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

1 Department of Information Science, Graduate School of Science, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8654, Japan 2 Course in Computer Science and Information Mathematics, Graduate School of Electro-Communications, 1-5-1 Chofugaoka, Choufu-city, Tokyo 182-8585, Japan 3 Human Genome Center, Institute of Medical Science, The University of Tokyo, 4-6-1 Shirokanedai, Minato-ku, Tokyo 108-8639, Japan

Parallelized Knowledge Discovery System

Construction and simulation of biological pathways are crucial steps in understanding complex networks of biological elements in cells. To construct simulatable models, structures of networks and chemical reactions are collected from existing literature and the values of parameters in the model are set based on the results of biological experiments or estimated based on observed data by some computational method. However, it is possible that there are some missing relationships or elements in the literature-based networks. Therefore, we need to develop a computational strategy to improve a prototype model and create better ones that can predict biological phenomena.

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Poster: Comprehensive pharmacogenomic pathway screening by data assimilation

The prognosis for acute myeloid leukemia (AML) with TP53 mutations (mTP53) after an allogeneic stem cell transplantation (allo-SCT) is unfavorable mainly due to a higher incidence of relapse [1,2]....

Azacitidine effectively reduces TP53-mutant leukemic cell burden in secondary acute myeloid leukemia after cord blood transplantation.

We define a motif as an expression Z1 .Z2 Zn with sets Zl, Z2, . . . , Zn of strings in a specified family 0 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 negaiive examples by exploiting the probabilistic argument. This paper concentrates on describing the idea of the greedy algorit hm with its underling theory. Its experiment a1 results on splicing sites and E. coli promoters are also presented.

A Greedy Strategy for Finding Motifs from Yes-No Examples

Disorder in protein structure and function have received very little attention even though the significance of these disordered regions from the functional aspects cannot be denied [2, 3, 5]. Protein Data Bank (PDB) which contains the largest collection of structural information of proteins is also the main source of disordered proteins. However, due to the lack of organised annotation and collective data on disordered regions from this database, the biological community at large is unaware of the importance of disorder. Hence, we propose here a database that will provide more accessibility to a systematic collection of disorder proteins from PDB.

Satoru Miyano

Papers

Parallelized Knowledge Discovery System

Poster: Comprehensive pharmacogenomic pathway screening by data assimilation

Azacitidine effectively reduces TP53-mutant leukemic cell burden in secondary acute myeloid leukemia after cord blood transplantation.

A Greedy Strategy for Finding Motifs from Yes-No Examples

PDB-DO: Database of Disorder