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

Data Mining Practical Machine Learning Tools and Techniques

MicroRNAs (miRNAs) constitute a large family of noncoding RNAs that function as guide molecules in diverse gene silencing pathways. Current efforts are focused on the regulatory function of miRNAs, while little is known about how these unusual genes themselves are regulated. Here we present the first direct evidence that miRNA genes are transcribed by RNA polymerase II (pol II). The primary miRNA transcripts (pri‐miRNAs) contain cap structures as well as poly(A) tails, which are the unique properties of class II gene transcripts. The treatment of human cells with α‐amanitin decreased the level of pri‐miRNAs at a concentration that selectively inhibits pol II activity. Furthermore, chromatin immunoprecipitation analyses show that pol II is physically associated with a miRNA promoter. We also describe, for the first time, the detailed structure of a miRNA gene by determining the promoter and the terminator of mir‐23a∼27a∼24‐2 . These data indicate that pol II is the main, if not the only, RNA polymerase for miRNA gene transcription. Our study offers a basis for understanding the structure and regulation of miRNA genes.

/pdf/microrna-genes-are-transcribed-by-rna-polymerase-ii-7trd0easzj.pdf

MicroRNA genes are transcribed by RNA polymerase II.

MicroRNAs (miRNAs) are a large family of post-transcriptional regulators of gene expression that are ~21 nucleotides in length and control many developmental and cellular processes in eukaryotic organisms. Research during the past decade has identified major factors participating in miRNA biogenesis and has established basic principles of miRNA function. More recently, it has become apparent that miRNA regulators themselves are subject to sophisticated control. Many reports over the past few years have reported the regulation of miRNA metabolism and function by a range of mechanisms involving numerous protein-protein and protein-RNA interactions. Such regulation has an important role in the context-specific functions of miRNAs.

http://m.docente.unife.it/franco.pagani/lezione-1101a-mirna/articoli-smallrna/2010%20FIlipowic%20The%20widespread%20regulation%20of%20miRNA%20%20copy.pdf

The widespread regulation of microRNA biogenesis, function and decay.

The Medical Subject Heading ‘Humans’ is manually curated and indicates human-related studies within MEDLINE However, newly published MEDLINE articles may take months to be indexed and non-MEDLINE articles lack consistent, transparent indexing of this feature Therefore, for up to date and broad literature searches, there is a need for an independent automated system to identify whether a given publication is human-related, particularly when they lack Medical Subject Headings One million MEDLINE records published in 1987–2014 were randomly selected Text-based features from the title, abstract, author name and journal fields were extracted A linear support vector machine was trained to estimate the probability that a given article should be indexed as Humans and was evaluated on records from 2015 to 2016 Overall accuracy was high: area under the receiver operating curve = 0976, F1 = 95% relative to MeSH indexing Manual review of cases of extreme disagreement with MEDLINE showed 735% agreement with the automated prediction We have tagged all articles indexed in PubMed with predictive scores and have made the information publicly available at http://arrowsmithpsychuicedu/evidence_based_medicine/indexhtml We have also made available a web-based interface to allow users to obtain predictive scores for non-MEDLINE articles This will assist in the triage of clinical evidence for writing systematic reviews

A probabilistic automated tagger to identify human-related publications.

Medical subject headings (MeSH) are a flexible and useful tool for describing biomedical concepts Here, we present MeSHier, a tool for assigning MeSH terms to biomedical documents based on abstract similarity and references to MEDLINE records When applied to PubMedCentral papers, NIH grants, and USPTO patents we find that these two sources of information produce largely disjoint sets of related MEDLINE records, albeit with some overlap in MeSH When combined they provide an enriched topical annotation that would not have been possible with either alone MeSHier is available as a demo tool that can take as input IDs of PubMed papers, USPTO patents, and NIH grants: http://abellisillinoisedu/cgi-bin/meshier/searchpy

/pdf/predicting-mesh-beyond-medline-58e54fd6vz.pdf

Predicting MeSH Beyond MEDLINE

Background: An article's citations are useful for finding related articles that may not be readily found by keyword searches or textual similarity. Citation analysis is also important for analyzing scientific innovation and the structure of the biomedical literature. We wanted to facilitate citation analysis for the broad community by providing a user-friendly interface for accessing and analyzing citation data for biomedical articles. Case Presentation: We seeded the Citation Cloud dataset with over 465 million open access citations culled from six different sources: PubMed Central, Microsoft Academic Graph, ArnetMiner, Semantic Scholar, Open Citations, and the NIH iCite dataset. We implemented a free, public extension to PubMed that allows any user to visualize and analyze the entire citation cloud around any paper of interest A: the set of articles cited by A, those which cite A, those which are co-cited with A, and those which are bibliographically coupled to A. Conclusions: Citation Cloud greatly enables the study of citations by the scientific community, including relatively advanced analyses (co-citations and bibliographic coupling) that cannot be undertaken using other available tools. The tool can be accessed by running any PubMed query on the Anne O'Tate value-added search interface and clicking on the Citations button next to any retrieved article.

/pdf/the-citation-cloud-of-a-biomedical-article-a-free-public-web-poc6dsvk.pdf

The Citation Cloud of a biomedical article: a free, public, web-based tool enabling citation analysis

Characterization of a novel set of membrane antigens associated with axonal growth. III: Expression in the regenerating goldfish optic nerve and tectum

Neil R. Smalheiser

Papers

A probabilistic automated tagger to identify human-related publications.

Predicting MeSH Beyond MEDLINE

The Citation Cloud of a biomedical article: a free, public, web-based tool enabling citation analysis

Characterization of a novel set of membrane antigens associated with axonal growth. III: Expression in the regenerating goldfish optic nerve and tectum

Means to immortalize neural cells