The Prediction of Cardiovascular Disease Based on Trace Element Contents in Hair and a Classifier of Boosting Decision Stumps

The early discovery of cardiovascular disease (CVD) is crucial for performing successful treatments. This study aims at exploring the feasibility of Adaboost (ensemble from machining learning) using decision stumps as weak classifier, combined with trace element analysis of hair, for accurately predicting early CVD. A total of 124 hair samples composed of two groups of samples (one is healthy group from 100 healthy persons aged 24–72 while the other is patient group from 24 cardiovascular disease patients aged 36–81) were used. Nine kinds of trace elements, i.e., chromium (Cr), manganese (Mn), cadmium (Cd), copper (Cu), zinc (Zn), selenium (Se), iron (Fe), aluminum (Al), and nickel (Ni), were selected. In a preliminary analysis, no obvious linear correlations between elements can be observed and the concentration of Cr, Fe, Al, Cd, Ni, or Se for healthy group is higher than that for patient group while the opposite is true for Mn, Cu, or Zn, indicating that both low Se/Fe and high Mn/Cu can be identified as major risk factors. Based on the proposed approach, the final ensemble classifier, constructed on the training set and contained only four decision stumps, achieved an overall identification accuracy of 95.2%, a sensitivity of 100% and a specificity of 94% on the independent test set. The results suggested that integrating Adaboost and trace element analysis of hair sample can serve as a useful tool of diagnosing CVD in clinical practice.     

Classification Models for Detection of Lung Cancer Based on Nine Element Distribution of Urine Samples

The detection of lung cancer has a special value in the diagnosis of cancer diseases. Based on nine elemental concentrations (i.e., chromium, iron, manganese, aluminum, cadmium, copper, zinc, nickel, and selenium) in urine samples and an ensemble linear discriminant analysis (ELDA), a detection method for lung cancer has been developed. A dataset containing 30 healthy samples and 27 lung cancer samples is used for experiment. The whole dataset was first split into a training set with 29 samples and a test set with 28 samples. The prediction results from the ELDA classifier were compared with those from single Fisher’s discriminate analysis (FDA). On the test set, the ELDA classifier achieved better performance, that is, a sensitivity of 100%, a specificity of 86.7%, and an overall accuracy of 92.9%, while the FDA classifier had a sensitivity of 92.3%, a specificity of 93.3%, and an overall accuracy of 92.9%. The superiority of ELDA to FDA is ascribed to the fact that ELDA can model more nonlinear relationships through the cooperation of several single models, suggesting that ensemble modeling is more advisable in such a task.     

Screening of Prostate Cancer by Analyzing Trace Elements in Hair and Chemometrics

Prostate cancer is the most common non-cutaneous malignancy and second leading cause of cancer mortality in men. The principle goal of this study was explore the feasibility of applying boosting coupled with trace element analysis of hair, for accurately distinguishing prostate cancer from healthy person. A total of 113 subjects containing 55 healthy men and 58 prostate cancers were collected. Based on a special index of variable importance and a forward selection scheme, only nine elements (i.e., Zn, Cr, Mg, Ca, Al, P, Cd, Fe, and Mo) were picked out from 20 candidate elements for modeling the relationship. As a result, an ensemble classifier consisting of only eight decision stumps achieved an overall accuracy of 98.2%, a sensitivity of 100%, and a specificity of 96.4% on the independent test set while all subjects on the training set are classified correctly. It seems that integrating boosting and element analysis of hair can serve as a valuable tool of diagnosing prostate cancer in practice.     

Ensemble Modeling Coupled with Six Element Concentrations in Human Blood for Cancer Diagnosis

Six important metal contents (i.e., zinc, barium, magnesium, calcium, copper, and selenium) in blood samples coupled with an ensemble classification algorithm have been used for the classification of normal people and cancer patients. A dataset containing 42 healthy samples and 32 cancer samples was used for experiment. The prediction results from this method outperformed those from the newly developed support vector machine, i.e., a sensitivity of 100%, a specificity of 95.2%, and an overall accuracy of 98.6%. It seems that ELDA coupled with blood element analysis can serve as a valuable tool for diagnosing cancer in clinical practice.     

Zinc, Copper, Iron, and Chromium Concentrations in Young Patients with Type 2 Diabetes Mellitus

Homeostasis of trace elements can be disrupted by diabetes mellitus. On the other hand, disturbance in trace element status in diabetes mellitus may contribute to the insulin resistance and development of diabetic complications. The aim of present study was to compare the concentration of essential trace elements, zinc, copper, iron, and chromium in serum of patients who have type 2 diabetes mellitus (n = 20) with those of nondiabetic control subjects (n = 20). The serum concentrations of zinc, copper, iron, and chromium were measured by means of an atomic absorption spectrophotometer (Shimadzu AA 670, Kyoto, Japan) after acid digestion. The results of this study showed that the mean values of zinc, copper, and chromium were significantly lower in the serum of patients with diabetes as compared to the control subjects (P < 0.05). Our results show that deficiency of some essential trace elements may play a role in the development of diabetes mellitus.     

Chromium homeostasis in patients with type II (NIDDM) diabetes.

The purpose of this study was to assess chromium handling in non-insulin dependent diabetic patients (NIDDM) compared to healthy volunteers. Chromium handling was evaluated using fasting blood and second morning void urine samples from 93 NIDDM patients and 33 healthy volunteers. Significant differences in chromium homeostasis were seen between patients and controls. NIDDM patients had mean levels of plasma chromium around 33% lower and urine values almost 100% higher than those found in health. Healthy volunteers showed a significant negative correlation between fasting levels of plasma chromium and insulin. This was not evident in NIDDM patients. In the early years of onset of NIDDM, plasma chromium values were inversely correlated with plasma glucose. This was lost in patients with diabetes of more than 2 years duration. We suggest large losses of chromium over many years may exacerbate an already compromised chromium status in NIDDM patients and might contribute to the developing insulin resistance seen in patients with type 2 diabetes.     

MOWGLI: prediction of protein–MannOse interacting residues With ensemble classifiers usinG evoLutionary Information

Proteins interact with carbohydrates to perform various cellular interactions. Of the many carbohydrate ligands that proteins bind with, mannose constitute an important class, playing important roles in host defense mechanisms. Accurate identification of mannose-interacting residues (MIR) may provide important clues to decipher the underlying mechanisms of protein–mannose interactions during infections. This study proposes an approach using an ensemble of base classifiers for prediction of MIR using their evolutionary information in the form of position-specific scoring matrix. The base classifiers are random forests trained by different subsets of training data set Dset128 using 10-fold cross-validation. The optimized ensemble of base classifiers, MOWGLI, is then used to predict MIR on protein chains of the test data set Dtestset29 which showed a promising performance with 92.0% accurate prediction. An overall improvement of 26.6% in precision was observed upon comparison with the state-of-art. It is hoped that this approach, yielding enhanced predictions, could be eventually used for applications in drug design and vaccine development.     

Overlapped Partitioning for Ensemble Classifiers of P300-Based Brain-Computer Interfaces

A P300-based brain-computer interface (BCI) enables a wide range of people to control devices that improve their quality of life. Ensemble classifiers with naive partitioning were recently applied to the P300-based BCI and these classification performances were assessed. However, they were usually trained on a large amount of training data (e.g., 15300). In this study, we evaluated ensemble linear discriminant analysis (LDA) classifiers with a newly proposed overlapped partitioning method using 900 training data. In addition, the classification performances of the ensemble classifier with naive partitioning and a single LDA classifier were compared. One of three conditions for dimension reduction was applied: the stepwise method, principal component analysis (PCA), or none. The results show that an ensemble stepwise LDA (SWLDA) classifier with overlapped partitioning achieved a better performance than the commonly used single SWLDA classifier and an ensemble SWLDA classifier with naive partitioning. This result implies that the performance of the SWLDA is improved by overlapped partitioning and the ensemble classifier with overlapped partitioning requires less training data than that with naive partitioning. This study contributes towards reducing the required amount of training data and achieving better classification performance.     

A novel multistage deep belief network based extreme learning machine ensemble learning paradigm for credit risk assessment

To achieve high assessment accuracy for credit risk, a novel multistage deep belief network (DBN) based extreme learning machine (ELM) ensemble learning methodology is proposed. In the proposed methodology, three main stages, i.e., training subsets generation, individual classifiers training and final ensemble output, are involved. In the first stage, bagging sampling algorithm is applied to generate different training subsets for guaranteeing enough training data. Second, the ELM, an effective AI forecasting tool with the unique merits of time-saving and high accuracy, is utilized as the individual classifier, and diverse ensemble members can be accordingly formulated with different subsets and different initial conditions. In the final stage, the individual results are fused into final classification output via the DBN model with sufficient hidden layers, which can effectively capture the valuable information hidden in ensemble members. For illustration and verification, the experimental study on one publicly available credit risk dataset is conducted, and the results show the superiority of the proposed multistage DBN-based ELM ensemble learning paradigm in terms of high classification accuracy.     

Modeling the Relationship Between Cervical Cancer Mortality and Trace Elements Based on Genetic Algorithm–Partial Least Squares and Support Vector Machines

The relationship between the mortality of cervical cancer and soil trace elements of 23 regions of China was investigated. A total of 25 elements (i.e., Na, K, Mg, Ca, Sr, Hg, Pb, B, Tm, Th, U, Sn, Hf, Bi, Ta, Te, Mo, Br, I, As, Cr, Cu, Fe, Zn, and Se) were considered. First, 23 samples were split into the training set with 12 samples and the test set with 11 samples. Then, a combination strategy called genetic algorithm–partial least squares (GA–PLS) was used to pick out five important elements. i.e., Br, Ta, Pb, Cr, and As. Afterwards, the classic partial least squares (PLS) model and least square support vector machine (LSSVM) model were developed and compared. The results revealed that the SVM model significantly outperforms the PLS model, indicating that the combination of GA–PLS and LSSVM can serve as a potential tool for predicting the mortality of cancer based on trace elements.     

Mining Proteins with Non-Experimental Annotations Based on an Active Sample Selection Strategy for Predicting Protein Subcellular Localization

Subcellular localization of a protein is important to understand proteins’ functions and interactions. There are many techniques based on computational methods to predict protein subcellular locations, but it has been shown that many prediction tasks have a training data shortage problem. This paper introduces a new method to mine proteins with non-experimental annotations, which are labeled by non-experimental evidences of protein databases to overcome the training data shortage problem. A novel active sample selection strategy is designed, taking advantage of active learning technology, to actively find useful samples from the entire data pool of candidate proteins with non-experimental annotations. This approach can adequately estimate the “value” of each sample, automatically select the most valuable samples and add them into the original training set, to help to retrain the classifiers. Numerical experiments with for four popular multi-label classifiers on three benchmark datasets show that the proposed method can effectively select the valuable samples to supplement the original training set and significantly improve the performances of predicting classifiers.     

Ensembles of neural networks based on the alteration of input feature values

An ensemble performs well when the component classifiers are diverse yet accurate, so that the failure of one is compensated for by others. A number of methods have been investigated for constructing ensemble in which some of them train classifiers with the generated patterns. This study investigates a new technique of training pattern generation. The method alters input feature values of some patterns using the values of other patterns to generate different patterns for different classifiers. The effectiveness of neural network ensemble based on the proposed technique was evaluated using a suite of 25 benchmark classification problems, and was found to achieve performance better than or competitive with related conventional methods. Experimental investigation of different input values alteration techniques finds that alteration with pattern values in the same class is better for generalization, although other alteration techniques may offer more diversity.     

Ensemble Positive Unlabeled Learning for Disease Gene Identification

An increasing number of genes have been experimentally confirmed in recent years as causative genes to various human diseases. The newly available knowledge can be exploited by machine learning methods to discover additional unknown genes that are likely to be associated with diseases. In particular, positive unlabeled learning (PU learning) methods, which require only a positive training set P (confirmed disease genes) and an unlabeled set U (the unknown candidate genes) instead of a negative training set N, have been shown to be effective in uncovering new disease genes in the current scenario. Using only a single source of data for prediction can be susceptible to bias due to incompleteness and noise in the genomic data and a single machine learning predictor prone to bias caused by inherent limitations of individual methods. In this paper, we propose an effective PU learning framework that integrates multiple biological data sources and an ensemble of powerful machine learning classifiers for disease gene identification. Our proposed method integrates data from multiple biological sources for training PU learning classifiers. A novel ensemble-based PU learning method EPU is then used to integrate multiple PU learning classifiers to achieve accurate and robust disease gene predictions. Our evaluation experiments across six disease groups showed that EPU achieved significantly better results compared with various state-of-the-art prediction methods as well as ensemble learning classifiers. Through integrating multiple biological data sources for training and the outputs of an ensemble of PU learning classifiers for prediction, we are able to minimize the potential bias and errors in individual data sources and machine learning algorithms to achieve more accurate and robust disease gene predictions. In the future, our EPU method provides an effective framework to integrate the additional biological and computational resources for better disease gene predictions.     

对治疗糖尿病的传统中药中生命元素含量测定初步研究

利用原子吸收分光光度法对几味治疗糖尿病的常用中药进行锌、镁、铁、铜、铬、硒元素的含量测定。结果表明,实验选取的中药中与糖尿病关系密切的生命元素含量较丰富,且所测元素含量与糖尿病患者体内其含量呈负相关性。为探讨中药的作用机理、中药配制工艺提供一定的信息和理论依据,对于糖尿病患者治疗具有参考价值。     

Influence of chosen elements on the dynamics of the cariogenic process

This prospective study comprised 140 natural crowns of the teeth extracted from 31 boys and 35 men, as well as 39 girls and 35 women. They were divided into two groups. Group I consisted of primary teeth and group II consisted of permanent teeth. In each group, two subgroups were distinguished: subgroup A containing teeth without caries and subgroup B comprising carietic teeth. Zinc, iron, copper, nickel, chromium, cobalt, lead, cadmium, selenium, and strontium were determined in the samples by using the total reflection X-ray fluorescence method. Significantly higher concentrations of zinc, iron, copper, nickel, selenium, and strontium were detected in the crowns of healthy primary and permanent teeth than in the crowns of the carietic primary and permanent teeth. The concentrations of chromium, cobalt, lead, and cadmium were significantly higher in primary and permanent teeth with caries than in the healthy ones. Judging from the obtained results, we think that lower concentrations of zinc, iron, copper, nickel, selenium, and strontium together with higher concentrations of chromium, cobalt, lead, and cadmium in the carietic primary and permanent teeth, in relation with the respective concentrations of those elements in healthy teeth, can be one of the caries risk factors.     

Heterogeneous Ensemble Combination Search Using Genetic Algorithm for Class Imbalanced Data Classification

Classification of datasets with imbalanced sample distributions has always been a challenge. In general, a popular approach for enhancing classification performance is the construction of an ensemble of classifiers. However, the performance of an ensemble is dependent on the choice of constituent base classifiers. Therefore, we propose a genetic algorithm-based search method for finding the optimum combination from a pool of base classifiers to form a heterogeneous ensemble. The algorithm, called GA-EoC, utilises 10 fold-cross validation on training data for evaluating the quality of each candidate ensembles. In order to combine the base classifiers decision into ensemble’s output, we used the simple and widely used majority voting approach. The proposed algorithm, along with the random sub-sampling approach to balance the class distribution, has been used for classifying class-imbalanced datasets. Additionally, if a feature set was not available, we used the (α, β) − k Feature Set method to select a better subset of features for classification. We have tested GA-EoC with three benchmarking datasets from the UCI-Machine Learning repository, one Alzheimer’s disease dataset and a subset of the PubFig database of Columbia University. In general, the performance of the proposed method on the chosen datasets is robust and better than that of the constituent base classifiers and many other well-known ensembles. Based on our empirical study we claim that a genetic algorithm is a superior and reliable approach to heterogeneous ensemble construction and we expect that the proposed GA-EoC would perform consistently in other cases.     

Impact of Trace Element Changes on Dehydroepiandrosterone Sulfate in Healthy and Diabetic States among Middle-Age and Elderly Egyptians

The aim of this study was to confirm if there is a link between the alteration in blood levels of trace elements (chromium, copper, lead, cadmium, and zinc) and dehydroepiandrosterone sulfate (DHEAS) in healthy and diabetic states. This study is the first study to test these parameters in Egyptians. The study included 150 subjects divided into the following four groups: healthy middle-aged, healthy elderly, middle-aged diabetics, and elderly diabetics. Our results revealed a statistically significant decrease in the level of DHEAS in the elderly compared to middle-aged healthy and diabetic groups (p < 0.05). There was a significant difference between the middle-aged groups with respect to zinc, copper, chromium, and cadmium levels. Zinc and copper were lower in the diabetic subjects while chromium and cadmium were higher in the same group in comparison to healthy subjects. In the elderly groups, there were significant increases in chromium and cadmium levels in diabetic subjects rather than healthy ones. There was a significant increase in the thiobarbituric acid reactive substance level in the elderly healthy and diabetic groups and a significant decrease in the glutathione level in the elderly groups. There was no correlation between the levels of trace elements and DHEAS or between the levels of DHEAS, oxidants, and antioxidants in all of the tested groups. In conclusion, only the DHEAS level was correlated with age. There was no difference between the diabetic and healthy groups with respect to the levels of trace elements, with the exception of chromium and cadmium, which suggests the effect of pollution on the pathogenesis of diabetes in Egyptians. No correlation existed between the levels of DHEAS and trace elements, oxidants, and antioxidants. Finally, we believe that there is a large regional variation in the levels of trace elements due to different environmental exposure and nutritional factors which are responsible for contradictory results regarding the pathogenesis of diseases related to alterations in the levels of trace elements.     

Concentrations of seven trace elements in different hematological matrices in patients with type 2 diabetes as compared to healthy controls

This study aimed to compare the trace element status of patients with type 2 diabetes (n=53) with those of nondiabetic healthy controls (n=50). The concentrations of seven trace elements were determined in the whole blood, blood plasma, erythrocytes, and lymphocytes of the study subjects. Vanadium and iron levels in lymphocytes were significantly higher in diabetic patients as compared to controls (p<0.05 for iron and p<0.01 for vanadium). In contrast, lower manganese (p<0.01) and selenium (p<0.01) concentrations were detected in lymphocytes derived from patients with type 2 diabetes versus healthy subjects. Furthermore, significantly lower chromium levels (p<0.05) were found in the plasma of diabetic individuals as compared to controls. Trace element concentrations were not dependent on the degree of glucose control as determined by correlation analysis between HBA_1c versus metal levels in the four blood fractions. In summary, this study primarily demonstrated that trace element levels in lymphocytes of patients with type 2 diabetes could deviate significantly from controls, whereas, in general, no considerable differences could be found when comparing the other fractions between both patient groups. Therefore, it seems reasonable to analyze metal levels in leukocytes to determine trace element status in patients with type 2 diabetes and perhaps in other diseases.     

Copper,Chromium, Manganese,Iron, Nickel,and Zinc Levels in Biological Samples of Diabetes Mellitus Patients

There is accumulating evidence that the metabolism of several trace elements is altered in diabetes mellitus and that these nutrients might have specific roles in the pathogenesis and progress of this disease. The aim of present study was to compare the level of essential trace elements, chromium (Cr), copper (Cu), iron (Fe), manganese (Mn), nickel (Ni), and zinc (Zn) in biological samples (whole blood, urine, and scalp hair) of patients who have diabetes mellitus type 2 (n = 257), with those of nondiabetic control subjects (n = 166), age ranged (45–75) of both genders. The element concentrations were measured by means of an atomic absorption spectrophotometer after microwave-induced acid digestion. The validity and accuracy was checked by conventional wet-acid-digestion method and using certified reference materials. The overall recoveries of all elements were found in the range of (97.60–99.49%) of certified values. The results of this study showed that the mean values of Zn, Mn, and Cr were significantly reduced in blood and scalp-hair samples of diabetic patients as compared to control subjects of both genders (p < 0.001). The urinary levels of these elements were found to be higher in the diabetic patients than in the age-matched healthy controls. In contrast, high mean values of Cu and Fe were detected in scalp hair and blood from patients versus the nondiabetic subjects, but the differences found in blood samples was not significant (p < 0.05). These results are consistent with those obtained in other studies, confirming that deficiency and efficiency of some essential trace metals may play a role in the development of diabetes mellitus.     

Using ensemble classifier to identify membrane protein types

Predicting membrane protein type is both an important and challenging topic in current molecular and cellular biology. This is because knowledge of membrane protein type often provides useful clues for determining, or sheds light upon, the function of an uncharacterized membrane protein. With the explosion of newly-found protein sequences in the post-genomic era, it is in a great demand to develop a computational method for fast and reliably identifying the types of membrane proteins according to their primary sequences. In this paper, a novel classifier, the so-called "ensemble classifier", was introduced. It is formed by fusing a set of nearest neighbor (NN) classifiers, each of which is defined in a different pseudo amino acid composition space. The type for a query protein is determined by the outcome of voting among these constituent individual classifiers. It was demonstrated through the self-consistency test, jackknife test, and independent dataset test that the ensemble classifier outperformed other existing classifiers widely used in biological literatures. It is anticipated that the idea of ensemble classifier can also be used to improve the prediction quality in classifying other attributes of proteins according to their sequences.