抗原变异可使得多种致病微生物易于逃避宿主免疫应答。表达在感染红细胞表面的恶性疟原虫红细胞表面蛋白1（PfPMP1）与感染红细胞、内皮细胞、树突状细胞以及胎盘的单个或多个受体作用，在黏附及免疫逃避中起关键的作用。每个单倍体基因组var基因家族编码约60种成员，通过启动转录不同的var基因变异体为抗原变异提供了分子基础。

疟原虫var基因转换速率变化导致抗原变异[英]／Paul H, Robert P, Christodoulou Z, et al//Proc Natl Acad Sci U S A

Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

The cBio Cancer Genomics Portal (http://cbioportal.org) is an open-access resource for interactive exploration of multidimensional cancer genomics data sets, currently providing access to data from more than 5,000 tumor samples from 20 cancer studies. The cBio Cancer Genomics Portal significantly lowers the barriers between complex genomic data and cancer researchers who want rapid, intuitive, and high-quality access to molecular profiles and clinical attributes from large-scale cancer genomics projects and empowers researchers to translate these rich data sets into biologic insights and clinical applications.

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The cBio Cancer Genomics Portal: An Open Platform for Exploring Multidimensional Cancer Genomics Data

The cBioPortal for Cancer Genomics (http://cbioportal.org) provides a Web resource for exploring, visualizing, and analyzing multidimensional cancer genomics data. The portal reduces molecular profiling data from cancer tissues and cell lines into readily understandable genetic, epigenetic, gene expression, and proteomic events. The query interface combined with customized data storage enables researchers to interactively explore genetic alterations across samples, genes, and pathways and, when available in the underlying data, to link these to clinical outcomes. The portal provides graphical summaries of gene-level data from multiple platforms, network visualization and analysis, survival analysis, patient-centric queries, and software programmatic access. The intuitive Web interface of the portal makes complex cancer genomics profiles accessible to researchers and clinicians without requiring bioinformatics expertise, thus facilitating biological discoveries. Here, we provide a practical guide to the analysis and visualization features of the cBioPortal for Cancer Genomics.

Integrative analysis of complex cancer genomics and clinical profiles using the cBioPortal

The worldwide obesity epidemic is stimulating efforts to identify host and environmental factors that affect energy balance. Comparisons of the distal gut microbiota of genetically obese mice and their lean littermates, as well as those of obese and lean human volunteers have revealed that obesity is associated with changes in the relative abundance of the two dominant bacterial divisions, the Bacteroidetes and the Firmicutes. Here we demonstrate through metagenomic and biochemical analyses that these changes affect the metabolic potential of the mouse gut microbiota. Our results indicate that the obese microbiome has an increased capacity to harvest energy from the diet. Furthermore, this trait is transmissible: colonization of germ-free mice with an 'obese microbiota' results in a significantly greater increase in total body fat than colonization with a 'lean microbiota'. These results identify the gut microbiota as an additional contributing factor to the pathophysiology of obesity.

An obesity-associated gut microbiome with increased capacity for energy harvest

Ionizing radiation is a risk factor for myeloid neoplasms including myelodysplastic syndromes (MDS), and atomic bomb survivors have been shown to have a significantly higher risk of MDS. Our previous analyses demonstrated that MDS among these survivors had a significantly higher frequency of complex karyotypes and structural alterations of chromosomes 3, 8, and 11. However, there was no difference in the median survival time between MDS among survivors compared with those of de novo origin. This suggested that a different pathophysiology may underlie the causative genetic aberrations for those among survivors. In this study, we performed genome analyses of MDS among survivors and found that proximally exposed patients had significantly fewer mutations in genes such as TET2 along the DNA methylation pathways, and they had a significantly higher rate of 11q deletions. Among the genes located in the deleted portion of chromosome 11, alterations of ATM were significantly more frequent in proximally exposed group with mutations identified on the remaining allele in 2 out of 5 cases. TP53, which is frequently mutated in therapy-related myeloid neoplasms, was equally affected between proximally and distally exposed patients. These results suggested that the genetic aberration profiles in MDS among atomic bomb survivors differed from those in therapy-related and de novo origin. Considering the role of ATM in DNA damage response after radiation exposure, further studies are warranted to elucidate how 11q deletion and aberrations of ATM contribute to the pathogenesis of MDS after radiation exposure.

Genome analysis of myelodysplastic syndromes among atomic bomb survivors in Nagasaki

We define a motif as an expression Z1.Z2...Zn with sets Z1, Z2,..., Zn of strings in a specified family omega called the type. This notion can capture the most of the motifs in PROSITE as well as regular pattern languages. A greedy strategy is developed for finding such motifs with ambiguity just from positive and negative examples by exploiting the probabilistic argument. This paper concentrates on describing the idea of the greedy algorithm with its underlying theory. Its experimental results on splicing sites and E. coli promoters are also presented.

A greedy strategy for finding motifs from yes-no examples.

DNA microarray technology enabled us to produce time series of gene expression patterns. Our research group launched a project whose purpose is to reveal the gene regulatory networks among the 6,200 genes of Saccharomyces cerevisiae. We have introduced a weighted network model as an edge-weighted graph, where each weight reflects the strength of the interaction, and analyzed its computational complexity [4]. We also proposed an algorithm to adjust the weights incrementally. Based on the algorithm, we have been developing a system to find genetic networks and visualize them [3]. One of the most serious problem in our model is that the network produced by our system might be dense, since we have not put any restrictions on in-degree nor out-degree of the network, although many biologists claim that the network should be sparse. We need some methods to reduce the edges so that the resulting network is sparse enough. In this paper, we propose a method to reduce the edges inspired by the covariance selection models [2].

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Finding Sparse Gene Networks

The prespore-specific activation of sigma factor SigF (sigma(F)) in Bacillus subtilis has been explained mainly by two factors, i.e., the transient genetic asymmetry and the volume difference between the mother cell and the prespore. Here, we systematically surveyed the effect of these two factors on sporulation using a quantitative modeling and simulation architecture named hybrid functional Petri net with extension (HFPNe). Considering the fact that the transient genetic asymmetry and the volume difference in sporulation of B. subtilis finally bring about the concentration difference in two proteins SpoIIAB (AB) and SpoIIAA (AA) between the mother cell and the prespore, we have surveyed the effect of AB and AA concentration on the prespore-specific activation of sigma(F) occurring in the early stage of sporulation. Our results show that the prespore-specific activation of sigma(F) could be governed by the ratio of AA to AB rather than their concentrations themselves. Our model also suggests that B. subtilis could maximize the ratio of AA to AB in the prespore and minimize it in the mother cell by employing both the transient genetic asymmetry and the volume difference simultaneously. This might give a good explanation to the co-occurrence of the transient asymmetry and the volume difference during sporulation of B. subtilis. In addition, we suggest for the first time that the sigma(F) activation in the prespore might be switched off by the decrease in the ratio of AA to AB after the transient genetic asymmetry is to an end by completion of DNA translocation into the prespore.

Satoru Miyano

Papers

Genome analysis of myelodysplastic syndromes among atomic bomb survivors in Nagasaki

A greedy strategy for finding motifs from yes-no examples.

Finding Sparse Gene Networks

The systems approach to the prespore-specific activation of sigma factor SigF in Bacillus subtilis

An NC Algorithm for Computing a Maximal Independent Set in a Hypergraph of Bounded Valence