No abstract available

The arrival of high-throughput technologies in cancer science and medicine has made the possibility for knowledge generation greater than ever before. However, this has brought with it real challenges as researchers struggle to analyse the avalanche of information available to them. A unique U.K.-based initiative has been established to promote data sharing in cancer science and medicine and to address the technical and cultural issues needed to support this.

A report on the Genome Informatics conference, held at the Wellcome Genome Campus Conference Centre, Hinxton, United Kingdom, 19-22 September 2016.

To summarize key contributions to current research in the field of Clinical Research Informatics (CRI) and to select best papers published in 2019.

Vaccine informatics is an emerging research area that focuses on development and applications of bioinformatics methods that can be used to facilitate every aspect of the preclinical, clinical, and postlicensure vaccine enterprises. Many immunoinformatics algorithms and resources have been developed to predict T- and B-cell immune epitopes for epitope vaccine development and protective immunity analysis. Vaccine protein candidates are predictable in silico from genome sequences using reverse vaccinology. Systematic transcriptomics and proteomics gene expression analyses facilitate rational vaccine design and identification of gene responses that are correlates of protection in vivo. Mathematical simulations have been used to model host-pathogen interactions and improve vaccine production and vaccination protocols. Computational methods have also been used for development of immunization registries or immunization information systems, assessment of vaccine safety and efficacy, and immunization modeling. Computational literature mining and databases effectively process, mine, and store large amounts of vaccine literature and data. Vaccine Ontology (VO) has been initiated to integrate various vaccine data and support automated reasoning.

No abstract available

Nursing has embraced online education to increase its workforce while providing flexible advanced education to nurse professionals. Faculty use virtual simulation and other adaptive learning technologies to enhance learning efficiency and student outcomes in online courses. The purpose of this study was to assess the impact of simulated Electronic Health Records (EHRs) on informatics competency in a graduate online informatics course.

This paper describes neuroinformatics technologies at 1 mm anatomical scale based on high-throughput 3D functional and structural imaging technologies of the human brain. The core is an abstract pipeline for converting functional and structural imagery into their high-dimensional neuroinformatic representation index containing O(1000-10,000) discriminating dimensions. The pipeline is based on advanced image analysis coupled to digital knowledge representations in the form of dense atlases of the human brain at gross anatomical scale. We demonstrate the integration of these high-dimensional representations with machine learning methods, which have become the mainstay of other fields of science including genomics as well as social networks. Such high-throughput facilities have the potential to alter the way medical images are stored and utilized in radiological workflows. The neuroinformatics pipeline is used to examine cross-sectional and personalized analyses of neuropsychiatric illnesses in clinical applications as well as longitudinal studies. We demonstrate the use of high-throughput machine learning methods for supporting (i) cross-sectional image analysis to evaluate the health status of individual subjects with respect to the population data, (ii) integration of image and personal medical record non-image information for diagnosis and prognosis.

Pig Genomic Information System (PigGIS) is a web-based depository of pig (Sus scrofa) genomic learning mainly engineered for biomedical research to locate pig genes from their human homologs and position single nucleotide polymorphisms (SNPs) in different pig populations. It utilizes a variety of sequence data, including whole genome shotgun (WGS) reads and expressed sequence tags (ESTs), and achieves a successful mapping solution to the low-coverage genome problem. With the data presently available, we have identified a total of 15 700 pig consensus sequences covering 18.5 Mb of the homologous human exons. We have also recovered 18 700 SNPs and 20 800 unique 60mer oligonucleotide probes for future pig genome analyses. PigGIS can be freely accessed via the web at http://www.piggis.org/ and http://pig.genomics.org.cn/.

The open source community within radiology is a vibrant collection of developers and users working on scores of collaborative projects with the goal of promoting the use of information technology within radiology for education, clinical, and research purposes. This community, which includes many commercial partners, has a rich history in supporting the success of the digital imaging and communication in medicine (DICOM) standard and today is pioneering interoperability limits by embracing the Integrating the Healthcare Enterprise. This article describes only a small portion of the more successful open source applications and is written to help end users see these projects as practical aids for the imaging informaticist and picture archiving and communication system (PACS) administrator.

To summarize significant research contributions on managing pandemics with health informatics published in 2020.

Modern data-driven tools are transforming application-specific polymer development cycles. Surrogate models that can be trained to predict properties of polymers are becoming commonplace. Nevertheless, these models do not utilize the full breadth of the knowledge available in datasets, which are oftentimes sparse; inherent correlations between different property datasets are disregarded. Here, we demonstrate the potency of multi-task learning approaches that exploit such inherent correlations effectively. Data pertaining to 36 different properties of over 13,000 polymers are supplied to deep-learning multi-task architectures. Compared to conventional single-task learning models, the multi-task approach is accurate, efficient, scalable, and amenable to transfer learning as more data on the same or different properties become available. Moreover, these models are interpretable. Chemical rules, that explain how certain features control trends in property values, emerge from the present work, paving the way for the rational design of application specific polymers meeting desired property or performance objectives.

To summarize key contributions to current research in the field of Clinical Research Informatics (CRI) and to select best papers published in 2020.

Despite the pressing need for the creation of applications that facilitate the aggregation of clinical and molecular data, most current applications are proprietary and lack the necessary compliance with standards that would allow for cross-institutional data exchange. In line with its mission of accelerating research discoveries and improving patient outcomes by linking networks of researchers, physicians, and patients focused on cancer research, caBIG (cancer Biomedical Informatics Grid) has sponsored the creation of the caTRIP (Cancer Translational Research Informatics Platform) tool, with the purpose of aggregating clinical and molecular data in a repository that is user-friendly, easily accessible, as well as compliant with regulatory requirements of privacy and security.

Through a scoping review, we examine in this survey what ways health equity has been promoted in clinical research informatics with patient implications and especially published in the year of 2021 (and some in 2022).

To examine roles for summer internship programs in expanding pathways into biomedical informatics, based on 10 years of the Vanderbilt Department of Biomedical Informatics (DBMI) Summer Research Internship Program.

A report on the 12th International Conference on Genome Informatics, Tokyo, Japan, 17-19 December 2001.

A report on the 20th International Conference on Genome Informatics, Yokohama, Japan, 14-16 December 2009.

Significant technical advances in imaging, molecular biology and genomics have fueled a revolution in cell biology, in that the molecular and structural processes of the cell are now visualized and measured routinely. Driving much of this recent development has been the advent of computational tools for the acquisition, visualization, analysis and dissemination of these datasets. These tools collectively make up a new subfield of computational biology called bioimage informatics, which is facilitated by open source approaches. We discuss why open source tools for image informatics in cell biology are needed, some of the key general attributes of what make an open source imaging application successful, and point to opportunities for further operability that should greatly accelerate future cell biology discovery.

The modern researcher is confronted with hundreds of published methods to interpret genetic variants. There are databases of genes and variants, phenotype-genotype relationships, algorithms that score and rank genes, and in silico variant effect prediction tools. Because variant prioritization is a multifactorial problem, a welcome development in the field has been the emergence of decision support frameworks, which make it easier to integrate multiple resources in an interactive environment. Current decision support frameworks are typically limited by closed proprietary architectures, access to a restricted set of tools, lack of customizability, Web dependencies that expose protected data, or limited scalability.