Type III secretion system (T3SS) effector protein is a part of bacterial secretion systems. T3SS exists in the pathogenic and symbiotic bacteria. How the T3SS effector proteins in these two classes differ from each other should be interesting. In this paper, we successfully discriminated T3SS effector proteins between plant pathogenic, animal pathogenic and plant symbiotic bacteria based on feature vectors inferred computationally by Yahara et al. only from amino acid sequences. This suggests that these three classes of bacteria employ distinct T3SS effector proteins. We also hypothesized that the feature vector proposed by Yahara et al. represents protein structure, possibly protein folds defined in Structural Classification of Proteins (SCOP) database.

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Identification of candidate target genes related to a particular disease is an important stage in drug development. A number of studies have extracted disease-related genes from the biomedical literature. We herein present a novel evaluation measure that identifies disease-associated genes and prioritizes the identified genes as drug target genes in terms of fewer side-effects using the biomedical literature. The proposed measure evaluates the specificity of a gene to a particular disease based on the number of diseases associated with the gene. The specificity of a gene is measured by means of, for example, term frequency-inverse document frequency (tf-idf), which is widely used in Web information retrieval. We assume that if a gene is chosen as a target gene for a disease, then side-effects are more likely to occur as the number of diseases associated with the gene increases. We verified the obtained ranking results by checking the ranks of known drug targets. As a result, 177 known drug targets were found to be ranked within the top 100 genes, and 21 drug targets were top ranked. The results suggest that the proposed measure is useful as a primary filter for extracting candidate target genes from a large number of genes.

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Mass++ is free platform software for mass spectrometry, mainly developed for biological science, with which users can construct their own functions or workflows for use as plug-ins. In this paper, we present an algorithm development example using Mass++ that performs a new baseline subtraction method. A signal processing technique previously developed to correct the atmospheric substances in infrared spectroscopy was converted to adjust to the mass spectrum baseline estimation, and a new method called Bottom Line Tracing (BLT) was constructed. BLT can estimate a suitable baseline for a mass spectrum with rapid changes in its waveform with easy parameter tuning. We confirm that it is beneficial to utilize techniques or knowledge acquired in another field to obtain a better solution for a problem, and that the practical barriers to algorithm development and distribution will be considerably reduced by platform software like Mass++.

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The inference of genetic networks is a problem to obtain mathematical models that can explain observed time-series of gene expression levels. A number of models have been proposed to describe genetic networks. The S-system model is one of the most studied models among them. Due to its advantageous features, numerous inference algorithms based on the S-system model have been proposed. The number of the parameters in the S-system model is however larger than those of the other well-studied models. Therefore, when trying to infer S-system models of genetic networks, we need to provide a larger amount of gene expression data to the inference method. In order to reduce the amount of gene expression data required for an inference of genetic networks, this study simplifies the S-system model by fixing some of its parameters to 0. In this study, we call this simplified S-system model a reduced S-system model. We then propose a new inference method that estimates the parameters of the reduced S-system model by minimizing two-dimensional functions. Finally, we check the effectiveness of the proposed method through numerical experiments on artificial and actual genetic network inference problems.

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