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

BIOE 402. Medical Technology Assessment. 2 or 3 hours. Bioentrepreneur course. Assessment of medical technology in the context of commercialization. Objectives, competition, market share, funding, pricing, manufacturing, growth, and intellectual property; many issues unique to biomedical products. Course Information: 2 undergraduate hours. 3 graduate hours. Prerequisite(s): Junior standing or above and consent of the instructor.

“Bioinformatics” 특집을 내면서

25. Multiple Imputation for Nonresponse in Surveys. By D. B. Rubin. ISBN 0 471 08705 X. Wiley, Chichester, 1987. 258 pp. £30.25.

Multiple Imputation for Nonresponse in Surveys

Matrix Factorization Techniques for Recommender Systems

We explore the effect of dimensionality on the nearest neighbor problem. We show that under a broad set of conditions (much broader than independent and identically distributed dimensions), as dimensionality increases, the distance to the nearest data point approaches the distance to the farthest data point. To provide a practical perspective, we present empirical results on both real and synthetic data sets that demonstrate that this effect can occur for as few as 10-15 dimensions. These results should not be interpreted to mean that high-dimensional indexing is never meaningful; we illustrate this point by identifying some high-dimensional workloads for which this effect does not occur. However, our results do emphasize that the methodology used almost universally in the database literature to evaluate high-dimensional indexing techniques is flawed, and should be modified. In particular, most such techniques proposed in the literature are not evaluated versus simple linear scan, and are evaluated over workloads for which nearest neighbor is not meaningful. Often, even the reported experiments, when analyzed carefully, show that linear scan would outperform the techniques being proposed on the workloads studied in high (10-15) dimensionality!.

When is nearest neighbor meaningful

Multi-document summarization aims to create a compressed summary while retaining the main characteristics of the original set of documents. Many approaches use statistics and machine learning techniques to extract sentences from documents. In this paper, we propose a new multi-document summarization framework based on sentence-level semantic analysis and symmetric non-negative matrix factorization. We first calculate sentence-sentence similarities using semantic analysis and construct the similarity matrix. Then symmetric matrix factorization, which has been shown to be equivalent to normalized spectral clustering, is used to group sentences into clusters. Finally, the most informative sentences are selected from each group to form the summary. Experimental results on DUC2005 and DUC2006 data sets demonstrate the improvement of our proposed framework over the implemented existing summarization systems. A further study on the factors that benefit the high performance is also conducted.

Multi-document summarization via sentence-level semantic analysis and symmetric matrix factorization

Deep Learning and Big Data analytics are two focal points of data science. Deep Learning models have achieved remarkable results in speech recognition and computer vision in recent years. Big Data is important for organizations that need to collect a huge amount of data like a social network and one of the greatest assets to use Deep Learning is analyzing a massive amount of data (Big Data). This advantage makes Deep Learning as a valuable tool for Big Data. Deep Learning can be used to extract incredible information that buried in a Big Data. The modern stock market is an example of these social networks. They are a popular place to increase wealth and generate income, but the fundamental problem of when to buy or sell shares, or which stocks to buy has not been solved. It is very common among investors to have professional financial advisors, but what is the best resource to support the decisions these people make? Investment banks such as Goldman Sachs, Lehman Brothers, and Salomon Brothers dominated the world of financial advice for more than a decade. However, via the popularity of the Internet and financial social networks such as StockTwits and SeekingAlpha, investors around the world have new opportunity to gather and share their experiences. Individual experts can predict the movement of the stock market in financial social networks with the reasonable accuracy, but what is the sentiment of a mass group of these expert authors towards various stocks? In this paper, we seek to determine if Deep Learning models can be adapted to improve the performance of sentiment analysis for StockTwits. We applied several neural network models such as long short-term memory, doc2vec, and convolutional neural networks, to stock market opinions posted in StockTwits. Our results show that Deep Learning model can be used effectively for financial sentiment analysis and a convolutional neural network is the best model to predict sentiment of authors in StockTwits dataset.

/pdf/big-data-deep-learning-for-financial-sentiment-analysis-3buttzxe1c.pdf

Big Data: Deep Learning for financial sentiment analysis

Recommending news articles has become a promising research direction as the Internet provides fast access to real-time information from multiple sources around the world. Traditional news recommendation systems strive to adapt their services to individual users by virtue of both user and news content information. However, the latent relationships among different news items, and the special properties of new articles, such as short shelf lives and value of immediacy, render the previous approaches inefficient. In this paper, we propose a scalable two-stage personalized news recommendation approach with a two-level representation, which considers the exclusive characteristics (e.g., news content, access patterns, named entities, popularity and recency) of news items when performing recommendation. Also, a principled framework for news selection based on the intrinsic property of user interest is presented, with a good balance between the novelty and diversity of the recommended result. Extensive empirical experiments on a collection of news articles obtained from various news websites demonstrate the efficacy and efficiency of our approach.

SCENE: a scalable two-stage personalized news recommendation system

The proliferation of malware has presented a serious threat to the security of computer systems. Traditional signature-based anti-virus systems fail to detect polymorphic and new, previously unseen malicious executables. In this paper, resting on the analysis of Windows API execution sequences called by PE files, we develop the Intelligent Malware Detection System (IMDS) using Objective-Oriented Association (OOA) mining based classification. IMDS is an integrated system consisting of three major modules: PE parser, OOA rule generator, and rule based classifier. An OOA_Fast_FP-Growth algorithm is adapted to efficiently generate OOA rules for classification. A comprehensive experimental study on a large collection of PE files obtained from the anti-virus laboratory of King-Soft Corporation is performed to compare various malware detection approaches. Promising experimental results demonstrate that the accuracy and efficiency of our IMDS system out perform popular anti-virus software such as Norton AntiVirus and McAfee VirusScan, as well as previous data mining based detection systems which employed Naive Bayes, Support Vector Machine (SVM) and Decision Tree techniques.

IMDS: intelligent malware detection system

The proliferation of malware has presented a serious threat to the security of computer systems. Traditional signature-based anti-virus systems fail to detect polymorphic/metamorphic and new, previously unseen malicious executables. Data mining methods such as Naive Bayes and Decision Tree have been studied on small collections of executables. In this paper, resting on the analysis of Windows APIs called by PE files, we develop the Intelligent Malware Detection System (IMDS) using Objective-Oriented Association (OOA) mining based classification. IMDS is an integrated system consisting of three major modules: PE parser, OOA rule generator, and rule based classifier. An OOA_Fast_FP-Growth algorithm is adapted to efficiently generate OOA rules for classification. A comprehensive experimental study on a large collection of PE files obtained from the anti-virus laboratory of KingSoft Corporation is performed to compare various malware detection approaches. Promising experimental results demonstrate that the accuracy and efficiency of our IMDS system outperform popular anti-virus software such as Norton AntiVirus and McAfee VirusScan, as well as previous data mining based detection systems which employed Naive Bayes, Support Vector Machine (SVM) and Decision Tree techniques. Our system has already been incorporated into the scanning tool of KingSoft’s Anti-Virus software.

Dingding Wang

Papers

Multi-document summarization via sentence-level semantic analysis and symmetric matrix factorization

Big Data: Deep Learning for financial sentiment analysis

SCENE: a scalable two-stage personalized news recommendation system

IMDS: intelligent malware detection system

An intelligent PE-malware detection system based on association mining