Note on Friedman's "what informatics is and isn't"

Friedman’s article ‘What informatics is and isn’t’, presents a necessary and timely analysis of the field of informatics

Nanoinformatics: a new area of research in nanomedicine

Over a decade ago, nanotechnologists began research on applications of nanomaterials for medicine. This research has revealed a wide range of different challenges, as well as many opportunities. Some of these challenges are strongly related to informatics issues, dealing, for instance, with the management and integration of heterogeneous information, defining nomenclatures, taxonomies and classifications for various types of nanomaterials, and research on new modeling and simulation techniques for nanoparticles. Nanoinformatics has recently emerged in the USA and Europe to address these issues. In this paper, we present a review of nanoinformatics, describing its origins, the problems it addresses, areas of interest, and examples of current research initiatives and informatics resources. We suggest that nanoinformatics could accelerate research and development in nanomedicine, as has occurred in the past in other fields. For instance, biomedical informatics served as a fundamental catalyst for the Human Genome Project, and other genomic and ?omics projects, as well as the translational efforts that link resulting molecular-level research to clinical problems and findings

A machine learning approach to identify clinical trials involving nanodrugs and nanodevices from ClinicalTrials.gov

BACKGROUND: Clinical Trials (CTs) are essential for bridging the gap between experimental research on new drugs and their clinical application. Just like CTs for traditional drugs and biologics have helped accelerate the translation of biomedical findings into medical practice, CTs for nanodrugs and nanodevices could advance novel nanomaterials as agents for diagnosis and therapy. Although there is publicly available information about nanomedicine-related CTs, the online archiving of this information is carried out without adhering to criteria that discriminate between studies involving nanomaterials or nanotechnology-based processes (nano), and CTs that do not involve nanotechnology (non-nano). Finding out whether nanodrugs and nanodevices were involved in a study from CT summaries alone is a challenging task. At the time of writing, CTs archived in the well-known online registry ClinicalTrials.gov are not easily told apart as to whether they are nano or non-nano CTs-even when performed by domain experts, due to the lack of both a common definition for nanotechnology and of standards for reporting nanomedical experiments and results. METHODS: We propose a supervised learning approach for classifying CT summaries from ClinicalTrials.gov according to whether they fall into the nano or the non-nano categories. Our method involves several stages: i) extraction and manual annotation of CTs as nano vs. non-nano, ii) pre-processing and automatic classification, and iii) performance evaluation using several state-of-the-art classifiers under different transformations of the original dataset. RESULTS AND CONCLUSIONS: The performance of the best automated classifier closely matches that of experts (AUC over 0.95), suggesting that it is feasible to automatically detect the presence of nanotechnology products in CT summaries with a high degree of accuracy. This can significantly speed up the process of finding whether reports on ClinicalTrials.gov might be relevant to a particular nanoparticle or nanodevice, which is essential to discover any precedents for nanotoxicity events or advantages for targeted drug therapy

Nanoinformatics: developing new computing applications for nanomedicine

Nanoinformatics has recently emerged to address the need of computing applications at the nano level. In this regard, the authors have participated in various initiatives to identify its concepts, foundations and challenges. While nanomaterials open up the possibility for developing new devices in many industrial and scientific areas, they also offer breakthrough perspectives for the prevention, diagnosis and treatment of diseases. In this paper, we analyze the different aspects of nanoinformatics and suggest five research topics to help catalyze new research and development in the area, particularly focused on nanomedicine. We also encompass the use of informatics to further the biological and clinical applications of basic research in nanoscience and nanotechnology, and the related concept of an extended ?nanotype? to coalesce information related to nanoparticles. We suggest how nanoinformatics could accelerate developments in nanomedicine, similarly to what happened with the Human Genome and other -omics projects, on issues like exchanging modeling and simulation methods and tools, linking toxicity information to clinical and personal databases or developing new approaches for scientific ontologies, among many others

e-MIR2: a public online inventory of medical informatics resources

Background. Over the last years, the number of available informatics resources in medicine has grown exponentially. While specific inventories of such resources have already begun to be developed for Bioinformatics (BI), comparable inventories are as yet not available for Medical Informatics (MI) field, so that locating and accessing them currently remains a hard and time-consuming task. Description. We have created a repository of MI resources from the scientific literature, providing free access to its contents through a web-based service. Relevant information describing the resources is automatically extracted from manuscripts published in top-ranked MI journals. We used a pattern matching approach to detect the resources? names and their main features. Detected resources are classified according to three different criteria: functionality, resource type and domain. To facilitate these tasks, we have built three different taxonomies by following a novel approach based on folksonomies and social tagging. We adopted the terminology most frequently used by MI researchers in their publications to create the concepts and hierarchical relationships belonging to the taxonomies. The classification algorithm identifies the categories associated to resources and annotates them accordingly. The database is then populated with this data after manual curation and validation. Conclusions. We have created an online repository of MI resources to assist researchers in locating and accessing the most suitable resources to perform specific tasks. The database contained 282 resources at the time of writing. We are continuing to expand the number of available resources by taking into account further publications as well as suggestions from users and resource developers

A pattern recognition approach to computer-aided medical diagnosis

Typescript.Thesis (Ph. D.)--University of Hawaii,1970.Bibliography: leaves [167]-169.ix, 169 l illus., tablesThis dissertation describes a pattern recognition model which has been successfully used to simulate a doctor's diagnostic process. A computer program implementing this model can be a valuable aid to the specialist, freeing him from routine screening procedures and making his skills more readily available to those patients who need them. The process of diagnostic inference is formulated as a pattern recognition task for which each disease category is represented by a characteristic pattern of symptoms and other patient variables. The method of class featuring information compression that was used assumes these characteristic patterns to form a subspace of the variable space. The subspaces are defined in terms of the components of an optimal expansion for the data vectors of a class. The use of this optimal expansion guarantees that the representative samples from which it is calculated will lie closer, on the average, to a subspace spanned by its own principal components than to any other subspace of the same dimension. The principal results of this dissertation in the pattern recognition field are procedures for selecting the dimensionality of the class subspaces to obtain good discrimination. Two quantities were found to be good predictors of recognition performance. One is a ratio of the inclusions of the paradigms of two classes within the subspace of one of them; the other is the average margin of correct classification for the paradigms of a class. Both, under certain conditions, are highly correlated with recognition performance. Therefore, a procedure for subspace selection is the maximization of the average margin of correct classification for one class subject to a constraint on the margins for all other classes. A similar procedure can be derived with the ratios. The average inclusions of paradigms within a subspace can be calculated from the autocorrelation and projection matrices of the classes. Thus, the ratios and margins are found and performance predicted without the need of actually performing any classifications. The model was tested with data obtained for 3291 patients who were examined at the Straub Clinic in Honolulu between 1963 and 1969 for the possibility of thyroid dysfunction. Data from 1963 to 1968 were used as paradigms and 1969 data as a test sample. In the diagnosis of hyperthyroidism the pattern recognition program performed consistently better than a linear discriminant method. In the diagnosis of hypothyroidism, however, the original class featuring information compression program did not perform as well as the other methods with which it was compared. Subspace selection by the method of constrained margin maximization improved the performance of the pattern recognition program considerably. The results indicate that this method can serve as a good representation of a realistic diagnostic situation. In order that the method be useful clinically a sequential version of the program was developed giving the classification of a patient at every stage of diagnosis. A category of deferred judgment was included in order that more data could be gathered when a diagnosis was uncertain. The sequential program satisfied clinical tolerances of accuracy as determined by a specialist. The on-line performance of this program has been simulated successfully and is to be implemented clinically