Hongmei Lu

A sensitive NADH and ethanol biosensor based on graphene-Au nanorods nanocomposites.

In this paper, a simple strategy for the synthesis of graphene-Au nanorods hybrid nanosheets (GN-AuNRs) through electrostatic interaction has been demonstrated. Due to the synergistic effect between AuNRs and GN, the hybrid nanosheets exhibited excellent performance toward dihydronicotinamide adenine dinucleotide (NADH) oxidation, with a low detection limit of 6 µM. The linear GN-AuNRs also served as a biocompatible and electroactive matrix for enzyme assembly to facilitate the electron transfer between the enzyme and the electrode. Using alcohol dehydrogenase (ADH) as a model system, a simple and effective sensing platform was developed for ethanol assay. The response displayed a good linear range from 5 to 377 µM with detection limit 1.5 μM. Furthermore, the interference effects of redox active substances, such as uric acid, ascorbic acid and glucose for the proposed biosensor were negligible.

A strategy that iteratively retains informative variables for selecting optimal variable subset in multivariate calibration

Nowadays, with a high dimensionality of dataset, it faces a great challenge in the creation of effective methods which can select an optimal variables subset. In this study, a strategy that considers the possible interaction effect among variables through random combinations was proposed, called iteratively retaining informative variables (IRIV). Moreover, the variables are classified into four categories as strongly informative, weakly informative, uninformative and interfering variables. On this basis, IRIV retains both the strongly and weakly informative variables in every iterative round until no uninformative and interfering variables exist. Three datasets were employed to investigate the performance of IRIV coupled with partial least squares (PLS). The results show that IRIV is a good alternative for variable selection strategy when compared with three outstanding and frequently used variable selection methods such as genetic algorithm-PLS, Monte Carlo uninformative variable elimination by PLS (MC-UVE-PLS) and competitive adaptive reweighted sampling (CARS). The MATLAB source code of IRIV can be freely downloaded for academy research at the website: http://code.google.com/p/multivariate-calibration/downloads/list.

Multiscale peak alignment for chromatographic datasets

Chromatography has been extensively applied in many fields, such as metabolomics and quality control of herbal medicines. Preprocessing, especially peak alignment, is a time-consuming task prior to the extraction of useful information from the datasets by chemometrics and statistics. To accurately and rapidly align shift peaks among one-dimensional chromatograms, multiscale peak alignment (MSPA) is presented in this research. Peaks of each chromatogram were detected based on continuous wavelet transform (CWT) and aligned against a reference chromatogram from large to small scale gradually, and the aligning procedure is accelerated by fast Fourier transform cross correlation. The presented method was compared with two widely used alignment methods on chromatographic dataset, which demonstrates that MSPA can preserve the shapes of peaks and has an excellent speed during alignment. Furthermore, MSPA method is robust and not sensitive to noise and baseline. MSPA was implemented and is available at http://code.google.com/p/mspa.

Large-scale prediction of drug-target interactions using protein sequences and drug topological structures

The identification of interactions between drugs and target proteins plays a key role in the process of genomic drug discovery. It is both consuming and costly to determine drug-target interactions by experiments alone. Therefore, there is an urgent need to develop new in silico prediction approaches capable of identifying these potential drug-target interactions in a timely manner. In this article, we aim at extending current structure-activity relationship (SAR) methodology to fulfill such requirements. In some sense, a drug-target interaction can be regarded as an event or property triggered by many influence factors from drugs and target proteins. Thus, each interaction pair can be represented theoretically by using these factors which are based on the structural and physicochemical properties simultaneously from drugs and proteins. To realize this, drug molecules are encoded with MACCS substructure fingerings representing existence of certain functional groups or fragments; and proteins are encoded with some biochemical and physicochemical properties. Four classes of drug-target interaction networks in humans involving enzymes, ion channels, G-protein-coupled receptors (GPCRs) and nuclear receptors, are independently used for establishing predictive models with support vector machines (SVMs). The SVM models gave prediction accuracy of 90.31%, 88.91%, 84.68% and 83.74% for four datasets, respectively. In conclusion, the results demonstrate the ability of our proposed method to predict the drug-target interactions, and show a general compatibility between the new scheme and current SAR methodology. They open the way to a host of new investigations on the diversity analysis and prediction of drug-target interactions.

Exploring metabolic syndrome serum profiling based on gas chromatography mass spectrometry and random forest models

Metabolic syndrome (MetS) is a constellation of the most dangerous heart attack risk factors: diabetes and raised fasting plasma glucose, abdominal obesity, high cholesterol and high blood pressure. Analysis and representation of the variances of metabolic profiles is urgently needed for early diagnosis and treatment of MetS. In current study, we proposed a metabolomics approach for analyzing MetS based on GC-MS profiling and random forest models. The serum samples from healthy controls and MetS patients were characterized by GC-MS. Then, random forest (RF) models were used to visually discriminate the serum changes in MetS based on these GC-MS profiles. Simultaneously, some informative metabolites or potential biomarkers were successfully discovered by means of variable importance ranking in random forest models. The metabolites such as 2-hydroxybutyric acid, inositol and d-glucose, were defined as potential biomarkers to diagnose the MetS. These results obtained by proposed method showed that the combining GC-MS profiling with random forest models was a useful approach to analyze metabolites variances and further screen the potential biomarkers for MetS diagnosis.

In silico classification of human maximum recommended daily dose based on modified random forest and substructure fingerprint

A modified random forest (RF) algorithm, as a novel machine learning technique, was developed to estimate the maximum recommended daily dose (MRDD) of a large and diverse pharmaceutical dataset for phase I human trials using substructure fingerprint descriptors calculated from simple molecular structure alone. This type of novel molecular descriptors encodes molecular structure in a series of binary bits that represent the presence or absence of particular substructures in the molecule and thereby can accurately and directly depict a series of local information hidden in this molecule. Two model validation approaches, 5-fold cross-validation and an independent validation set, were used for assessing the prediction capability of our models. The results obtained in this study indicate that the modified RF gave prediction accuracy of 80.45%, sensitivity of 75.08%, specificity of 84.85% for 5-fold cross-validation, and prediction accuracy of 80.5%, sensitivity of 76.47%, specificity of 83.48% for independent validation set, respectively, which are as a whole better than those by the original RF. At the same time, the important substructure fingerprints, recognized by the RF technique, gave some insights into the structure features related to toxicity of pharmaceuticals. This could help provide intuitive understanding for medicinal chemists.

Deep-Learning-Based Drug–Target Interaction Prediction

Identifying interactions between known drugs and targets is a major challenge in drug repositioning. In silico prediction of drug-target interaction (DTI) can speed up the expensive and time-consuming experimental work by providing the most potent DTIs. In silico prediction of DTI can also provide insights about the potential drug-drug interaction and promote the exploration of drug side effects. Traditionally, the performance of DTI prediction depends heavily on the descriptors used to represent the drugs and the target proteins. In this paper, to accurately predict new DTIs between approved drugs and targets without separating the targets into different classes, we developed a deep-learning-based algorithmic framework named DeepDTIs. It first abstracts representations from raw input descriptors using unsupervised pretraining and then applies known label pairs of interaction to build a classification model. Compared with other methods, it is found that DeepDTIs reaches or outperforms other state-of-the-art methods. The DeepDTIs can be further used to predict whether a new drug targets to some existing targets or whether a new target interacts with some existing drugs.

A novel kernel Fisher discriminant analysis: constructing informative kernel by decision tree ensemble for metabolomics data analysis.

Large amounts of data from high-throughput metabolomics experiments become commonly more and more complex, which brings an enormous amount of challenges to existing statistical modeling. Thus there is a need to develop statistically efficient approach for mining the underlying metabolite information contained by metabolomics data under investigation. In the work, we developed a novel kernel Fisher discriminant analysis (KFDA) algorithm by constructing an informative kernel based on decision tree ensemble. The constructed kernel can effectively encode the similarities of metabolomics samples between informative metabolites/biomarkers in specific parts of the measurement space. Simultaneously, informative metabolites or potential biomarkers can be successfully discovered by variable importance ranking in the process of building kernel. Moreover, KFDA can also deal with nonlinear relationship in the metabolomics data by such a kernel to some extent. Finally, two real metabolomics datasets together with a simulated data were used to demonstrate the performance of the proposed approach through the comparison of different approaches.

Exploring metabolic syndrome serum free fatty acid profiles based on GC-SIM-MS combined with random forests and canonical correlation analysis.

Metabolic syndrome (MetS) is a cluster of metabolic abnormalities associated with an increased risk of developing cardiovascular diseases or type II diabetes. Till now, the etiology of MetS is complex and still unknown. Metabolic profiling is a powerful tool for exploring metabolic perturbations and potential biomarkers, thus may shed light on the pathophysiological mechanism of diseases. In this study, fatty acid profiling was employed to exploit the metabolic disturbances and discover potential biomarkers of MetS. Fatty acid profiles of serum samples from metabolic syndrome patients and healthy controls were first analyzed by gas chromatography-selected ion monitoring-mass spectrometry (GC-SIM-MS), a robust method for quantitation of fatty acids. Then, the supervised multivariate statistical method of random forests (RF) was used to establish a classification and prediction model for MetS, which could assist the diagnosis of MetS. Furthermore, canonical correlation analysis (CCA) was employed to investigate the relationships between free fatty acids (FFAs) and clinical parameters. As a result, several FFAs, including C16:1n-9c, C20:1n-9c and C22:4n-6c, were identified as potential biomarkers of MetS. The results also indicated that high density lipoprotein-cholesterol (HDL-C), triglycerides (TG) and fasting blood glucose (FBG) were the most important parameters which were closely correlated with FFAs disturbances of MetS, thus they should be paid more attention in clinical practice for monitoring FFAs disturbances of MetS than waist circumference (WC) and systolic blood pressure/diastolic blood pressure (SBP/DBP). The results have demonstrated that metabolic profiling by GC-SIM-MS combined with RF and CCA may be a useful tool for discovering the perturbations of serum FFAs and possible biomarkers for MetS.

A bootstrapping soft shrinkage approach for variable selection in chemical modeling.

In this study, a new variable selection method called bootstrapping soft shrinkage (BOSS) method is developed. It is derived from the idea of weighted bootstrap sampling (WBS) and model population analysis (MPA). The weights of variables are determined based on the absolute values of regression coefficients. WBS is applied according to the weights to generate sub-models and MPA is used to analyze the sub-models to update weights for variables. The optimization procedure follows the rule of soft shrinkage, in which less important variables are not eliminated directly but are assigned smaller weights. The algorithm runs iteratively and terminates until the number of variables reaches one. The optimal variable set with the lowest root mean squared error of cross-validation (RMSECV) is selected. The method was tested on three groups of near infrared (NIR) spectroscopic datasets, i.e. corn datasets, diesel fuels datasets and soy datasets. Three high performing variable selection methods, i.e. Monte Carlo uninformative variable elimination (MCUVE), competitive adaptive reweighted sampling (CARS) and genetic algorithm partial least squares (GA-PLS) are used for comparison. The results show that BOSS is promising with improved prediction performance. The Matlab codes for implementing BOSS are freely available on the website: http://www.mathworks.com/matlabcentral/fileexchange/52770-boss.