtschaffen and Kales and annotates occurrences of apnea-hypopnea (total or partial cessation of respiration). After that, casebased reasoning is applied in order to improve quality. We tested and evaluated our approach on several large public databases from PhysioBank, which showed an overall accuracy of 95.2% for sleep stage scoring and 94.5% for classifying minutes as apneic or non-apneic.     

Pharmacological relationships and ligand discovery of G protein-coupled receptors revealed by simultaneous ligand and receptor clustering

Conventional ligand and receptor similarity methods have been extensively used for exposing pharmacological relationships and drug lead discovery. They may in some cases neglect minor relationships useful for target hopping particularly against the remote family members. To complement the conventional methods for capturing these minor relationships, we developed a new method that uses a SLARC (Simultaneous Ligand And Receptor Clustering) 2D map to simultaneously characterize the ligand structural and receptor binding-site sequence relationships of a receptor family. The SLARC maps of the rhodopsin-like GPCR family comprehensively revealed scaffold hopping, target hopping, and multi-target relationships for the ligands of both homologous and remote family members. Their usefulness in new ligand discovery was validated by guiding the prospective discovery of novel indole piperazinylpyrimidine dual-targeting adenosine A2A receptor antagonist and dopamine D2 agonist compounds. The SLARC approach is useful for revealing pharmacological relationships and discovering new ligands at target family levels.     

Neighborhood rough set and SVM based hybrid credit scoring classifier

The credit scoring model development has become a very important issue, as the credit industry is highly competitive. Therefore, considerable credit scoring models have been widely studied in the areas of statistics to improve the accuracy of credit scoring during the past few years. This study constructs a hybrid SVM-based credit scoring models to evaluate the applicant’s credit score according to the applicant’s input features: (1) using neighborhood rough set to select input features; (2) using grid search to optimize RBF kernel parameters; (3) using the hybrid optimal input features and model parameters to solve the credit scoring problem with 10-fold cross validation; (4) comparing the accuracy of the proposed method with other methods. Experiment results demonstrate that the neighborhood rough set and SVM based hybrid classifier has the best credit scoring capability compared with other hybrid classifiers. It also outperforms linear discriminant analysis, logistic regression and neural networks.     

Detecting rapid eye movement sleep using a single EEG signal channel

Sleep stage scoring is generally determined in a polysomnographic (PSG) study where technologists use electroencephalogram (EEG), electromyogram (EMG), and electrooculogram (EOG) signals to determine the sleep stages. Such a process is time consuming and labor intensive. To reduce the workload and to improve the sleep stage scoring performance of sleep experts, this paper introduces an intelligent rapid eye movement (REM) sleep detection method that requires only a single EEG channel. The proposed approach distinguishes itself from previous automatic sleep staging methods by introducing two sets of auxiliary features to help resolve the difficulties caused by interpersonal EEG signal differences. In addition to adopting conventional time and frequency domain features, two empirical rules are introduced to enhance REM detection performance based on sleep being a continuous process. The approach was tested with 779,661 epochs obtained from 947 overnight PSG studies. The REM sleep detection results show a kappa coefficient at 0.752, an accuracy level of 0.930, a sensitivity score of 0.814, and a positive predictive value of 0.775. The results also show that the performance of the approach varies with the ratio of REM sleep and the severity of sleep apnea of the subjects. The experimental results also show that it is possible to improve the performance of an automatic sleep staging method by tailoring it to subgroups of persons that have similar sleep architecture and clinical characteristics.     

Variable selection for collaborative filtering with market basket data

The market basket data in the form of a binary user–item matrix or a binary item–user matrix can be modeled as a binary classification problem, which actually tackles collaborative filtering (CF) as well as target marketing. Effective variable selection (VS) can increase the prediction accuracy as well as identify important users or items in CF as well as target marketing. Therefore, we propose two new VS approaches: a Pearson correlation‐based approach and a forward random forests regression‐based approach, comparing the performance in a variety of experimental settings. The experimental results show that the proposed VS approaches outperform the conventional approaches in the examples. Furthermore, the experimental results are more reasonable and informative than the previous experimental results because the binary misclassification error and Top‐N accuracy for the user CF, the item CF, the user modeling, and the item modeling are all considered in this paper.     

基于边界矩阵低阶近似和近邻模型的协同过滤算法

为解决矩阵分解应用到协同过滤算法的局限性和准确率等问题,提出基于边界矩阵低阶近似（BMA）和近邻模型的协同过滤算法（BMAN-CF）来提高物品评分预测的准确率。首先,引入BMA的矩阵分解算法,挖掘子矩阵的隐含特征信息,提高近邻集合查找的准确率;然后,根据传统基于用户和基于物品的协同过滤算法分别预测出目标用户对目标物品的评分,利用平衡因子和控制因子动态平衡两个预测结果,得到目标用户对物品的评分;最后,利用MapReduce计算框架的特点,对数据进行分块,将该算法在Hadoop环境下并行化。实验结果表明,BMAN-CF比其他矩阵分解算法有更高的评分预测准确率,且加速比实验验证了该算法具有较好的可扩展性。     

New validation methods for improving standard and multi-parametric support vector regression training time

The selection of hyper-parameters in support vector regression algorithms (SVMr) is an essential process in the training of these learning machines. Unfortunately, there is not an exact method to obtain the optimal values of SVMr hyper-parameters. Therefore, it is necessary to use a search algorithm and sometimes a validation method in order to find the best combination of hyper-parameters. The problem is that the SVMr training time can be huge in large training databases if standard search algorithms and validation methods (such as grid search and K-fold cross validation), are used. In this paper we propose two novel validation methods which reduce the SVMr training time, maintaining the accuracy of the final machine. We show the good performance of both methods in the standard SVMr with 3 hyper-parameters (where the hyper-parameters search is usually carried out by means of a grid search) and also in the extension to multi-parametric kernels, where meta-heuristic approaches such as evolutionary algorithms must be used to look for the best set of SVMr hyper-parameters. In all cases the new validation methods have provided very good results in terms of training time, without affecting the final SVMr accuracy.     

Rough set and scatter search metaheuristic based feature selection for credit scoring

As the credit industry has been growing rapidly, credit scoring models have been widely used by the financial industry during this time to improve cash flow and credit collections. However, a large amount of redundant information and features are involved in the credit dataset, which leads to lower accuracy and higher complexity of the credit scoring model. So, effective feature selection methods are necessary for credit dataset with huge number of features. In this paper, a novel approach, called RSFS, to feature selection based on rough set and scatter search is proposed. In RSFS, conditional entropy is regarded as the heuristic to search the optimal solutions. Two credit datasets in UCI database are selected to demonstrate the competitive performance of RSFS consisted in three credit models including neural network model, J48 decision tree and Logistic regression. The experimental result shows that RSFS has a superior performance in saving the computational costs and improving classification accuracy compared with the base classification methods.     

Molecular docking and dynamic simulation evaluation of Rohinitib — Cantharidin based novel HSF1 inhibitors for cancer therapy

Recent developments in the target based cancer therapies have identified HSF1 as a novel non oncogenic drug target. The present study delineates the design and molecular docking evaluation of Rohinitib (RHT) — Cantharidin (CLA) based novel HSF1 inhibitors for target-based cancer therapy. Here, we exploited the pharmacophoric features of both the parent ligands for the design of novel hybrid HSF1 inhibitors. The RHT-CLA ligands were designed and characterized for ADME/Tox features, interaction with HSF1 DNA binding domain and their pharmacophoric features essential for interaction. From the results, amino acid residues Ala17, Phe61, His63, Asn65, Ser68, Arg71 and Gln72 were found crucial for HSF1 interaction with the Heat shock elements (HSE). The hybrid ligands had better affinity towards the HSF1 DNA binding domain, in comparison to RHT or CLA and interacted with most of the active site residues. Additionally, the HSF1-ligand complex had a reduced affinity towards HSE in comparison to native HSF1. Based on the results, ligand RC15 and RC17 were non carcinogenic, non mutagenic, completely biodegradable under aerobic conditions, had better affinity for HSF1 (1.132 and 1.129 folds increase respectively) and diminished the interaction of HSF1 with HSE (1.203 and 1.239 folds decrease respectively). The simulation analysis also suggested that the ligands formed a stable complex with HSF1, restraining the movement of active site residues. In conclusion, RHT-CLA hybrid ligands can be used as a potential inhibitor of HSF1 for non-oncogene target based cancer therapy.