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

In his seminal book, Shewhart (1931) makes no demand on the distribution of the characteristic to be plotted on a control chart. How then can we explain the idea that normality is, if not required, at least highly desirable? I believe that it has come about through the many statistical studies of control-chart behavior. If one is to study how a control chart behaves, it is necessary to relate it to some distribution. The obvious choice is the normal distribution because of its ubiquity as a satisfactory model. This is bolstered by the existence of the Central Limit Theorem.

Sampling: Design and Analysis

Hypoxia inducible factorにより誘導される癌細胞の接着分子

Motivation Figures and captions convey essential information in biomedical documents. As such, there is a growing interest in mining published biomedical figures and in utilizing their respective captions as a source of knowledge. Notably, an essential step underlying such mining is the extraction of figures and captions from publications. While several PDF parsing tools that extract information from such documents are publicly available, they attempt to identify images by analyzing the PDF encoding and structure and the complex graphical objects embedded within. As such, they often incorrectly identify figures and captions in scientific publications, whose structure is often non-trivial. The extraction of figures, captions and figure-caption pairs from biomedical publications is thus neither well-studied nor yet well-addressed. Results We introduce a new and effective system for figure and caption extraction, PDFigCapX. Unlike existing methods, we first separate between text and graphical contents, and then utilize layout information to effectively detect and extract figures and captions. We generate files containing the figures and their associated captions and provide those as output to the end-user.We test our system both over a public dataset of computer science documents previously used by others, and over two newly collected sets of publications focusing on the biomedical domain. Our experiments and results comparing PDFigCapX to other state-of-the-art systems show a significant improvement in performance, and demonstrate the effectiveness and robustness of our approach. Availability and implementation Our system is publicly available for use at: https://www.eecis.udel.edu/~compbio/PDFigCapX. The two new datasets are available at: https://www.eecis.udel.edu/~compbio/PDFigCapX/Downloads.

Figure and caption extraction from biomedical documents.

Dihydroartemisinin alleviates deoxynivalenol induced liver apoptosis and inflammation in piglets.

Deoxynivalenol (DON) is a common mycotoxin that pollutes food crops and adversely affects the health of animals, even humans. Lactobacillus rhamnosus GG (LGG) can alleviate intestinal injury, and anti-inflammatory and antioxidant effects. However, the potential of LGG in alleviating kidney injury induced by DON in piglets remains to be studied. The objective of this study was to investigate the adverse effect of DON on kidney injury and the protective ability of LGG. A total of twenty-seven weaned piglets were divided into three groups: CON group, DON group (3.11 mg kg-1 feed) and LGG + DON group (LGG powder 1 g kg-1 + DON 3.15 mg kg-1 feed). DON increased the MDA content, and decreased antioxidant enzyme activity (GSH-Px) and total antioxidant capacity (P < 0.05). Meanwhile, DON activated the Nrf2 antioxidant pathway. However, LGG supplementation alleviated the damage of DON to the kidney antioxidant system of piglets. Notably, DON significantly reduced the Sirt3 expression (P < 0.05), which was alleviated by LGG addition. The expression of mitochondrial biogenesis related factors such as VDAC1 and Cyt C was up-regulated by DON (P < 0.05), and LGG could improve mitochondrial ultrastructural abnormalities and mitochondrial dysfunction. In addition, LGG mitigated DON-induced mitochondrial fusion inhibition, and prevented DON-mediated mitochondrial autophagy. In conclusion, LGG play a protective role in DON-induced kidney toxicity, and dietary intervention may be a strategy to reduce mycotoxins.

Lactobacillus rhamnosus GG ameliorates deoxynivalenol-induced kidney oxidative damage and mitochondrial injury in weaned piglets.

Abstract Background Deoxynivalenol (DON) is one of the most common environmental pollutants that induces intestinal inflammation and microbiota dysbiosis. Lactobacillus rhamnosus GG (LGG) is a probiotic that not only has anti-inflammatory effects, but also shows protective effect on the intestinal barrier. However, it is still unknown whether LGG exerts beneficial effects against DON-induced intestinal damage in piglets. In this work, a total of 36 weaned piglets were randomized to one of four treatment groups for 21 d. The treatment groups were CON (basal diet); LGG (basal diet supplemented with 1.77 × 10 11 CFU/kg LGG); DON (DON-contaminated diet) and LGG + DON (DON-contaminated diet supplemented with 1.77 × 10 11 CFU/kg LGG). Result Supplementation of LGG can enhance growth performance of piglets exposed to DON by improving intestinal barrier function. LGG has a mitigating effect on intestinal inflammation induced by DON exposure, largely through repression of the TLR4/NF-κB signaling pathway. Furthermore, supplementation of LGG increased the relative abundances of beneficial bacteria (e.g., Collinsella , Lactobacillus , Ruminococcus_torques_group and Anaerofustis ), and decreased the relative abundances of harmful bacteria (e.g., Parabacteroides and Ruminiclostridium_6 ), and also promoted the production of SCFAs. Conclusions LGG ameliorates DON-induced intestinal damage, which may provide theoretical support for the application of LGG to alleviate the adverse effects induced by DON exposure. 

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Lactobacillus rhamnosus GG ameliorates DON-induced intestinal damage depending on the enrichment of beneficial bacteria in weaned piglets

Images form a rich information source, which remains underutilized in biomedical document classification. We present here work that uses both image- and text-based features in order to identify articles of interest, in this case, pertaining to cis-regulatory modules in the context of gene-networks. Extending on our new idea, which we have recently introduced, of using OCR-based features to identify DNA contents in images, we combine image and text based classifiers to categorize documents as relevant or irrelevant to cis-regulatory modules. Using a set of hundreds of articles, marked by experts as relevant or irrelevant to cis-regulatory modules, we train/test image and text based classifiers, as well as classifiers integrating both. Our results indicate that the latter show the best performance with Recall, F-measure and Utility measures all above 0.9, demonstrating the significance of incorporating image data, and specifically OCR-based features, into the document categorization process. Moreover, the use of character distribution properties to represent images is directly relevant to other biomedical images containing text (e.g. RNA, proteins). Diagrams and other images containing text are also prevalent outside the biomedical domain, hence the work stands to be applicable and beneficial in other application areas.

Utilizing image-based features in biomedical document classification

Left Ventricular Diastolic Dysfunction (LVDD) is a decompensatory change in the relaxation properties of the heart, the risk for which increases with age. Currently, physicians use a decision-tree-like algorithm to distinguish between discrete LVDD levels. This approach, based on cut-off thresholds, can potentially lead to information loss and possibly to misdiagnosis. This paper aims to explore an alternative diagnostic method to determine LVDD risk level, taking into account a wide variety of attributes available in patient records, without pre-setting cut-off thresholds. Using a large dataset derived from the Baltimore Longitude Study of Aging (BLSA), and adjusting the data for age and gender, we employ the Chi Square test and the information gain criterion to identify attributes that correlate well with the physician-assigned grades; such attributes are referred to as distinguishing attributes. We then apply the expectation maximization (EM) algorithm, as well as the K-Means, in order to cluster records that are represented using distinguishing attributes. While clusters resulting from the K-Means are not stable, three stable and tightly-formed clusters, which are obtained from the EM algorithm, roughly correspond to the physician-assigned categories. Based on the results from the EM algorithm, we can compute a patient's probability to have low, high or no risk for LVDD, and use this probability as a basis for defining a risk score to determine the patient's LVDD severity.

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Kaidi Ma

Papers

Dihydroartemisinin alleviates deoxynivalenol induced liver apoptosis and inflammation in piglets.

Lactobacillus rhamnosus GG ameliorates deoxynivalenol-induced kidney oxidative damage and mitochondrial injury in weaned piglets.

Lactobacillus rhamnosus GG ameliorates DON-induced intestinal damage depending on the enrichment of beneficial bacteria in weaned piglets

Utilizing image-based features in biomedical document classification

Using unsupervised learning to determine risk level for left ventricular diastolic dysfunction