Rong-Ming Chen

FMGA: finding motifs by genetic algorithm

In the era of post-genomics, almost all the genes have been sequenced and enormous amounts of data have been generated. Hence, to mine useful information from these data is a very important topic. In this paper we propose a new approach for finding potential motifs in the regions located from the -2000 bp upstream to +1000 bp downstream of transcription start site (TSS). This new approach is developed based on the genetic algorithm (GA). The mutation in the GA is performed by using position weight matrices to reserve the completely conserved positions. The crossover is implemented with special-designed gap penalties to produce the optimal child pattern. We also present a rearrangement method based on position weight matrices to avoid the presence of a very stable local minimum, which may make it quite difficult for the other operators to generate the optimal pattern. Our approach shows superior results by comparing with multiple em for motif elicitation (MEME) and Gibbs sampler, which are two popular algorithms for finding motifs.

Overexpression of Thy1/CD90 in human hepatocellular carcinoma is associated with HBV infection and poor prognosis.

Thy1/CD90 is an important marker of many types of stem cells. It functions as a tumor suppressor in ovarian cancer and in nasopharyngeal carcinoma. In this study, the expression status of Thy1 in clinical hepatocellular carcinoma (HCC) tissue samples was investigated. Relationships of Thy1 expression with clinical parameters and patient survival rate were analyzed. The quantities of Thy1 mRNA were statistically higher in tumor tissues than those in the adjacent non-tumor tissues (p<0.001). Immunohistochemical data confirmed that Thy1 protein was increased in 73% of HCC samples. Thy1 expression was not influenced by chronic alcohol exposure or cirrhosis. Overexpression in Thy1 was correlated with age (p=0.006), hepatitis B virus (HBV) infection (p=0.044), and histological grade (p=0.014). Patients with the highest level of Thy1 expression showed the poorest prognosis (p=0.040). In conclusion, overexpression of Thy1 may not suppress the development of HCC. Thy1 could provide a clinical prognostic marker for HCC.

Increased expression of PRL-1 protein correlates with shortened patient survival in human hepatocellular carcinoma

BackgroundThe purposes of the current study were to investigate whether overexpression of the PRL-1 is clinically relevant to hepatocellular carcinoma (HCC) and whether expression patterns of PRL-1 in HCC have diagnostic and prognostic value.MethodsImmunohistochemistry analysis was performed for PRL-1 in 60 HCC samples. The data were correlated with clinicopathological features. The univariate and multivariate survival analyses were also performed to determine their prognostic significance.ResultsPRL-1 protein was overexpressed (83%) in HCC as compared with the adjacent normal tissue. PRL-1 expression was not influenced by chronic alcohol exposure or cirrhosis. High expression of PRL-1 was correlated with smoking (p=0.012), cirrhosis (p=0.047) and histological grade (p=0.055). The Kaplan-Meier survival curves showed that high PRL-1 expression related to a poor survival with statistical significance (I vs. III, p=0.010; II vs. III, p=0.001). Univariate analysis showed that PRL-1 expression was associated with tumour size, stage and PRL-1 score. Multivariate analysis revealed that the PRL-1 protein expression level was an independent factor for overall survival (HR, 5.367; 95% CI, 2.270–12.692; p=0.001). This is the first demonstration that the expression level of PRL-1 is correlated with tumour progression and prognosis in HCC.ConclusionsAlong with other results, the PRL-1 protein is a candidate biomarker and a potential target for novel therapies against human HCC progression.

Blood Cell Image Classification Based on Hierarchical SVM

The problem of identifying and counting blood cells within the blood smear is of both theoretical and practical interest. The differential counting of blood cells provides invaluable information to pathologist for diagnosis and treatment of many diseases. In this paper we propose an efficient hierarchical blood cell image identification and classification method based on multi-class support vector machine. In this automated process, segmentation and classification of blood cells are the most important stages. We segment the stained blood cells in digital microscopic images and extract the geometric features for each segment to identify and classify the different types of blood cells. The experimental results are compared with the manual results obtained by the pathologist, and demonstrate the effectiveness of the proposed method.

USING SCDL FOR INTEGRATING TOOLS AND DATA FOR COMPLEX BIOMEDICAL APPLICATIONS

Current bioinformatics tools or databases are very heterogeneous in terms of data formats, database schema, and terminologies. Additionally, most biomedical databases and analysis tools are scattered across different web sites making interoperability across such different services more difficult. It is desired that these diverse databases and analysis tools be normalized, integrated and encompassed with a semantic interface such that users of biological data and tools could communicate with the system in natural language and a workflow could be automatically generated and distributed into appropriate tools. In this paper, the BioSemantic System is presented to bridge complex biological/biomedical research problems and computational solutions via semantic computing. Due to the diversity of problems in various research fields, the semantic capability description language (SCDL) plays an important role as a common language and generic form for problem formalization. Several queries as well as their corresponding SCDL descriptions are provided as examples. For complex applications, multiple SCDL queries may be connected via control structures. For these cases, we present an algorithm to map a user request to one or more existing services if they exist.

Generating high-quality discrete LOD meshes for 3D computer games in linear time

The real-time interactive 3D multimedia applications such as 3D computer games and virtual reality (VR) have become prominent multimedia applications in recent years. In these applications, both visual fidelity and degree of interactivity are usually crucial to the success or failure of employment. Although the visual fidelity can be increased using more polygons for representing an object, it takes a higher rendering cost and adversely affects the rendering efficiency. To balance between the visual quality and the rendering efficiency, a set of level-of-detail (LOD) meshes has to be generated in advance. In this paper, we propose a highly efficient polygonal mesh simplification algorithm that is capable of generating a set of high-quality discrete LOD meshes in linear run time. The new algorithm adopts memoryless vertex quadric computation, and suggests the use of constant size replacement selection min-heap, pipelined simplification, two-stage optimization, and a new hole-filling scheme, which enable it to generate very high-quality LOD meshes using relatively small amount of main memory space in linear runtime.

A linear time algorithm for high quality mesh simplification

High resolution 3D range scanning as well as iso-surface extraction have introduced densely and uniformly sampled models that are difficult to render at an interactive rate. To remove excessive details and produce meshes of various resolutions for different kinds of applications, the study of fast and high quality polygonal mesh simplification algorithms has become important. In this paper, we propose a new linear time algorithm that can achieve fast and high quality mesh simplification. In the new algorithm, we pipeline the cost computation, optimization, and edge collapse, and use a small constant-sized replacement selection min-heap instead of a large greedy queue to effectively reduce the runtime complexity to linear complexity. Compared to previous works, our new algorithm has at least three advantages. First, the new algorithm is runtime efficient. Second, the new algorithm is memory efficient. Third, the algorithm is capable of generating competitive high quality outputs.

Using distributed computing platform to solve high computing and data processing problems in bioinformatics

Since the problems in bioinformatics are related to massive computing and massive data. In recent years, due to distributed computing is gaining recognition. The task originally requiring high computing power does not only rely on supercomputer. Distributed computing used off-the-shelf PC with high speed network can offer low cost and high performance computing power to handle the task. Therefore, the purpose of this paper is to implement a complete distributed computing platform based on peer-to-peer file sharing technology. The platform integrated scheduling, load balancing, file sharing, maintenance of data integrity, and user-friendly interface etc. functions. Through the platform can assist bioinformaticists in massive computing and massive data problems. Besides, the platform is easier use, more reliable, and more helpful than others for researchers to conduct bioinformatics research.

BioSemantic System: Applications of Structured Natural Language to Biological and Biochemical Research

Recent advances and new technologies in biological and medical research have resulted in a rapid accumulation of enormous amounts and types of data and data analysis tools. Although most of these databases and tools are available through the internet and are easily accessible for users, they are highly heterogeneous making it difficult to integrate into efficient workflows. In this paper, we present a natural- language, object-based computing system, BioSemantic System (BSS), for seamlessly integrating these diverse bioinformatics databases and tools into efficient workflows that will increase the productivity of end-user researchers. Below, we present vocabulary, including nouns, verbs and adjectives as well as several examples of applications to biological and biomedical research problems.

A SOFTWARE ARCHITECTURE FOR FINDING MOTIFS USING GENETIC ALGORITHM

In the era of post-genomics, almost all the genes have been sequenced and enormous amount of data have been generated. Hence, to mine useful information from these data is a very important topic. In this paper, we present a software architecture for finding motifs using genetic algorithm (GA). The new approach can find potential motifs in the regions located from the -2000 bp upstream to +1000 bp downstream of transcription start site (TSS). The mutation in the genetic algorithm is performed using position weight matrices to reserve the completely conserved positions. The crossover in the GA is implemented with specially-designed gap penalties to produce an optimal child pattern. We also present a rearrangement method based on position weight matrices to avoid the presence of a very stable local minimum that may be difficult for operators to generate the optimal pattern. The predicted results obtained from our approach are more accurate than that of Gibbs sampler and we spend less computation time than MEME.