Argumentation Based Joint Learning: A Novel Ensemble Learning Approach

Recently, ensemble learning methods have been widely used to improve classification performance in machine learning. In this paper, we present a novel ensemble learning method: argumentation based multi-agent joint learning (AMAJL), which integrates ideas from multi-agent argumentation, ensemble learning, and association rule mining. In AMAJL, argumentation technology is introduced as an ensemble strategy to integrate multiple base classifiers and generate a high performance ensemble classifier. We design an argumentation framework named Arena as a communication platform for knowledge integration. Through argumentation based joint learning, high quality individual knowledge can be extracted, and thus a refined global knowledge base can be generated and used independently for classification. We perform numerous experiments on multiple public datasets using AMAJL and other benchmark methods. The results demonstrate that our method can effectively extract high quality knowledge for ensemble classifier and improve the performance of classification.     

A Tetra-Orthogonal Strategy for the Efficient Synthesis of Scaffolds Based on Cyclic Peptides

We have developed a straightforward and robust strategy for synthesizing a family of cyclic peptide scaffolds for the presentation of defined moieties in a wide range of orientations. Specifically we are exploring quinoxaline as the moiety, as a potential nucleic acid binding motif. The method requires the use of four degrees of orthogonality, which in turn allow the extension of the main chain, incorporation of the target side chains, on-resin cyclization, and the revelation of an amino group upon cleavage to increase solubility. We show that related approaches fail for a range of reasons, including the failure of cyclization. Following the optimization of the approach with a single cyclic peptide, we synthesized a family of all possible bis and tris quinoxaline adducts showing by ESI–MS that the desired full length cyclic product is produced in a majority of cases.     

基于Delphi法的中国生物制药行业技术发展趋势预测分析

运用传统的技术预测方法---Delphi法,通过组织“我国生物医药技术发展趋势预测”专家调研,对我国生物制药行业的技术发展方向进行了预测分析,目的是从技术发展的角度给出生物制药行业投资机会分析的一些可供借鉴的建议。调研结果显示,我国生物药品的研发应采取跟踪模仿与创新相结合的模式,进行模仿性、延伸性的新药研发,重点开发肿瘤药物、疫苗和诊断试剂等,重点开发针对恶性肿瘤、心脑血管疾病及感染性疾病(炎症)等病症的药物;目前影响我国生物药品技术发展的主要因素包括宏观政策、市场需求、资源特性、新药审批制度等;在未来生物药品新药研发过程中最有可能被采用的新技术、新方法包括细胞凋亡机制、计算机辅助设计及人类基因组研究等。     

An Efficient Gait-generating Method for Electrical Quadruped Robot Based on Humanoid Power Planning Approach

The research field of legged robots has always relied on the bionic robotic research,especially in locomotion regulating approaches,such as foot trajectory planning,body stability regulating and energy efficiency prompting.Minimizing energy consumption and keeping the stability of body are considered as two main characteristics of human walking.This work devotes to develop an energy-efficient gait control method for electrical quadruped robots with the inspiration of human walking pattern.Based on the mechanical power distribution trend,an efficient humanoid power redistribution approach is established for the electrical quadruped robot.Through studying the walking behavior acted by mankind,such as the foot trajectory and change of mechanical power,we believe that the proposed controller which includes the bionic foot movement trajectory and humanoid power redistribution method can be implemented on the electrical quadruped robot prototype.The stability and energy efficiency of the proposed controller are tested by the simulation and the single-leg prototype experi-ment.The results verify that the humanoid power planning approach can improve the energy efficiency of the electrical quadruped robots.     

A Novel Approach for Lie Detection Based on F-Score and Extreme Learning Machine

A new machine learning method referred to as F-score_ELM was proposed to classify the lying and truth-telling using the electroencephalogram (EEG) signals from 28 guilty and innocent subjects. Thirty-one features were extracted from the probe responses from these subjects. Then, a recently-developed classifier called extreme learning machine (ELM) was combined with F-score, a simple but effective feature selection method, to jointly optimize the number of the hidden nodes of ELM and the feature subset by a grid-searching training procedure. The method was compared to two classification models combining principal component analysis with back-propagation network and support vector machine classifiers. We thoroughly assessed the performance of these classification models including the training and testing time, sensitivity and specificity from the training and testing sets, as well as network size. The experimental results showed that the number of the hidden nodes can be effectively optimized by the proposed method. Also, F-score_ELM obtained the best classification accuracy and required the shortest training and testing time.     

A Novel Evaluation Strategy to Artificial Neural Network Model Based on Bionics

With the continuous deepening of Artificial Neural Network(ANN)research,ANN model structure and function are improv-ing towards diversification and intelligence...     

Reconstruction of Gene Regulatory Networks Based on Two-Stage Bayesian Network Structure Learning Algorithm

In the post-genomic biology era,the reconstruction of gene regulatory networks from microarray gene expression data isvery important to understand the underlying biological system,and it has been a challenging task in bioinformatics.TheBayesian network model has been used in reconstructing the gene regulatory network for its advantages,but how to determinethe network structure and parameters is still important to be explored.This paper proposes a two-stage structure learning algorithmwhich integrates immune evolution algorithm to build a Bayesian network.The new algorithm is evaluated with the use ofboth simulated and yeast cell cycle data.The experimental results indicate that the proposed algorithm can find many of theknown real regulatory relationships from literature and predict the others unknown with high validity and accuracy.     

设施黄瓜育成品种果实外观品质的遗传多样性分析

对我国新育成的不同生态类型的138个设施黄瓜品种的19个果实外观性状进行了调查分析,研究其遗传多样性。结果表明,参试品种质量性状的遗传多样性指数都小于数量性状,不同生态类型黄瓜品种果实外观性状的遗传多样性指数为华北型(1.33)>华南型(1.25)>欧洲温室型(1.0)。质量性状的遗传多样性指数为华南型(0.91)>华北型(0.65)>欧洲温室型(0.48);数量性状的遗传多样性指数为华北型(1.94)>华南型(1.56)>欧洲温室型(1.47)。华南型品种果实数量性状的变异系数均高于华北型和欧洲温室型品种。主坐标轴分析(PCO)将所有种质划分为3个区组,即1区为华北型种质优势区、2区为华北型华南型和欧洲温室型种质混合分布区、3区为华南型种质区。PCO结果表明,1区和2区发生了基因渗透。对交流区域中华北型品种自交分离,后代中出现的稀刺瘤和光皮种质的情况进一步验证了基因渗透的结果。参照育成品种的果实性状信息对黄瓜以后的育种工作提出了建议。     

Temporal Ordering of Cancer Microarray Data through a Reinforcement Learning Based Approach

Temporal modeling and analysis and more specifically, temporal ordering are very important problems within the fields of bioinformatics and computational biology, as the temporal analysis of the events characterizing a certain biological process could provide significant insights into its development and progression. Particularly, in the case of cancer, understanding the dynamics and the evolution of this disease could lead to better methods for prediction and treatment. In this paper we tackle, from a computational perspective, the temporal ordering problem, which refers to constructing a sorted collection of multi-dimensional biological data, collection that reflects an accurate temporal evolution of biological systems. We introduce a novel approach, based on reinforcement learning, more precisely, on Q-learning, for the biological temporal ordering problem. The experimental evaluation is performed using several DNA microarray data sets, two of which contain cancer gene expression data. The obtained solutions are correlated either to the given correct ordering (in the cases where this is provided for validation), or to the overall survival time of the patients (in the case of the cancer data sets), thus confirming a good performance of the proposed model and indicating the potential of our proposal.     

Learning Effective Connectivity Network Structure from fMRI Data Based on Artificial Immune Algorithm

Many approaches have been designed to extract brain effective connectivity from functional magnetic resonance imaging (fMRI) data. However, few of them can effectively identify the connectivity network structure due to different defects. In this paper, a new algorithm is developed to infer the effective connectivity between different brain regions by combining artificial immune algorithm (AIA) with the Bayes net method, named as AIAEC. In the proposed algorithm, a brain effective connectivity network is mapped onto an antibody, and four immune operators are employed to perform the optimization process of antibodies, including clonal selection operator, crossover operator, mutation operator and suppression operator, and finally gets an antibody with the highest K2 score as the solution. AIAEC is then tested on Smith’s simulated datasets, and the effect of the different factors on AIAEC is evaluated, including the node number, session length, as well as the other potential confounding factors of the blood oxygen level dependent (BOLD) signal. It was revealed that, as contrast to other existing methods, AIAEC got the best performance on the majority of the datasets. It was also found that AIAEC could attain a relative better solution under the influence of many factors, although AIAEC was differently affected by the aforementioned factors. AIAEC is thus demonstrated to be an effective method for detecting the brain effective connectivity.     

Efficient and Robust Analysis of Biomacromolecular Flexibility Using Ensembles of Network Topologies Based on Fuzzy Noncovalent Constraints

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A Low Dimensional Approach on Network Characterization

In many applications, one may need to characterize a given network among a large set of base networks, and these networks are large in size and diverse in structure over the search space. In addition, the characterization algorithms are required to have low volatility and with a small circle of uncertainty. For large datasets, these algorithms are computationally intensive and inefficient. However, under the context of network mining, a major concern of some applications is speed. Hence, we are motivated to develop a fast characterization algorithm, which can be used to quickly construct a graph space for analysis purpose. Our approach is to transform a network characterization measure, commonly formulated based on similarity matrices, into simple vector form signatures. We shall show that the similarity matrix can be represented by a dyadic product of two N-dimensional signature vectors; thus the network alignment process, which is usually solved as an assignment problem, can be reduced into a simple alignment problem based on separate signature vectors.     

A Study on Seed Characters in Chinese Blyxa

The present paper reports seed characters of 14 populations in Chinese Blyxa, examined using light microscope and scanning electron microscope. The internal structures of seeds were observed under light microscope after being prepared by paraffin section. The results show that differences between populations of different species are more distinct than between those within a species. The 14 populations in Chinese Blyxa are grouped into 4 species, i.e. Blyxa echinosperma (G. B. clarke) Hook. f., B. auberti Rich., B. leiosper-ma Koid., B. japonica (Miq.) Maxim.     

An Efficient Approach for Differentiating Alzheimer's Disease from Normal Elderly Based on Multicenter MRI Using Gray-Level Invariant Features

Machine learning techniques, along with imaging markers extracted from structural magnetic resonance images, have been shown to increase the accuracy to differentiate patients with Alzheimer''s disease (AD) from normal elderly controls. Several forms of anatomical features, such as cortical volume, shape, and thickness, have demonstrated discriminative capability. These approaches rely on accurate non-linear image transformation, which could invite several nuisance factors, such as dependency on transformation parameters and the degree of anatomical abnormality, and an unpredictable influence of residual registration errors. In this study, we tested a simple method to extract disease-related anatomical features, which is suitable for initial stratification of the heterogeneous patient populations often encountered in clinical data. The method employed gray-level invariant features, which were extracted from linearly transformed images, to characterize AD-specific anatomical features. The intensity information from a disease-specific spatial masking, which was linearly registered to each patient, was used to capture the anatomical features. We implemented a two-step feature selection for anatomic recognition. First, a statistic-based feature selection was implemented to extract AD-related anatomical features while excluding non-significant features. Then, seven knowledge-based ROIs were used to capture the local discriminative powers of selected voxels within areas that were sensitive to AD or mild cognitive impairment (MCI). The discriminative capability of the proposed feature was measured by its performance in differentiating AD or MCI from normal elderly controls (NC) using a support vector machine. The statistic-based feature selection, together with the knowledge-based masks, provided a promising solution for capturing anatomical features of the brain efficiently. For the analysis of clinical populations, which are inherently heterogeneous, this approach could stratify the large amount of data rapidly and could be combined with more detailed subsequent analyses based on non-linear transformation.     

基于整合网络和联合策略预测心肌梗死的新致病基因

目的:基于整合网络和联合策略预测心肌梗死的新致病基因.方法:从系统生物学的角度,提出基于蛋白质亚细胞定位信息,构建区域化的蛋白质互作的整合网络;通过疾病风险基因与已知致病基因的功能一致性程度和互作相关性的强度联合筛选的新策略,预测心肌梗死的新致病基因.结果:预测出10个心肌梗死的新致病基因(CCL19、CCL25、COMP、CCL11、CCL7、F2、KLKB1、HTR6、ADRB1、BDKRB2),其中8个基因(CCL 19、CCL25、CCL11、CCL7、F2、KLKB1、ADRB1、BDKRB2)经文献证实与心肌梗死的发生发展有着密切的联系;另外2个基因(COMP、HTR6)尚需实验验证.结论:基于整合网络和联合策略预测出10个心肌梗死的新致病基因,此方法为探索复疾病的致病基因提供了新的思路,有助于阐明复杂疾病的致病机理.     

中国甜瓜种质资源形态性状遗传多样性分析

对我国各地257份代表性的甜瓜种质资源的20个形态性状进行调查分析,研究其遗传多样性。结果表明,7个质量性状(果实形状、果皮底色、覆纹颜色、覆纹形状、果肉颜色、果肉质地和种子颜色)和6个数量性状(果实横径、果实纵径、单果鲜重、果肉厚度、可溶性固形物含量和种子千粒重)差异明显,其Shannon’s指数分别大于1.00和1.50。所有种质的平均遗传多样性指数为1.09,不同地区种质资源遗传多样性差异明显,多样性指数高低次序分别为：西北、华中、华东、华北、东北和华南。主座标标分析(PCO)将所有种质划分为3个区域,即厚皮种质优势区、厚皮和薄皮种质混合分布区、薄皮种质优势区,不同地区的种质在PCO图上的分布差异明显,西北地区的厚皮甜瓜种质和华中、华东地区的薄皮甜瓜种质广泛分布于3个区域中,其形态遗传多样性比其他地区的种质更加丰富,支持了新疆地区为厚皮甜瓜次级起源中心、黄淮及长江流域为薄皮甜瓜初级起源中心的观点。     

Cardiovascular Disorder Severity Detection Using Myocardial Anatomic Features Based Optimized Extreme Learning Machine Approach

ObjectivesThis study focuses on integration of anatomical left ventricle myocardium features and optimized extreme learning machine (ELM) for discrimination of subjects with normal, mild, moderate and severe abnormal ejection fraction (EF). The physiological alterations in myocardium have diagnostic relevance to the etiology of cardiovascular diseases (CVD) with reduced EF.Materials and MethodsThis assessment is carried out on cardiovascular magnetic resonance (CMR) images of 104 subjects available in Kaggle Second Annual Data Science Bowl. The Segment CMR framework is used to segment myocardium from cardiac MR images, and it is subdivided into 16 sectors. 86 clinically significant anatomical features are extracted and subjected to ELM framework. Regularization coefficient and hidden neurons influence the prediction accuracy of ELM. The optimal value for these parameters is achieved with the butterfly optimizer (BO). A comparative study of BOELM framework with different activation functions and feature set has been conducted.ResultsAmong the individual feature set, myocardial volume at ED gives a better classification accuracy of 83.3% compared to others. Further, the given BOELM framework is able to provide higher multi-class accuracy of 95.2% with the entire feature set than ELM. Better discrimination of healthy and moderate abnormal subjects is achieved than other sub groups.ConclusionThe combined anatomical sector wise myocardial features assisted BOELM is able to predict the severity levels of CVDs. Thus, this study supports the radiologists in the mass diagnosis of cardiac disorder.