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.

/pdf/the-cbio-cancer-genomics-portal-an-open-platform-for-4kclcxogdw.pdf

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

Splicing is a process that removes introns from the pre-mRNA transcript of genes and thereby connects the exons to form the mature mRNA. It takes place in the cell nucleus and is known to play a major role in the expression of genetic information in eukaryotes. Alternative splicing, splicing enhancers, and splicing inhibitors are fields of active research [1, 2, 3]. In this work, we focus on aberrant splicing. Aberrant splicing refers to abnormal variations in the splicing process that can cause diseases. We analyze a dataset of several hundred cases in which aberrant splicing is caused by mutations. The mutations are substitutions, insertions, deletions and duplications. In about 95% of these cases, the splicing was affected and changed in one of the following ways: (at least one) exon was skipped, an intron was retained, the length of one exon was changed. In order to understand the differential effect of mutations on splicing, we made use of the human genomic sequence and studied the sequences in the regions of these mutations.

http://www.jsbi.org/pdfs/journal1/GIW02/GIW02P159.pdf

Analysis of Aberrant Splicing Data

PSSMs (Position-Specific Score Matrices) have been applied to various problems in Bioinformatics. We study the following problem: given positive examples (sequences) and negative examples (sequences), find a PSSM which correctly discriminates between positive and negative examples. We prove that this problem is solved in polynomial time if the size of a PSSM is bounded by a constant. On the other hand, we prove that this problem is NP-hard if the size is not bounded. We also prove similar results on deriving a mixture of PSSMs.

On the complexity of deriving position specific Score matrices from examples

Databases of DNA and amino acid sequences compile many sequences where some regions of speci c functions or structures are indicated as segments. For example, -helices, -sheets, transmembrane domains and signal peptides are speci ed by positions on the sequences. In this paper we call such speci ed regions as marked regions. For each family of sequences with marked regions of a speci c type, some programs have been devolved for predicting such marked regions on unknown sequences. For example, ChouFasman's method [2] is well known for predicting the -helices and -sheets. The hydropathy plot of Kyte and Doolittle [3] predicts transmembrane domains on amino acid sequences of membrane proteins. Instead of designing such programs independently for each family of sequences, we have developed an automatic predictor generator HAKKE by using the knowledge discovery system BONSAI [1, 4] which nds hypotheses from positive and negative examples of sequences. Given a sample collection of sequences with marked regions, HAKKE produces automatically a C program called a predictor that may predict marked regions on unknown sequences.

HAKKE: Automatic Predictor Generator for Sequences

Analogy is described in terms of predicate logic. This paper considers the complexity of analogical reasoning in which no function symbols except constants are allowed. We show that the problem of deciding whether a given atomic formula can be inferred by analogy is NP-hard.

/pdf/np-hard-aspects-in-analogical-reasoning-50tj5cr6fm.pdf

Np-hard aspects in analogical reasoning

https://ascopubs.org/doi/pdf/10.1200/PO.19.00254

Satoru Miyano

Papers

Analysis of Aberrant Splicing Data

On the complexity of deriving position specific Score matrices from examples

HAKKE: Automatic Predictor Generator for Sequences

Np-hard aspects in analogical reasoning

Successful Clinical Sequencing by Molecular Tumor Board in an Elderly Patient With Refractory Sézary Syndrome