From a textual checklist to an information system: The case study of ITALIC,the Information System on Italian Lichens

Abstract

Checklists are fundamental for accessing information about organisms known to occur in a given area. It is possible to convert textual, paper-printed checklists into structured digital formats. This process can eventually lead to the development of digital information systems, for which the output can be far more complex than a list of taxa. Digital information systems can be continuously updated by a constant flow of information, and their content can be exported in many other different formats, hence not only mobilising, but also making biodiversity data reusable on different platforms. The conversion of the Checklist of Italian Lichens into an information system is discussed, in order to provide some general guidelines of such a process.     

Colonic lesion expert system. Evaluation of sensitivity

In a previous study, an expert system using visually assessed diagnostic clues for diagnosing colonic tissue as normal, adenoma or adenocarcinoma arrived at diagnoses agreeing with the evaluation by pathologists ("correct diagnoses") for all 49 cases of normal colon, for 49 of 50 cases of adenoma and for 48 of 49 cases of adenocarcinoma. The present study examined the robustness and sensitivity of the expert system to changes in the knowledge base, to changes in criteria specified by the user and to missing information. Alternative rules for combining certainty factors are discussed.     

A framework for querying a database for structural information on 3D images of macromolecules: A web-based query-by-content prototype on the BioImage macromolecular server

Nowadays we are experiencing a remarkable growth in the number of databases that have become accessible over the Web. However, in a certain number of cases, for example, in the case of BioImage, this information is not of a textual nature, thus posing new challenges in the design of tools to handle these data. In this work, we concentrate on the development of new mechanisms aimed at "querying" these databases of complex data sets by their intrinsic content, rather than by their textual annotations only. We concentrate our efforts on a subset of BioImage containing 3D images (volumes) of biological macromolecules, implementing a first prototype of a "query-by-content" system. In the context of databases of complex data types the term query-by-content makes reference to those data modeling techniques in which user-defined functions aim at "understanding" (to some extent) the informational content of the data sets. In these systems the matching criteria introduced by the user are related to intrinsic features concerning the 3D images themselves, hence, complementing traditional queries by textual key words only. Efficient computational algorithms are required in order to "extract" structural information of the 3D images prior to storing them in the database. Also, easy-to-use interfaces should be implemented in order to obtain feedback from the expert. Our query-by-content prototype is used to construct a concrete query, making use of basic structural features, which are then evaluated over a set of three-dimensional images of biological macromolecules. This experimental implementation can be accessed via the Web at the BioImage server in Madrid, at http://www.bioimage.org/qbc/index.html.     

Finding gene regulatory network candidates using the gene expression knowledge base

Background

Network-based approaches for the analysis of large-scale genomics data have become well established. Biological networks provide a knowledge scaffold against which the patterns and dynamics of ‘omics’ data can be interpreted. The background information required for the construction of such networks is often dispersed across a multitude of knowledge bases in a variety of formats. The seamless integration of this information is one of the main challenges in bioinformatics. The Semantic Web offers powerful technologies for the assembly of integrated knowledge bases that are computationally comprehensible, thereby providing a potentially powerful resource for constructing biological networks and network-based analysis.

Results

We have developed the Gene eXpression Knowledge Base (GeXKB), a semantic web technology based resource that contains integrated knowledge about gene expression regulation. To affirm the utility of GeXKB we demonstrate how this resource can be exploited for the identification of candidate regulatory network proteins. We present four use cases that were designed from a biological perspective in order to find candidate members relevant for the gastrin hormone signaling network model. We show how a combination of specific query definitions and additional selection criteria derived from gene expression data and prior knowledge concerning candidate proteins can be used to retrieve a set of proteins that constitute valid candidates for regulatory network extensions.

Conclusions

Semantic web technologies provide the means for processing and integrating various heterogeneous information sources. The GeXKB offers biologists such an integrated knowledge resource, allowing them to address complex biological questions pertaining to gene expression. This work illustrates how GeXKB can be used in combination with gene expression results and literature information to identify new potential candidates that may be considered for extending a gene regulatory network.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-014-0386-y) contains supplementary material, which is available to authorized users.     

Fighting the good cause: meaning,purpose, difference,and choice

Concepts of cause, choice, and information are closely related. A cause is a choice that can be held responsible. It is a difference that makes a difference. Information about past causes and their effects is a valuable commodity because it can be used to guide future choices. Information about criteria of choice is generated by choosing a subset from an ensemble for ‘reasons’ and has meaning for an interpreter when it is used to achieve an end. Natural selection evolves interpreters with ends. Surviving genes embody a textual record of past choices that had favorable outcomes. Consultation of these archives guides current choices. Purposive choice is well-informed difference making.     

Using neural networks as an aid in the determination of disease status: comparison of clinical diagnosis to neural-network predictions in a pedigree with autosomal dominant limb-girdle muscular dystrophy.

Studies of the genetics of certain inherited diseases require expertise in the determination of disease status even for single-locus traits. For example, in the diagnosis of autosomal dominant limb-girdle muscular dystrophy (LGMD1A), it is not always possible to make a clear-cut determination of disease, because of variability in the diagnostic criteria, age at onset, and differential presentation of disease. Mapping such diseases is greatly simplified if the data present a homogeneous genetic trait and if disease status can be reliably determined. Here, we present an approach to determination of disease status, using methods of artificial neural-network analysis. The method entails "training" an artificial neural network, with input facts (based on diagnostic criteria) and related results (based on disease diagnosis). The network contains weight factors connecting input "neurons" to output "neurons," and these connections are adjusted until the network can reliably produce the appropriate outputs for the given input facts. The trained network can be "tested" with a second set of facts, in which the outcomes are known but not provided to the network, to see how well the training has worked. The method was applied to members of a pedigree with LGMD1A, now mapped to chromosome 5q. We used diagnostic criteria and disease status to train a neural network to classify individuals as "affected" or "not affected." The trained network reproduced the disease diagnosis of all individuals of known phenotype, with 98% reliability. This approach defined an appropriate choice of clinical factors for determination of disease status. Additionally, it provided insight into disease classification of those considered to have an "unknown" phenotype on the basis of standard clinical diagnostic methods.     

The principles and advances of quantitative pathology

This article gives an overview of the quantitative pathologic techniques used today, with special emphasis on interactive morphometry and its application in the clinical setting. At present, stereologic calculations may be necessary, but in a diagnostic setting have only rarely proved to be essential. The reproducibility of the measurements is discussed in relation to the definition of the particles and staining methods. A number of technical factors that cause random errors are mentioned, such as quality of the slides, magnification, definition of the particles to be measured and measuring protocol. Large-scale experiments have revealed that the means of nuclear morphometric and certain stereologic features are reproducible, such as in the volume percentage of epithelium, the surface densities of glands and the mitotic activity index. The diagnostic applications of the standard deviation and shape factors of nuclear quantitative features require additional precautions, however. Having quantified cell and tissue features, multivariate analysis may result in a better discrimination of two or more groups under study. The quantitative pathologic examination of cells and tissues can provide important diagnostic and prognostic information. Quantitation in pathology is especially useful in so-called continuous lesions, in which interobserver and intraobserver disagreement is considerable. An important requirement of diagnostic morphometry is object selection by a skilled pathologist; the use of morphometry as a black box can result in dramatic errors. The criteria used for a morphometric classification rule that can be used for clinical applications are summarized. Quality control of the whole measuring system is essential. Application of these techniques for more than six years in diagnostic pathology has repeatedly corrected previous qualitative diagnoses.(ABSTRACT TRUNCATED AT 250 WORDS)     

The evaluation of contrast-enhancing brain lesions: pitfalls in current practice

The definitive diagnosis of space-occupying brain lesions can be established more readily since the advent of computerized tomographic (CT) scanning. Some brain lesions are more clearly defined when contrast-enhancing agents are utilized; however, so-called ring-enhancing lesions are not pathognomonic for specific neurological entities. Review of the literature suggests that at least four disorders must be considered in the differential diagnosis of contrast-enhancing lesions. These include mature brain abscesses of any etiology, cerebrovascular accidents, and primary or metastatic brain tumors. Since the medical and surgical management of these conditions is quite different, it is critical to establish a diagnosis before therapy is instituted. In many instances the combination of history, physical examination, laboratory, and radiologic examination will enable physicians to correctly diagnose the etiology of such brain lesions. However, we present two cases for which the above clinical and non-invasive parameters led to incorrect working diagnoses. Brain biopsy was required before appropriate management was eventually instituted. Potentially, such delays in diagnosis and institution of therapy can result in unnecessary morbidity and mortality. Each case illustrates the need to substantiate a presumptive diagnosis based on these clinical and radiographic criteria, regardless of how "typical" lesions may appear on CT scans.     

Morphometry in the diagnosis of Sj?gren's syndrome

Sublabial salivary gland biopsies of 20 patients with Sj?gren's syndrome and 58 controls were analyzed morphometrically to determine which histologic changes in the tissue are specific enough to justify a diagnosis of Sj?gren's syndrome and which changes are due to physiologic aging. The acinar atrophy, fibrosis, ductal hyperplasia and ductal dilatation mentioned in the literature as features of Sj?gren's syndrome are also observed in the tissue of aging individuals, and the lymphocytic focus score cited as the most important diagnostic parameter gives rise to about 9% of false-positive diagnoses. When using single quantitative histologic parameters, the volume percentages (Vol%) of lymphocytic foci, diffuse lymphoplasmacytic infiltrate (DLPI), acini and the inner diameter of intralobular ducts (ILD) were able to discern between the patients and the controls at a significant level, regardless of age, although considerable overlap was still present. This overlap could be reduced by consideration of at least two histologic parameters. The inhomogeneity within the tissue constituents was also used in discriminating between the patients and the control subjects. The best bivariate discriminating combination of histologic parameters was Vol% of lymphocytic foci and DLPI. Compared with qualitative subjective evaluation, this morphometric decision rule in the present material gave a 5 X reduction in the number of false-positive diagnoses of Sj?gren's syndrome, with only 1 of the 58 control subjects erroneously classified as having the syndrome. We conclude that quantitative investigation of sublabial salivary gland tissue will improve the diagnostic criteria needed for the definition of Sj?gren's syndrome.     

Lack of CD117 and rare bcl-2 expression in stomach cancer by immunohistochemistry. An immunohistochemical study with review of the literature

Aims

To develop and implement an automated virtual slide screening system that distinguishes normal histological findings and several tissue – based crude (texture – based) diagnoses.

Theoretical considerations

Virtual slide technology has to handle and transfer images of GB Bytes in size. The performance of tissue based diagnosis can be separated into a) a sampling procedure to allocate the slide area containing the most significant diagnostic information, and b) the evaluation of the diagnosis obtained from the information present in the selected area. Nyquist's theorem that is broadly applied in acoustics, can also serve for quality assurance in image information analysis, especially to preset the accuracy of sampling. Texture – based diagnosis can be performed with recursive formulas that do not require a detailed segmentation procedure. The obtained results will then be transferred into a "self-learning" discrimination system that adjusts itself to changes of image parameters such as brightness, shading, or contrast.

Methods

Non-overlapping compartments of the original virtual slide (image) will be chosen at random and according to Nyquist's theorem (predefined error-rate). The compartments will be standardized by local filter operations, and are subject for texture analysis. The texture analysis is performed on the basis of a recursive formula that computes the median gray value and the local noise distribution. The computations will be performed at different magnifications that are adjusted to the most frequently used objectives (*2, *4.5, *10, *20, *40). The obtained data are statistically analyzed in a hierarchical sequence, and in relation to the clinical significance of the diagnosis.

Results

The system has been tested with a total of 896 lung cancer cases that include the diagnoses groups: cohort (1) normal lung – cancer; cancer subdivided: cohort (2) small cell lung cancer – non small cell lung cancer; non small cell lung cancer subdivided: cohort (3) squamous cell carcinoma – adenocarcinoma – large cell carcinoma. The system can classify all diagnoses of the cohorts (1) and (2) correctly in 100%, those of cohort (3) in more than 95%. The percentage of the selected area can be limited to only 10% of the original image without any increased error rate.

Conclusion

The developed system is a fast and reliable procedure to fulfill all requirements for an automated "pre-screening" of virtual slides in lung pathology.     

Use of information on disease diagnoses from databases for animal health economic,welfare and food safety purposes: strengths and limitations of recordings

Many animal health, welfare and food safety databases include data on clinical and test-based disease diagnoses. However, the circumstances and constraints for establishing the diagnoses vary considerably among databases. Therefore results based on different databases are difficult to compare and compilation of data in order to perform meta-analysis is almost impossible. Nevertheless, diagnostic information collected either routinely or in research projects is valuable in cross comparisons between databases, but there is a need for improved transparency and documentation of the data and the performance characteristics of tests used to establish diagnoses. The objective of this paper is to outline the circumstances and constraints for recording of disease diagnoses in different types of databases, and to discuss these in the context of disease diagnoses when using them for additional purposes, including research. Finally some limitations and recommendations for use of data and for recording of diagnostic information in the future are given. It is concluded that many research questions have such a specific objective that investigators need to collect their own data. However, there are also examples, where a minimal amount of extra information or continued validation could make sufficient improvement of secondary data to be used for other purposes. Regardless, researchers should always carefully evaluate the opportunities and constraints when they decide to use secondary data. If the data in the existing databases are not sufficiently valid, researchers may have to collect their own data, but improved recording of diagnostic data may improve the usefulness of secondary diagnostic data in the future.     

Evaluation of a decision support system in a medical environment

This paper describes the impact of a decision support system on the quality of recorded diagnoses and the completeness of medical records. The assumption is that for quality assessment purposes enough data have to be recorded in an electronic medical record so that diagnostic decisions can be justified. The hypotheses were tested that active decision support will lead to better quality recorded diagnoses and more complete medical records. Three groups of ten GPs were presented with 10 cases each. The GPs had to enter the data about these cases in a GP information system. One group of GPs was not supported. The second group was presented with the ICHPPC-II-Defined criteria that had to be fulfilled when a diagnosis was entered. In a third group, the GPs were asked those data that were needed to justify an entered diagnosis (active support). It could be shown that the last group of GPs entered better quality diagnoses than the other two groups. These latter groups also entered so little data that most of their diagnoses could not be justified. It is concluded from the study that only active decision support will lead to better diagnoses and a more complete medical record that can be used for quality assessment.     

New feature subset selection procedures for classification of expression profiles

Methods for extracting useful information from the datasets produced by microarray experiments are at present of much interest. Here we present new methods for finding gene sets that are well suited for distinguishing experiment classes, such as healthy versus diseased tissues. Our methods are based on evaluating genes in pairs and evaluating how well a pair in combination distinguishes two experiment classes. We tested the ability of our pair-based methods to select gene sets that generalize the differences between experiment classes and compared the performance relative to two standard methods. To assess the ability to generalize class differences, we studied how well the gene sets we select are suited for learning a classifier. We show that the gene sets selected by our methods outperform the standard methods, in some cases by a large margin, in terms of cross-validation prediction accuracy of the learned classifier. We show that on two public datasets, accurate diagnoses can be made using only 15-30 genes. Our results have implications for how to select marker genes and how many gene measurements are needed for diagnostic purposes. When looking for differential expression between experiment classes, it may not be sufficient to look at each gene in a separate universe. Evaluating combinations of genes reveals interesting information that will not be discovered otherwise. Our results show that class prediction can be improved by taking advantage of this extra information.     

Diagnostic difficulties in the interpretation of fine needle aspirates of salivary gland lesions: the problem revisited

Diagnostic difficulties in the interpretation of he needle aspirates of salivary gland lesions: the problem revisited
Cases of salivary gland lesions ( n =325), mainly neoplastic but including a small number of non-neoplastic lesions, investigated by fine needle aspiration (FNA) and with histological correlation, are reviewed. The review identified a number of differential diagnostic problems which are discussed in some detail. One false-positive and eight false-negative diagnoses had been made resulting in a 99.5% specificity and a 85.5% sensitivity. If type-specific diagnoses are made only when all defined diagnostic criteria are present and if any uncertainty is clearly conveyed to the clinician, FNA is a safe and accurate tool in the investigation of salivary gland lesions.     

Use of Expert Panels to Define the Reference Standard in Diagnostic Research: A Systematic Review of Published Methods and Reporting

Background

In diagnostic studies, a single and error-free test that can be used as the reference (gold) standard often does not exist. One solution is the use of panel diagnosis, i.e., a group of experts who assess the results from multiple tests to reach a final diagnosis in each patient. Although panel diagnosis, also known as consensus or expert diagnosis, is frequently used as the reference standard, guidance on preferred methodology is lacking. The aim of this study is to provide an overview of methods used in panel diagnoses and to provide initial guidance on the use and reporting of panel diagnosis as reference standard.

Methods and Findings

PubMed was systematically searched for diagnostic studies applying a panel diagnosis as reference standard published up to May 31, 2012. We included diagnostic studies in which the final diagnosis was made by two or more persons based on results from multiple tests. General study characteristics and details of panel methodology were extracted. Eighty-one studies were included, of which most reported on psychiatry (37%) and cardiovascular (21%) diseases. Data extraction was hampered by incomplete reporting; one or more pieces of critical information about panel reference standard methodology was missing in 83% of studies. In most studies (75%), the panel consisted of three or fewer members. Panel members were blinded to the results of the index test results in 31% of studies. Reproducibility of the decision process was assessed in 17 (21%) studies. Reported details on panel constitution, information for diagnosis and methods of decision making varied considerably between studies.

Conclusions

Methods of panel diagnosis varied substantially across studies and many aspects of the procedure were either unclear or not reported. On the basis of our review, we identified areas for improvement and developed a checklist and flow chart for initial guidance for researchers conducting and reporting of studies involving panel diagnosis. Please see later in the article for the Editors'' Summary