BACKGROUND: Understanding the molecular mechanisms involved in disease is critical for the development of more effective and individualized strategies for prevention and treatment. The amount of disease-related literature, including new genetic information on the molecular mechanisms of disease, is rapidly increasing. Extracting beneficial information from literature can be facilitated by computational methods such as the knowledge-discovery approach. Several methods for mining gene-disease relationships using computational methods have been developed, however, there has been a lack of research evaluating specific disease candidate genes. RESULTS: We present a novel method for gathering and prioritizing specific disease candidate genes. Our approach involved the construction of a set of Medical Subject Headings (MeSH) terms for the effective retrieval of publications related to a disease candidate gene. Information regarding the relationships between genes and publications was obtained from the gene2pubmed database. The set of genes was prioritized using a "weighted literature score" based on the number of publications and weighted by the number of genes occurring in a publication. Using our method for the disease states of pain and Alzheimer's disease, a total of 1101 pain candidate genes and 2810 Alzheimer's disease candidate genes were gathered and prioritized. The precision was 0.30 and the recall was 0.89 in the case study of pain. The precision was 0.04 and the recall was 0.6 in the case study of Alzheimer's disease. The precision-recall curve indicated that the performance of our method was superior to that of other publicly available tools. CONCLUSIONS: Our method, which involved the use of a set of MeSH terms related to disease candidate genes and a novel weighted literature score, improved the accuracy of gathering and prioritizing candidate genes by focusing on a specific disease.

We propose a systematic method to find sequence signatures of proteins which share a common interacting partner, using a protein similarity measure based on gene ontology (GO) annotations. In a computational experiment on our original human data set of protein-protein interactions determined by Y2H assays, we have succeeded in discovering convincing candidates for interacting sites and other functional regions.

BACKGROUND: In class prediction problems using microarray data, gene selection is essential to improve the prediction accuracy and to identify potential marker genes for a disease. Among numerous existing methods for gene selection, support vector machine-based recursive feature elimination (SVM-RFE) has become one of the leading methods and is being widely used. The SVM-based approach performs gene selection using the weight vector of the hyperplane constructed by the samples on the margin. However, the performance can be easily affected by noise and outliers, when it is applied to noisy, small sample size microarray data. RESULTS: In this paper, we propose a recursive gene selection method using the discriminant vector of the maximum margin criterion (MMC), which is a variant of classical linear discriminant analysis (LDA). To overcome the computational drawback of classical LDA and the problem of high dimensionality, we present efficient and stable algorithms for MMC-based RFE (MMC-RFE). The MMC-RFE algorithms naturally extend to multi-class cases. The performance of MMC-RFE was extensively compared with that of SVM-RFE using nine cancer microarray datasets, including four multi-class datasets. CONCLUSION: Our extensive comparison has demonstrated that for binary-class datasets MMC-RFE tends to show intermediate performance between hard-margin SVM-RFE and SVM-RFE with a properly chosen soft-margin parameter. Notably, MMC-RFE achieves significantly better performance with a smaller number of genes than SVM-RFE for multi-class datasets. The results suggest that MMC-RFE is less sensitive to noise and outliers due to the use of average margin, and thus may be useful for biomarker discovery from noisy data.

Due to the recent progress of the DNA microarray technology, a large number of gene expression profile data are being produced. How to analyze gene expression data is an important topic in computational molecular biology Several studies have been done using the Boolean network as a model of a genetic network This paper proposes efficient algorithms for identifying Boolean networks of bounded indegree and related biological networks, where identification of a Boolean network can be formalized as a problem of identifying many Boolean functions simultaneously. For the identification of a Boolean network, an O(mnD+1) time naive algorithm and a simple O(mnD) time algorithm are known, where n denotes the number of nodes, m denotes the number of examples, and D denotes the maximum indegree. This paper presents an improved O(mw-2nD + mnD+w-3) time Monte-Carlo type randomized algorithm, where w is the exponent of matrix multiplication (currently, w < 2376). The algorithm is obtained by combining fast matrix multiplication with the randomized fingerprint function for string matching. Although the algorithm and its analysis are simple, the result is non-trivial and the technique can be applied to several related problems.

MOTIVATION: Inferring genetic network architecture from time series data of gene expression patterns is an important topic in bioinformatics. Although inference algorithms based on the Boolean network were proposed, the Boolean network was not sufficient as a model of a genetic network. RESULTS: First, a Boolean network model with noise is proposed, together with an inference algorithm for it. Next, a qualitative network model is proposed, in which regulation rules are represented as qualitative rules and embedded in the network structure. Algorithms are also presented for inferring qualitative relations from time series data. Then, an algorithm for inferring S-systems (synergistic and saturable systems) from time series data is presented, where S-systems are based on a particular kind of nonlinear differential equation and have been applied to the analysis of various biological systems. Theoretical results are shown for Boolean networks with noises and simple qualitative networks. Computational results are shown for Boolean networks with noises and S-systems, where real data are not used because the proposed models are still conceptual and the quantity and quality of currently available data are not enough for the application of the proposed methods.

MOTIVATION: In the process of protein construction, buried hydrophobic residues tend to assemble in a core of a protein. Methods used to predict these cores involve use or no use of sequential alignment. In the case of a close homology, prediction was more accurate if sequential alignment was used. If the homology was weak, predictions would be unreliable. A hydrophobicity plot involving the hydropathy index is useful for purposes of prediction, and smoothing is essential. However, the proposed methods are insufficient. We attempted to predict hydrophobic cores with a low frequency extracted from the hydrophobicity plot, using wavelet analysis. RESULTS: The cores were predicted at a rate of 68.7%, by cross-validation. Using wavelet analysis, the cores of non-homologous proteins can be predicted with close to 70% accuracy, without sequential alignment. AVAILABILITY: The program used in this study is available from Intergalactic Reality (http://www.intergalact.com). CONTACT: [email protected], [email protected]

A deductive database system PACADE (Protein Atomic Coordinate Analyzer with Deductive Engine) has been developed for protein structure analysis. With this system, super-secondary structures described in logical and declarative rules can be retrieved effectively. For protein structure analysis, comparison of local structures in different proteins is a necessary mean. A function to search for similar structures has, therefore, been added to the PACADE system. We describe herein the result of searches for the same topological structures and three-dimensionally similar ones. A user of PACADE can select these two levels of similarity by changing parameters. This function enables the inference system to retrieve similar structures, according to the restraints of variables defined by the user. Similar super-secondary structures among proteins can be searched for automatically, which is useful for protein structure analysis. The retrieved similar super-secondary structures can serve as criteria for protein spatial alignment.

We have developed a deductive database system PACADE for analyzing 3-D and secondary structures of protein. The PACADE system consists of a relational database created from Protein Data Bank and a deductive engine DEE based on logic programming. It has the following features: (1) The system has an inference mechanism. This means by which users can easily write and check biological hypotheses using logical and declarative rules instead of procedural programs. (2) The relational database of the PACADE system stores data on both 3-D and secondary structures of protein. The integration of this two level structure makes feasible an abstract representation of the protein structure. We describe herein the design, functions, and implementation of this PACADE system.