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.

High quality, cost-effective medical care requires consideration of the best available, most appropriate evidence in the care of each patient, a practice known as Evidence-based Medicine (EBM). EBM is dependent upon the wide availability and coverage of accurate, objective syntheses called evidence reports (also called systematic reviews). These are compiled by a time and resource-intensive process that is largely manual, and that has not taken advantage of many of the advances in information processing technologies that have assisted other textual domains. We propose a specific text-mining based pipeline to support the creation and updating of evidence reports that provides support for the literature collection, collation, and triage steps of the systematic review process. The pipeline includes a metasearch engine that covers both bibliographic databases and selected "grey" literature; a module that classifies articles according to study type; a module for grouping studies that are closely related (e.g. that derive from the same underlying clinical trial or same study cohort); and an automated system that ranks publications according to the likelihood that they will meet inclusion criteria for the report. The proposed pipeline will also enable groups performing systematic review to reuse tools and models created by other groups, and will provide a test-bed for further informatics research to develop improved tools in the future. Ultimately, this should increase the rate that high-quality systematic reviews and meta-analyses can be generated, accessed and utilized by clinicians, patients, care-givers, and policymakers, resulting in better and more cost-effective care.

/pdf/evidence-based-medicine-the-essential-role-of-systematic-2c1ct46gmq.pdf

Evidence-based medicine, the essential role of systematic reviews, and the need for automated text mining tools

Neurites from mouse retina and dorsal root ganglion explants show specific behavior within co-cultured tectum or spinal cord.

of data, but rather as providing significant ‘added value’. Consider a commercial database consisting of credit-card transactions: its purpose is to keep track of individual accounts, and most of the queries to the database are specific, focused and initiated individually. In contrast, automated data-mining techniques permit the same database to be characterised in terms of significant large-scale correlations that provide a rich array of market research data. More importantly, one can search on an ongoing basis for anomalous patterns of activity that raise the possibility of fraud; in fact, a commercial database that does not carry out such automated ‘data-driven discovery’ might even be considered negligent. I suggest that research databases that are populated and analysed according to specific

/pdf/informatics-and-hypothesis-driven-research-3g250qr8aq.pdf

Informatics and hypothesis‐driven research

Feature representation is one of the key issues in data clustering. The existing feature representation of scientific data is not sufficient, which to some extent affects the result of scientific data clustering. Therefore, the paper proposes a concept of composite text description (CTD) and a CTD-based feature representation method for biomedical scientific data. The method mainly uses different feature weight algorisms to represent candidate features based on two types of data sources respectively, combines and finally strengthens the two feature sets. Experiments show that comparing with traditional methods, the feature representation method is more effective than traditional methods and can significantly improve the performance of biomedcial data clustering.

/pdf/rediscovering-don-swanson-the-past-present-and-future-of-6hnk63uvxq.pdf

Neil R. Smalheiser

Papers

Evidence-based medicine, the essential role of systematic reviews, and the need for automated text mining tools

Neurites from mouse retina and dorsal root ganglion explants show specific behavior within co-cultured tectum or spinal cord.

Informatics and hypothesis‐driven research

Rediscovering Don Swanson: the Past, Present and Future of Literature-Based Discovery.

Automated confidence ranked classification of randomized controlled trial articles: an aid to evidence-based medicine