High-order dynamic Bayesian Network learning with hidden common causes for causal gene regulatory network.

Background: Inferring gene regulatory network (GRN) has been an important topic in Bioinformatics. Many computational methods infer the GRN from high-throughput expression data. Due to the presence of time delays in the regulatory relationships, High-Order Dynamic Bayesian Network (HO-DBN) is a good model of GRN.

Inferring Time-Delayed Causal Gene Network Using Time-Series Expression Data.

Inferring gene regulatory network (GRN) from the microarray expression data is an important problem in Bioinformatics, because knowing the GRN is an essential first step in understanding the inner workings of the cell and the related diseases. Time delays exist in the regulatory effects from one gene to another due to the time needed for transcription, translation, and to accumulate a sufficient number of needed proteins. Also, it is known that the delays are important for oscillatory phenomenon.

Time Delayed Causal Gene Regulatory Network Inference with Hidden Common Causes.

Inferring the gene regulatory network (GRN) is crucial to understanding the working of the cell. Many computational methods attempt to infer the GRN from time series expression data, instead of through expensive and time-consuming experiments. However, existing methods make the convenient but unrealistic assumption of causal sufficiency, i.

Subtypes of associated protein-DNA (Transcription Factor-Transcription Factor Binding Site) patterns.

In protein-DNA interactions, particularly transcription factor (TF) and transcription factor binding site (TFBS) bindings, associated residue variations form patterns denoted as subtypes. Subtypes may lead to changed binding preferences, distinguish conserved from flexible binding residues and reveal novel binding mechanisms. However, subtypes must be studied in the context of core bindings.

Data mining on DNA sequences of hepatitis B virus.

Extraction of meaningful information from large experimental data sets is a key element in bioinformatics research. One of the challenges is to identify genomic markers in Hepatitis B Virus (HBV) that are associated with HCC (liver cancer) development by comparing the complete genomic sequences of HBV among patients with HCC and those without HCC. In this study, a data mining framework, which includes molecular evolution analysis, clustering, feature selection, classifier learning, and classification, is introduced.

Discovering approximate-associated sequence patterns for protein-DNA interactions.

Motivation: The bindings between transcription factors (TFs) and transcription factor binding sites (TFBSs) are fundamental protein-DNA interactions in transcriptional regulation. Extensive efforts have been made to better understand the protein-DNA interactions. Recent mining on exact TF-TFBS-associated sequence patterns (rules) has shown great potentials and achieved very promising results.

A cluster refinement algorithm for motif discovery.

Finding Transcription Factor Binding Sites, i.e., motif discovery, is crucial for understanding the gene regulatory relationship.

Discovering protein-DNA binding sequence patterns using association rule mining.

Protein-DNA bindings between transcription factors (TFs) and transcription factor binding sites (TFBSs) play an essential role in transcriptional regulation. Over the past decades, significant efforts have been made to study the principles for protein-DNA bindings. However, it is considered that there are no simple one-to-one rules between amino acids and nucleotides.

Discovering multiple realistic TFBS motifs based on a generalized model.

Background: Identification of transcription factor binding sites (TFBSs) is a central problem in Bioinformatics on gene regulation. de novo motif discovery serves as a promising way to predict and better understand TFBSs for biological verifications. Real TFBSs of a motif may vary in their widths and their conservation degrees within a certain range.

Instruction-matrix-based genetic programming.

In genetic programming (GP), evolving tree nodes separately would reduce the huge solution space. However, tree nodes are highly interdependent with respect to their fitness. In this paper, we propose a new GP framework, namely, instruction-matrix (IM)-based GP (IMGP), to handle their interactions.

Genotype-specific genomic markers associated with primary hepatomas, based on complete genomic sequencing of hepatitis B virus.

We aimed to identify genomic markers in hepatitis B virus (HBV) that are associated with hepatocellular carcinoma (HCC) development by comparing the complete genomic sequences of HBVs among patients with HCC and those without. One hundred patients with HBV-related HCC and 100 age-matched HBV-infected non-HCC patients (controls) were studied. HBV DNA from serum was directly sequenced to study the whole viral genome.

TFBS identification based on genetic algorithm with combined representations and adaptive post-processing.

Motivation: Identification of transcription factor binding sites (TFBSs) plays an important role in deciphering the mechanisms of gene regulation. Recently, GAME, a Genetic Algorithm (GA)-based approach with iterative post-processing, has shown superior performance in TFBS identification. However, the basic GA in GAME is not elaborately designed, and may be trapped in local optima in real problems.

A memetic algorithm for multiple-drug cancer chemotherapy schedule optimization.

This correspondence introduces a multidrug cancer chemotherapy model to simulate the possible response of the tumor cells under drug administration. We formulate the model as an optimal control problem. The algorithm in this correspondence optimizes the multidrug cancer chemotherapy schedule.

Genetic Parallel Programming: design and implementation.

This paper presents a novel Genetic Parallel Programming (GPP) paradigm for evolving parallel programs running on a Multi-Arithmetic-Logic-Unit (Multi-ALU) Processor (MAP). The MAP is a Multiple Instruction-streams, Multiple Data-streams (MIMD), general-purpose register machine that can be implemented on modern Very Large-Scale Integrated Circuits (VLSIs) in order to evaluate genetic programs at high speed. For human programmers, writing parallel programs is more difficult than writing sequential programs.

Phylogenetic, virological, and clinical characteristics of genotype C hepatitis B virus with TCC at codon 15 of the precore region.

Hepatitis B virus (HBV) with T-1856 of the precore region is always associated with C-1858 (i.e., TCC at nucleotides 1856 to 1858), and it is reported only in genotype C HBV isolates.

Epidemiological and virological characteristics of 2 subgroups of hepatitis B virus genotype C.

Background: We aimed to investigate the characteristics of hepatitis B virus (HBV) genotype C subgroups in Hong Kong and their relationship with HBV genotype C in other parts of Asia.

Methods: Full-genome nucleotide sequences of 49 HBV genotype C isolates from Chinese patients with chronic hepatitis B were compared with the sequences of 69 HBV genotype C isolates and 12 non-genotype C isolates in the GenBank database. Phylogenetic analysis was performed to define the subgroups of HBV genotype C on the basis of >4% heterogeneity of the entire HBV genome.