Explore the vast range of titles available.

Biomedical discovery is fraught with challenges stemming from diverse data types and siloed analysis. In this study, we explored common biomedical data tasks and pain points that could be addressed to elevate data quality, enhance sharing, streamline analysis, and foster collaboration across stakeholders. We recruited fifteen professionals from various biomedical roles and industries to participate in sixty-minute semi-structured interviews, which involved an assessment of their challenges, needs, and tasks as well as a brainstorm exercise to validate each professional’s research process. We applied a qualitative analysis of individual interviews using an inductive-deductive thematic coding approach for emerging themes. We identified a common set of challenges related to procuring and validating data, applying new analysis techniques and navigating varied computational environments, distributing results effectively and reproducibly, and managing the flow of data across phases of the data lifecycle. Our findings emphasize the importance of secure data sharing and facilities for collaboration throughout the discovery process. Our identified pain points provide researchers with an opportunity to align workstreams and enhance research data lifecycles to conduct biomedical discovery. We conclude our study with a summary of key actionable recommendations to tackle multiomic data challenges across the stages and phases of biomedical discovery.

ROBOKOP (Reasoning Over Biomedical Objects linked in Knowledge Oriented Pathways) is an open-source, modular, biomedical, knowledge graph (KG)–based system comprised of several key components: the ROBOKOP KG; a user interface (UI); and a variety of supporting resources, including tools and services to support deep exploration of the ROBOKOP KG and each of its underlying knowledge sources. A custom software pipeline termed Operational Routine for the Ingest and Output of Networks (ORION) standardizes, integrates, and harmonizes ROBOKOP’s knowledge sources as interoperable KGs by leveraging the community-developed Biolink Model’s universal KG schema and upper-level biomedical ontology. A ROBOKOP Graphs interface exposes the ROBOKOP KG and the other interoperable KGs, thus supporting access independent of the UI. All components of ROBOKOP are publicly accessible. Herein, we describe the v1.0 major release of ROBOKOP and highlight its features and functionalities in several application use cases, including a validation use case on asthma gene targets and two user-provided exploratory use cases on cardiotoxicity related to exposure to brominated flame retardants and diabetes mellitus related to exposure to agricultural pesticides.

Background Representation learning provides new and powerful graph analytical approaches and tools for the highly valued data science challenge of mining knowledge graphs. Since previous graph analytical methods have mostly focused on homogeneous graphs, an important current challenge is extending this methodology for richly heterogeneous graphs and knowledge domains. The biomedical sciences are such a domain, reflecting the complexity of biology, with entities such as genes, proteins, drugs, diseases, and phenotypes, and relationships such as gene co-expression, biochemical regulation, and biomolecular inhibition or activation. Therefore, the semantics of edges and nodes are critical for representation learning and knowledge discovery in real world biomedical problems. Results In this paper, we propose the edge2vec model, which represents graphs considering edge semantics. An edge-type transition matrix is trained by an Expectation-Maximization approach, and a stochastic gradient descent model is employed to learn node embedding on a heterogeneous graph via the trained transition matrix. edge2vec is validated on three biomedical domain tasks: biomedical entity classification, compound-gene bioactivity prediction, and biomedical information retrieval. Results show that by considering edge-types into node embedding learning in heterogeneous graphs, edge2vec significantly outperforms state-of-the-art models on all three tasks. Conclusions We propose this method for its added value relative to existing graph analytical methodology, and in the real world context of biomedical knowledge discovery applicability.

Background Literature based discovery (LBD) automatically infers missed connections between concepts in literature. It is often assumed that LBD generates more information than can be reasonably examined. Methods We present a detailed analysis of the quantity of hidden knowledge produced by an LBD system and the effect of various filtering approaches upon this. The investigation of filtering combined with single or multi-step linking term chains is carried out on all articles in PubMed. Results The evaluation is carried out using both replication of existing discoveries, which provides justification for multi-step linking chain knowledge in specific cases, and using timeslicing, which gives a large scale measure of performance. Conclusions While the quantity of hidden knowledge generated by LBD can be vast, we demonstrate that (a) intelligent filtering can greatly reduce the number of hidden knowledge pairs generated, (b) for a specific term, the number of single step connections can be manageable, and (c) in the absence of single step hidden links, considering multiple steps can provide valid links.

Scientific workforce diversity in biomedicine is a complex system influenced by institutional culture and programming, which collectively affect the demographic distribution and inclusion of researchers and healthcare providers. Developing and retaining a diverse scientific workforce has been encouraged as a key strategy for resolving health disparities, based on reports indicating that race‐concordance between patients and their care providers is associated with superior clinical outcomes. However, the nature of this interaction remains incompletely understood. Relevant topics to be further explored include the impact of concordance between researchers and research participants on inclusion in clinical trials; scientists’ choice of specialization and research topics; cultural competency, and others. Each of these factors affect knowledge, communication, difference, and services as related to research and/or healthcare. For example, lack of concordance between patients and physicians may result in unequal treatment for individuals from under‐represented groups. Research has also shown that sociocultural factors affect concordance, which in turn may contribute to health disparities. Further study is needed to understand how researcher/provider diversity affects the knowledge and products of biomedical inquiry and care, as well as how sociocultural factors influence relationships between researchers/research participants and providers/patients. The challenges of enhancing scientific workforce diversity, resolving health disparities, and understanding the relationship between the two call for a rigorous scientific approach featuring integrated methods of study and companion rigorous data collection and evaluation: akin to the biomedical discovery process itself. Research progress to date is encouraging, but more work remains to be done to determine the extent and nature of various domains of workforce diversity on health outcomes, and in particular on health disparities.

数据科学和情报学方法的核心在于如何从数据中挖掘出知识和见解。在与生命健康密切相关的医学和医疗领域,大数据分析应在相关性挖掘基础上揭示因果关系,增强重复性和解释性。基于因果关系的数据关联对于智库研究和情报感知具有重要意义。文章提出基于多维数据关联和深层数据关联的医学知识挖掘思路,介绍了相关数据平台和研究进展。一是实验室—临床知识转化测度与临界分析;二是科学的技术影响力测度;三是交叉性、变革性创新前沿识别;四是基于全文本、融合文献计量学与计算语言学的不确定性医学知识挖掘。前三个方面拓展了医学知识的空间,包括从实验室到临床,从科学空间到技术空间。对于确定性/不确定性医学证据和论断挖掘深化了对医学知识的因果关系的揭示和解释。

To accelerate biomedical research process, deep-learning systems are developed to automatically acquire knowledge about molecule entities by reading large-scale biomedical data. Inspired by humans that learn deep molecule knowledge from versatile reading on both molecule structure and biomedical text information, we propose a knowledgeable machine reading system that bridges both types of information in a unified deep-learning framework for comprehensive biomedical research assistance. We solve the problem that existing machine reading models can only process different types of data separately, and thus achieve a comprehensive and thorough understanding of molecule entities. By grasping meta-knowledge in an unsupervised fashion within and across different information sources, our system can facilitate various real-world biomedical applications, including molecular property prediction, biomedical relation extraction and so on. Experimental results show that our system even surpasses human professionals in the capability of molecular property comprehension, and also reveal its promising potential in facilitating automatic drug discovery and documentation in the future. To accelerate biomedical research process, deep-learning systems are developed to automatically acquire knowledge about molecule entities by reading large-scale biomedical data. Inspired by humans that learn deep molecule knowledge from both molecule structure and biomedical text information, the authors propose a machine reading system that bridges both types of information.

Recent progress in the various lab-on-a-chip microtechnologies is reviewed and the clinical and research areas in which they have made the greatest impact are discussed. Lab-on-a-chip technologies in biomedical research and diagnostics Microfluidics exploits the properties of fluids trapped in submillimetre-scale spaces — the physics behind inkjet printing, DNA microarrays, lab-on-a-chip chemistry and much else — to useful practical effect. In the past decade microfluidic devices have shown considerable promise in diagnostics and primary research in the biological sciences. In this Review, Eric Sackmann, Anna Fulton and David Beebe analyse the progress seen in lab-on-a-chip microtechnologies in recent years and discuss the clinical and research areas in which they have made — and may make — the greatest impact. Microfluidics, a technology characterized by the engineered manipulation of fluids at the submillimetre scale, has shown considerable promise for improving diagnostics and biology research. Certain properties of microfluidic technologies, such as rapid sample processing and the precise control of fluids in an assay, have made them attractive candidates to replace traditional experimental approaches. Here we analyse the progress made by lab-on-a-chip microtechnologies in recent years, and discuss the clinical and research areas in which they have made the greatest impact. We also suggest directions that biologists, engineers and clinicians can take to help this technology live up to its potential.

Artificial intelligence (AI) has been commoditized. It has evolved from a specialty resource to a readily accessible tool for cancer researchers. AI-based tools can boost research productivity in daily workflows, but can also extract hidden information from existing data, thereby enabling new scientific discoveries. Building a basic literacy in these tools is useful for every cancer researcher. Researchers with a traditional biological science focus can use AI-based tools through off-the-shelf software, whereas those who are more computationally inclined can develop their own AI-based software pipelines. In this article, we provide a practical guide for non-computational cancer researchers to understand how AI-based tools can benefit them. We convey general principles of AI for applications in image analysis, natural language processing and drug discovery. In addition, we give examples of how non-computational researchers can get started on the journey to productively use AI in their own work. This Review provides an introductory guide to artificial intelligence (AI)-based tools for non-computational cancer researchers. Here, Perez-Lopez et al. outline the general principles of AI for image analysis, natural language processing and drug discovery, as well as how researchers can get started with each of them.