Huaming Chen

Supervised Machine Learning Model for High Dimensional Gene Data in Colon Cancer Detection

With well-developed methods in gene level data extraction, there are huge amount of gene expression data, including normal composition and abnormal ones. Therefore, mining gene expression data is currently an urgent research question, for detecting a corresponding pattern, such as cancer species, quickly and accurately. Since gene expression data classification problem has been widely studied accompanying with the development of gene technology, by far numerous methods, mainly neural network related, have been deployed in medical data analysis, which is mainly dealing with the high dimension and small quantity. A lot of research has been conducted on clustering approaches, extreme learning machine and so on. They are usually applied in a shallow neural network model. Recently deep learning has shown its power and good performance on high dimensional datasets. Unlike current popular deep neural network, we will continue to apply shallow neural network but develop an innovative algorithm for shallow neural network. In the supervised model, we demonstrate a shallow neural network model with a batch of parameters, and narrow its computational process into several positive parts, which process smoothly for a better result and finally achieve an optimal goal. It shows a stable and excellent result comparable to deep neural network. An analysis of the algorithm is also presented in this paper.

Neural network model with Monte Carlo algorithm for electricity demand forecasting in Queensland

With the rapid growth over the past few decades, people are consuming more and more electrical energies. In order to solve the contradiction between supply and demand to minimize electricity cost, it is necessary and useful to predict the electricity demand. In this paper, we apply an improved neural network algorithm to forecast the electricity, and we test it on a collected electricity demand data set in Queensland to verify its performance. There are two contributions in this paper. Firstly, comparing with backpropagation (BP) neural network, the results show a better performance on this improved neural network. Secondly, the performance on various hidden layers shows that different dimension of hidden layer in this improved neural network has little impact on the Queenslands electricity demand forecasting.

A Server Model for Reliable Communication on Cell/B.E.

In most cases of safety-related systems, the network is an indispensable part. At this point, the system reliability is as important as the system communication quality. With the emergence of multi-core architectures, the first generation usually aims to provide reliable and deterministic computing resources. Therefore, with the boost requirement of reliability and throughput that cannot be satisfied by general single-core processors, the deployment of safety-related systems is transferred and processed multi-core environments. In this paper, we propose Reliable Communication Server on SPU (RCSoS), which is a server model for reliable communication utilizing SPU (Synergistic Processor Unit) in Cell/B.E (Cell Broadband Engine). It simulates SPU as a communication server and guarantees the reliability and determinacy by the isolation mode of SPU and contract model. We have implemented RCSoS in PlayStation 3, which dynamically adjust parameters, and inform applications on contract violations. Experiments show the performance of this model.

An Evaluation of the Dynamics of Diluted Neural Network

The Monte Carlo adaptation rule has been proposed to design asymmetric neural network. By adjusting the degree of the symmetry of the networks designed by this rule, the spurious memories or unwanted attractors of the networks can be suppressed completely. We have extended this rule to design full-connected neural networks and diluted neural networks. Comparing the dynamics of these two neural networks, the simulation results indicated that the performance of diluted neural network was poorer than the performance of full-connected neural network. As to this point, further research is needed. In this paper, we use the annealed dilution method to design a diluted neural network with fixed degree of dilution. By analyzing the dynamics of the diluted neural network, it is verified that asymmetric full-connected neural network do have significant advantages over the asymmetric diluted neural network.

Collaborative data analytics towards prediction on pathogen-host protein-protein interactions

Nowadays more and more data are being sequenced and accumulated in system biology, which brings the data analytics researchers to a brand new era, namely ‘big data’, to extract the inner relationship and knowledge from the huge amount of data. Bridging the gap between computational methodology and biology to accelerate the development of biology analytics has been a hot area. In this paper, we focus on these enormous amounts of data generated with the speedy development of high throughput technologies during the past decades, especially for protein-protein interactions, which are the critical molecular process in biology. Since pathogen-host protein-protein interactions are the major and basic problems for not only infectious diseases but also drug design, molecular level interactions between pathogen and host play very critical role for the study of infection mechanisms. In this paper, we built a basic framework for analyzing the specific problems about pathogen-host protein-protein interactions (PHPPI), meanwhile, we also presented the state-of-art deep learning method results on prediction of PHPPI comparing with other machine learning methods. Utilizing the evaluation methods, specifically by considering the high skewed imbalanced ratio and huge amount of data, we detailed the pipeline solution on both storing and learning for PHPPI. This work contributes as a basis for a further investigation of protein and protein-protein interactions, with the collaboration of data analytics results from the vast amount of data dispersedly available in biology literature.

Parallel GPU-based collision detection of irregular vessel wall for massive particles

In this paper, we present a novel GPU-based limit space decomposition collision detection algorithm (LSDCD) for performing collision detection between a massive number of particles and irregular objects, which is used in the design of the Accelerator Driven Sub-Critical (ADS) system. Test results indicate that, the collisions between ten million particles and the vessel can be detected on a general personal computer in only 0.5 s per frame. With this algorithm, the collision detection of maximum sixty million particles are calculated in 3.488030 s. Experiment results show that our algorithm is promising for fast collision detection.

Cloud Services Aided E-Tourism: In the Case of Low-Cost Airlines for Backpacking

The emergence of Cloud Services and Mobile Internet has influenced the society a lot, including the tourism industry. This paper proposes a design of backpacking service, not only aimed at the travelling routines, but also focus on the low-cost airlines. This kind of service aids backpackers with an effective travelling and satisfy the price requirement. With this low-cost airlines system, the backpackers can experience real e-Tourism and enjoy a better travel aided by the real-time information. According to the basic principles of low-cost airlines, this paper provides the backpackers with a definitely efficient way to check out a suitable flight itinerary under an acceptable price.

Real-Time Management of Groundwater Resources Based on Wireless Sensors Networks

Groundwater plays a vital role in the arid inland river basins, in which the groundwater management is critical to the sustainable development of area economy and ecology. Traditional sustainable management approaches are to analyze different scenarios subject to assumptions or to construct simulation–optimization models to obtain optimal strategy. However, groundwater system is time-varying due to exogenous inputs. In this sense, the groundwater management based on static data is relatively outdated. As part of the Heihe River Basin (HRB), which is a typical arid river basin in Northwestern China, the Daman irrigation district was selected as the study area in this paper. First, a simulation–optimization model was constructed to optimize the pumping rates of the study area according to the groundwater level constraints. Three different groundwater level constraints were assigned to explore sustainable strategies for groundwater resources. The results indicated that the simulation–optimization model was capable of identifying the optimal pumping yields and satisfy the given constraints. Second, the simulation–optimization model was integrated with wireless sensors network (WSN) technology to provide real-time features for the management. The results showed time-varying feature for the groundwater management, which was capable of updating observations, constraints, and decision variables in real time. Furthermore, a web-based platform was developed to facilitate the decision-making process. This study combined simulation and optimization model with WSN techniques and meanwhile attempted to real-time monitor and manage the scarce groundwater resource, which could be used to support the decision-making related to sustainable management.

Structural Principles Analysis of Host-Pathogen Protein-Protein Interactions: A Structural Bioinformatics Survey

Computational-intelligence methods in bioinformatics and systems biology show promising potential for leveraging abundant, large-scale molecular data. These methods can facilitate analysis and prediction of the principles of biological systems through the construction of statistical and visualized models. Specifically, structural data from exogenous and endogenous protein–protein interactions are of vital significance in this context, encompassing primarily 3-D structural information for a cohort of macromolecules underpinning the biological system. In this paper, we surveyed the main methodologies and algorithms for the reconstruction and modeling of the structural-interaction networks (SINs) of host–pathogen protein–protein interactions (HPPPIs), regarding how the protein domains interact with each other to constitute a SIN. Surveying the pattern and the organization of the SIN delivers a state-of-the-art view of HPPPIs and illustrates prospective future research directions. In addition to the binary PPI network, we distilled the relevant data sources into several branching research areas and further expanded the discussions into computational-intelligence methods according to the algorithms applied, including machine learning statistical models, to shed light on effective method design. In particular, atomic resolution level investigations can reveal novel insights into the underlying principles of the organization and the complexity of HPPPIs networks. Combining data analytics and machine-learning technologies, we anticipate that our systematic overview will serve as a useful guide for interested researchers to carry out related studies on this exciting and challenging research topic in system biology.

Towards Elucidating the Structural Principles of Host-Pathogen Protein-Protein Interaction Networks: A Bioinformatics Survey

The ultimate goal of systems biology research area is to accurately predict the behavior of biological systems through the construction of computational models, using the related molecular-level data as the input, especially when the structural information of such biological system is available. Combining the three-dimensional (3D) structural information of the cohort of macromolecules underpinning the biological system, the researchers are poised with an unprecedented opportunity to gain a full understanding on how the molecules interact with each other, particularly for an interaction network, e.g. protein-protein interaction networks. Specifically, there are currently a limited number of studies focused on the reconstruction and modelling of the structural interaction networks (SIN) between hosts-pathogens protein-protein interaction networks. In this paper, we will survey the SIN on protein-protein interactions network, in which we focus on the interactions between pathogen and host species (PHPPI). As one of the most important component of inter-species PPI study, in-depth study of PHPPI at atomic-resolution level would reveal novel insights into the underlying principles of the organization and complexity of host-pathogen PPI networks. Several related sub areas are discussed, and the related typical Big Data methods including machine learning methodologies and statistics models will also be discussed. This paper contributes to a new, yet challenging, research area in applying data analytic and machine learning technologies in bioinformatics.