Yongli Wang

Inference of microbial interactions from time series data using vector autoregression model

Microbial interaction, such as species competition and symbiotic relationships, plays important role to enable microorganisms to survive by establishing a homeostasis between microbial neighbors and local environments. Thanks to the recent accumulation of large-scale high-throughput sequencing data of complex microbial communities, there are increasing interests in identifying microbial interactions. Computational methods for microbial interactions inference are currently focused on the similarity among microbial individuals (i.e. cooccurrence and correlation patterns), however, less methods considered the dynamics of a single complex community over time. In this paper, we propose to use a multivariate statistical method - Multivariate Vector Autoregression (MVAR) to infer dynamic microbial interactions from the time series of human gut microbiomes. Specifically, we apply MVAR model on a time series data of human gut microbiomes which were treated with repeated antibiotics. The referred microbial interactions identify novel interactions which may provide a novel complementary to similarity or correlation-based methods.

Privacy management of patient physiological parameters

Abstract Patient Physiological Parameters (PPPs) seem to be the most extensively accessed and utilized Personal Health Information (PHI) in hospitals, and their utilization by the various medical entities for treatment and diagnosis creates a real threat to patient privacy. This study aims to investigate whether PPPs access in a hospital environment violates patient privacy. If so, to what extent can we manage patient privacy while accessing PPPs in this environment? We investigated this question by analyzing questionnaire-based data from two Asian countries: Group A (China) and Group B (Pakistan). For data collection, we targeted those medical entities which were directly dealing with PPPs in their routine tasks. Results suggest that patient type directly influences the collection of PPPs: Group A (one-time = 1.9, follow-up = 1.06) and Group B (one-time = 2.0 and follow-up = 1.9). Both groups agreed that patients have the right to control their own PPPs. In both, doctors are the most trusted entity: for Group A, the Pearson Chi-Square with one degree of freedom is 1.414, p = 0.234, whereas for Group B, the Pearson Chi-Square with three degrees of freedom is 4.511, p = 0.11. Most of the Group A entities (92%) are familiar with unauthorized access of PPPs, while in Group B the level was only 35%. In Group B, only 35% of entities stated the purpose, specification and use limitations of PPPs. Doctors in both groups showed a high utilization of PPPs read authorization rights. This empirical evidence about PPPs usage in both countries will benefit health technology and improve policy on patient privacy.

ALO-DM: A Smart Approach Based on Ant Lion Optimizer with Differential Mutation Operator in Big Data Analytics

The ant lion optimizer (ALO) is a novel swarm intelligence optimization algorithm, but its population diversity and convergence precision can be limited in some applications. In this paper, we proposed an approach based on ALO and differential mutation operator that called ALO-DM. In this method, differential mutation operator and greedy strategy enhance the diversity of the population. In addition, combining it with data mining algorithms can be useful and practical in big data analytics problems. The simulation results not only show that the ALO-DM is able to obtain accurate solution, but also demonstrate that it is feasible and effective.

Revisiting transductive support vector machines with margin distribution embedding

Transductive Support Vector Machine (TSVM) is one of the most successful classification methods for semi-supervised learning (SSL). One challenge of TSVMs is that the performance degeneration is caused by unlabeled examples that are obscure or misleading for the discovery of the underlying distribution. To address this problem, we disclose the underlying data distribution and describe the margin distribution of TSVMs as the first-order (margin mean) and second-order (margin variance) statistics of examples. Since the optimization problems of TSVMs are not convex, we utilize the concave-convex procedure and variation of stochastic variance reduced gradient methods to solve them. Particularly, we propose two specific algorithms to optimize the margin distribution of TSVM via maximizing the margin mean and minimizing the margin variance simultaneously, which the generalization ability is improved and being robust to the outliers and noise. In addition, we derive a bound on the expectation of error according to the leave-one-out cross-validation estimate, which is an unbiased estimate of the probability of test error. Finally, to validate the effectiveness of the proposed method, extensive experiments are conducted on diversity datasets. The experimental results demonstrate that the performance of proposed algorithms are superior to the existing TSVMs and other semi-supervised learning methods.

A survey of real-time approximate nearest neighbor query over streaming data for fog computing

Abstract Real-time approximate nearest neighbor (ANN) query over streaming data in fog computing environment is the fundamental problem of real-time analysis of big data. As the fog computing paradigm needs to provide real-time and low latency services, and traditional streaming data ANN query technology cannot be directly applied. Exploring the basic theory, querying framework and technology of real-time ANN query over streaming data for fog computing becomes one of the current research hotspots. This paper summarizes the related ANN query technology based on random hash, learning-to-hash and synopses, analyzes the problems and challenges of real-time ANN query in resource-limited fog computing environment, and finally discusses in detail the basic theory and method of the query, the dimension reduction and encoding method based on learning-to-hash, the generating synopses method for ANN query over streaming data from Internet of Thing, and the future related research directions of ANN query framework and others. Additionally, we propose a Dynamic Adaptive Quantization (DAQ) method for learning-to-hash. Experiments show that DAQ outperformed other quantization methods.

A systematic framework to discover pattern for web spam classification

Web spam is a big problem for search engine users in World Wide Web. They use deceptive techniques to achieve high rankings. Although many researchers have presented the different approach for classification and web spam detection still it is an open issue in computer science. Analyzing and evaluating these websites can be an effective step for discovering and categorizing the features of these websites. There are several methods and algorithms for detecting those websites, such as decision tree algorithm. In this paper, we present a systematic framework based on CHAID algorithm and a modified string matching algorithm (KMP) for extract features and analysis of these websites. We evaluated our model and other methods with a dataset of Alexa Top 500 Global Sites and Bing search engine results in 500 queries.

TMDFM: A data fusion model for combined detection of tumor markers

The field of biomarkers in cancer research has recently gained widespread interest, for its potential to improve diagnosis accuracy, prognosis, and make cancer treatments to be more personalized. However, the detection of multi-tumor markers method still has many problems, such as limited applicability, need to different model for different tumor markers, a simple series or parallel method cannot effectively take advantage of different tumor markers. This paper proposed a data fusion method for multi-tumor markers, which can be adapted to different scene. It can effectively use the different markers to give an adjuvant diagnosis. With the new markers continue to be found, we can provide guidance for the combined detection.

Manifold-constrained regularization for variable selection in envrionmental microbiomic data

Current data mining and statistical methods to extract patterns and relationships in microbiomic data are often based on several assumptions such as Euclidean, linear, continuous and metric space which may not be the true space of microbiomic data. For example, the microbial profiles (functional and taxonomic classifications) are often correlated in a hierarchical style. These assumptions prevent discovering the true relationships in microbiomic data analysis. Thus, it is urgent to develop new computational methods to overcome these assumptions and consider the microbiomic data properties in the analysis procedure. In this study, we will propose novel variable selection method based on manifold-constrained regularization (McRe). Considering the nonlinear and correlation structure of data, McRe get improved results in simulation data. The method is also applied to a microbiomic dataset.