Multivariate Data Analysis for Neuroimaging Data: Overview and Application to Alzheimer’s Disease

As clinical and cognitive neuroscience mature, the need for sophisticated neuroimaging analysis becomes more apparent. Multivariate analysis techniques have recently received increasing attention as they have many attractive features that cannot be easily realized by the more commonly used univariate, voxel-wise, techniques. Multivariate approaches evaluate correlation/covariance of activation across brain regions, rather than proceeding on a voxel-by-voxel basis. Thus, their results can be more easily interpreted as a signature of neural networks. Univariate approaches, on the other hand, cannot directly address functional connectivity in the brain. The covariance approach can also result in greater statistical power when compared with univariate techniques, which are forced to employ very stringent, and often overly conservative, corrections for voxel-wise multiple comparisons. Multivariate techniques also lend themselves much better to prospective application of results from the analysis of one dataset to entirely new datasets. Multivariate techniques are thus well placed to provide information about mean differences and correlations with behavior, similarly to univariate approaches, with potentially greater statistical power and better reproducibility checks. In contrast to these advantages is the high barrier of entry to the use of multivariate approaches, preventing more widespread application in the community. To the neuroscientist becoming familiar with multivariate analysis techniques, an initial survey of the field might present a bewildering variety of approaches that, although algorithmically similar, are presented with different emphases, typically by people with mathematics backgrounds. We believe that multivariate analysis techniques have sufficient potential to warrant better dissemination. Researchers should be able to employ them in an informed and accessible manner. The following article attempts to provide a basic introduction with sample applications to simulated and real-world data sets.     

Special Issue on “Big Data and Precision Medicine”

正The journal Genomics ProteomicsBioinformatics(GPB)is now inviting submissions for a special issue on the topic of‘‘Big Data and Precision Medicine’’to be published in the fall of 2016.For many complex diseases,the traditional‘‘one drug for one disease’’scenario may soon become history.The new concept of‘‘precision medicine’’will heavily rely on the collection of large amount of data from the population as well as patients of specific diseases.     

Special Issue on “Big Data and Precision Medicine”

正The journal Genomics ProteomicsBioinformatics(GPB)is now inviting submissions for a special issue on the topic of‘‘Big Data and Precision Medicine’’to be published in the fall of 2016.For many complex diseases,the traditional‘‘one drug for one disease’’scenario may     

Special Issue on ‘‘Big Data and Precision Medicine'

正The journal Genomics ProteomicsBioinformatics(GPB)is now inviting submissions for a special issue on the topic of‘‘Big Data and Precision Medicine’’to be published in the fall of 2016.For many complex diseases,the traditional‘‘one drug for one disease’’scenario may soon become history.The new concept of‘‘precision medicine’’will heavily rely on the collection of large amount of data from the population as well as patients of specific diseases.     

Special Issue on“Big Data and Precision Medicine”

<正>The journal Genomics ProteomicsBioinformatics(GPB)is now inviting submissions for a special issue on the topic of"Big data for biomedicine"to be published in the spring of 2016.For many complex diseases,the traditional"one drug for one disease"scenario may soon become history.The new concept of"precision medicine"will heavily rely on the collection of     

Special Issue on “Big Data and Precision Medicine”

<正>The journal Genomics ProteomicsBioinformatics(GPB)is now inviting submissions for a special issue on the topic of‘‘Big Data and Precision Medicine’’to be published in the spring of 2016.For many complex diseases,the traditional‘‘one drug for one disease’’scenario may soon become history.The new concept of‘‘precision medicine’’will heavily rely on the     

Special Issue on ‘‘Big Data and Precision Medicine'

<正>The journal Genomics ProteomicsBioinformatics(GPB)is now inviting submissions for a special issue on the topic of‘‘Big Data and Precision Medicine’’to be published in the spring of 2016.For many complex diseases,the traditional‘‘one drug for one disease’’scenario may soon become history.The new concept of‘‘precision medicine’’will heavily rely on the     

How Do Astronomers Share Data? Reliability and Persistence of Datasets Linked in AAS Publications and a Qualitative Study of Data Practices among US Astronomers

We analyze data sharing practices of astronomers over the past fifteen years. An analysis of URL links embedded in papers published by the American Astronomical Society reveals that the total number of links included in the literature rose dramatically from 1997 until 2005, when it leveled off at around 1500 per year. The analysis also shows that the availability of linked material decays with time: in 2011, 44% of links published a decade earlier, in 2001, were broken. A rough analysis of link types reveals that links to data hosted on astronomers'' personal websites become unreachable much faster than links to datasets on curated institutional sites. To gauge astronomers'' current data sharing practices and preferences further, we performed in-depth interviews with 12 scientists and online surveys with 173 scientists, all at a large astrophysical research institute in the United States: the Harvard-Smithsonian Center for Astrophysics, in Cambridge, MA. Both the in-depth interviews and the online survey indicate that, in principle, there is no philosophical objection to data-sharing among astronomers at this institution. Key reasons that more data are not presently shared more efficiently in astronomy include: the difficulty of sharing large data sets; over reliance on non-robust, non-reproducible mechanisms for sharing data (e.g. emailing it); unfamiliarity with options that make data-sharing easier (faster) and/or more robust; and, lastly, a sense that other researchers would not want the data to be shared. We conclude with a short discussion of a new effort to implement an easy-to-use, robust, system for data sharing in astronomy, at theastrodata.org, and we analyze the uptake of that system to-date.     

Heteromerization of Arabidopsis Kv channel α-subunits: Data and prospects

Potassium translocation in plants is accomplished by a large variety of transport systems. Most of the available molecular information on these proteins concerns voltage-gated potassium channels (Kv channels). The Arabidopsis genome comprises nine genes encoding α-subunits of Kv channels. Based on knowledge of their animal homologues, and on biochemical investigations, it is broadly admitted that four such polypeptides must assemble to yield a functional Kv channel. The intrinsic functional properties of Kv channel α-subunits have been described by expressing them in suitable heterologous contexts where homo-tetrameric channels could be characterized. However, due to the high similarity of both the polypeptidic sequence and the structural scheme of Kv channel α-subunits, formation of heteromeric Kv channels by at least two types of α-subunits is conceivable. Several examples of such heteromeric plant Kv channels have been studied in heterologous expression systems and evidence that heteromerization actually occurs in planta has now been published. It is therefore challenging to uncover the physiological role of this heteromerization. Fine tuning of Kv channels by heteromerisation could be relevant not only to potassium transport but also to electrical signaling within the plant.Key words: heteromerization, voltage-gated channels, membrane potential     

Special Issue on ‘‘Big Data and Precision Medicine”

<正>The journal Genomics ProteomicsBioinformatics(GPB)is now inviting submissions for a special issue on the topic of‘‘Big Data and Precision Medicine’’to be published in the spring of 2016.For many complex diseases,the traditional‘‘one drug for one disease’’scenario may soon become history.The new concept of‘‘precision medicine’’will heavily rely on the     

Are We Filling the Data Void? An Assessment of the Amount and Extent of Plant Collection Records and Census Data Available for Tropical South America

Large-scale studies are needed to increase our understanding of how large-scale conservation threats, such as climate change and deforestation, are impacting diverse tropical ecosystems. These types of studies rely fundamentally on access to extensive and representative datasets (i.e., “big data”). In this study, I asses the availability of plant species occurrence records through the Global Biodiversity Information Facility (GBIF) and the distribution of networked vegetation census plots in tropical South America. I analyze how the amount of available data has changed through time and the consequent changes in taxonomic, spatial, habitat, and climatic representativeness. I show that there are large and growing amounts of data available for tropical South America. Specifically, there are almost 2,000,000 unique geo-referenced collection records representing more than 50,000 species of plants in tropical South America and over 1,500 census plots. However, there is still a gaping “data void” such that many species and many habitats remain so poorly represented in either of the databases as to be functionally invisible for most studies. It is important that we support efforts to increase the availability of data, and the representativeness of these data, so that we can better predict and mitigate the impacts of anthropogenic disturbances.     

LIVE BIRTH CERTIFICATES—Evaluation of Medical and Health Data in California

In a study of 1,609 single live births occurring in San Francisco County, the information on the birth certificate was compared with that on the hospital record to determine completeness and accuracy of the items reported on the certificate.Items such as color or race of mother, age of mother, birth weight and birth length of child were well recorded on the certificate and agreed with information found in the hospital record.Medical conditions were grossly underreported on the birth certificate. Conditions relating to the mother were more frequently recorded than those relating to the infant, but the birth certificates recorded less than one-fifth of all medical conditions of both mother and infant that were entered in the hospital records.Methods suggested for improving the quality of maternal and newborn morbidity information include revision of the medical section of the present certificates of live birth and fetal death and use of a precoded hospital record.     

Towards “A Natural History of Data”: Evolving Practices and Epistemologies of Data in Paleontology, 1800–2000

The fossil record is paleontology’s great resource, telling us virtually everything we know about the past history of life. This record, which has been accumulating since the beginning of paleontology as a professional discipline in the early nineteenth century, is a collection of objects. The fossil record exists literally, in the specimen drawers where fossils are kept, and figuratively, in the illustrations and records of fossils compiled in paleontological atlases and compendia. However, as has become increasingly clear since the later twentieth century, the fossil record is also a record of data. Paleontologists now routinely abstract information from the physical fossil record to construct databases that serve as the basis for quantitative analysis of patterns in the history of life. What is the significance of this distinction? While it is often assumed that the orientation towards treating the fossil record as a record of data is an innovation of the computer age, it turns out that nineteenth century paleontology was substantially “data driven.” This paper traces the evolution of data practices and analyses in paleontology, primarily through examination of the compendia in which the fossil record has been recorded over the past 200 years. I argue that the transition towards conceptualizing the fossil record as a record of data began long before the emergence of the technologies associated with modern databases (such as digital computers and modern statistical methods). I will also argue that this history reveals how new forms of visual representation were associated with the transition from seeing the fossil record as a record of objects to one of data or information, which allowed paleontologists to make new visual arguments about their data. While these practices and techniques have become increasingly sophisticated in recent decades, I will show that their basic methodology was in place over a century ago, and that, in a sense, paleontology has always been a “data driven” science.     

Is Bagging Effective in the Classification of Small-Sample Genomic and Proteomic Data?

There has been considerable interest recently in the application of bagging in the classification of both gene-expression data and protein-abundance mass spectrometry data. The approach is often justified by the improvement it produces on the performance of unstable, overfitting classification rules under small-sample situations. However, the question of real practical interest is whether the ensemble scheme will improve performance of those classifiers sufficiently to beat the performance of single stable, nonoverfitting classifiers, in the case of small-sample genomic and proteomic data sets. To investigate that question, we conducted a detailed empirical study, using publicly-available data sets from published genomic and proteomic studies. We observed that, under t-test and RELIEF filter-based feature selection, bagging generally does a good job of improving the performance of unstable, overfitting classifiers, such as CART decision trees and neural networks, but that improvement was not sufficient to beat the performance of single stable, nonoverfitting classifiers, such as diagonal and plain linear discriminant analysis, or 3-nearest neighbors. Furthermore, as expected, the ensemble method did not improve the performance of these classifiers significantly. Representative experimental results are presented and discussed in this work.     

CHOLEDOCHOLITHIASIS—Correlation of Preoperative with Operative and Postoperative Data to Enhance Diagnostic Insight

One hundred cases of common bile duct explorations were reviewed in an attempt to obtain information that might give insight into the diagnosis and definitive treatment of choledocholithiasis. Fifty of the hundred patients had common duct stones. Correlations were made between the incidence of choledocholithiasis as proved at operation, and the following factors: Kind and number of choledochal exploratory criteria used, the clinical diagnosis of common duct stones, and the pathologic features of gallbladders removed.The incidence of stones was statistically related to aging.The most frequent choledochal exploratory criteria were common duct dilatation or thickening (63 cases) and history of jaundice (50 cases).The most reliable single criterion in “diagnosing” common duct stones was palpable common or hepatic duct stones, the diagnosis having been correct in 15 of 17 such cases.The most reliable combination of criteria was a history of jaundice, plus palpable stones, with correct diagnosis in all such cases.The clinical diagnosis of choledocholithiasis was correct in only 17 per cent of cases.The correlation of the incidence of common duct stones with the degree of gallbladder disease—that is, acute or chronic—did not provide information that might be helpful in diagnosing choledocholithiasis.The incidence of proven retained common duct stones was 3 per cent, the non-fatal postoperative complication rate was 21 per cent and operative mortality was 1 per cent.     

Data warehouse for assessing animal health,welfare, risk management and –communication

The objective of this paper is to give an overview of existing databases in Denmark and describe some of the most important of these in relation to establishment of the Danish Veterinary and Food Administrations’ veterinary data warehouse. The purpose of the data warehouse and possible use of the data are described. Finally, sharing of data and validity of data is discussed. There are databases in other countries describing animal husbandry and veterinary antimicrobial consumption, but Denmark will be the first country relating all data concerning animal husbandry, -health and -welfare in Danish production animals to each other in a data warehouse. Moreover, creating access to these data for researchers and authorities will hopefully result in easier and more substantial risk based control, risk management and risk communication by the authorities and access to data for researchers for epidemiological studies in animal health and welfare.     

Mapping Plant Interactomes Using Literature Curated and Predicted Protein–Protein Interaction Data Sets

Most cellular processes are enabled by cohorts of interacting proteins that form dynamic networks within the plant proteome. The study of these networks can provide insight into protein function and provide new avenues for research. This article informs the plant science community of the currently available sources of protein interaction data and discusses how they can be useful to researchers. Using our recently curated IntAct Arabidopsis thaliana protein–protein interaction data set as an example, we discuss potentials and limitations of the plant interactomes generated to date. In addition, we present our efforts to add value to the interaction data by using them to seed a proteome-wide map of predicted protein subcellular locations.For well over two decades, plant scientists have studied protein interactions within plants using many different and evolving approaches. Their findings are represented by a large and growing corpus of peer-reviewed literature reflecting the increasing activity in this area of plant proteomic research. More recently, a number of predicted interactomes have been reported in plants and, while these predictions remain largely untested, they could act as a useful guide for future research. These studies have allowed researchers to better understand the function of protein complexes and to refine our understanding of protein function within the cell (Uhrig, 2006; Morsy et al., 2008). The extraction of protein interaction data from the literature and its standardized deposition and representation within publicly available databases remains a challenging task. Aggregating the data in databases allows researchers to leverage visualization, data mining, and integrative approaches to produce new insights that would be unachievable when the data are dispersed within largely inaccessible formats (Rodriguez et al., 2009).Currently, there are three databases that act as repositories of plant protein interaction data. These are IntAct (http://www.ebi.ac.uk/intact/; Aranda et al., 2010), The Arabidopsis Information Resource (TAIR; http://www.Arabidopsis.org/; Poole, 2007), and BioGRID (http://www.thebiogrid.org/; Breitkreutz et al., 2008). These databases curate experimentally established interactions available from the peer-reviewed literature (as opposed to predicted interactions, which will be discussed below). Each repository takes its own approach to the capture, storage, and representation of protein interaction data. TAIR focuses on Arabidopsis thaliana protein–protein interaction data exclusively; BioGRID currently focuses on the plant species Arabidopsis and rice (Oryza sativa), while IntAct attempts to capture protein interaction data from any plant species. Unlike the other repositories, IntAct follows a deep curation strategy that captures detailed experimental and biophysical details, such as binding regions and subcellular locations of interactions using controlled vocabularies (Aranda et al., 2010). While the majority of plant interaction data held by IntAct concern protein–protein interaction data in Arabidopsis, there is a small but growing content of interaction data relating to protein–DNA, protein–RNA, and protein–small molecule interactions, as well as interaction data from other plant species.Using the IntAct Arabidopsis data set as an example, we outline how the accumulating knowledge captured in these repositories can be used to further our understanding of the plant proteome. We compare the characteristics of predicted interactomes with the IntAct protein–protein interaction data set, which consists entirely of experimentally measured protein interactions, to gauge the predictive accuracy of these studies. Finally, we show how the IntAct data set can be used together with a recently developed Divide and Conquer k-Nearest Neighbors Method (DC-kNN; K. Lee et al., 2008) to predict the subcellular locations for most Arabidopsis proteins. This data set predicts high confidence subcellular locations for many unannotated Arabidopsis proteins and should act as a useful resource for future studies of protein function. Although this article focuses on the IntAct Arabidopsis protein–protein interaction data set, readers are also encouraged to explore the resources offered by our colleagues at TAIR and BioGRID.Each database employs its own system to report molecular interactions, as represented in the referenced source publications, and each avoids making judgments on interaction reliability or whether two participants in a complex have a direct interaction. Thus, the user should carefully filter these data sets for their specific purpose based on the full annotation of the data sets. In particular, the user should consider the experimental methods and independent observation of the same interaction in different publications when assessing the reliability and type of interaction of the proteins (e.g., direct or indirect). Confidence scoring schemes for interaction data are discussed widely in the literature (Yu and Finley, 2009).