Dyport: dynamic importance-based biomedical hypothesis generation benchmarking technique.

BACKGROUND: Automated hypothesis generation (HG) focuses on uncovering hidden connections within the extensive information that is publicly available. This domain has become increasingly popular, thanks to modern machine learning algorithms. However, the automated evaluation of HG systems is still an open problem, especially on a larger scale. RESULTS: This paper presents a novel benchmarking framework Dyport for evaluating biomedical hypothesis generation systems. Utilizing curated datasets, our approach tests these systems under realistic conditions, enhancing the relevance of our evaluations. We integrate knowledge from the curated databases into a dynamic graph, accompanied by a method to quantify discovery importance. This not only assesses hypotheses accuracy but also their potential impact in biomedical research which significantly extends traditional link prediction benchmarks. Applicability of our benchmarking process is demonstrated on several link prediction systems applied on biomedical semantic knowledge graphs. Being flexible, our benchmarking system is designed for broad application in hypothesis generation quality verification, aiming to expand the scope of scientific discovery within the biomedical research community. CONCLUSIONS: Dyport is an open-source benchmarking framework designed for biomedical hypothesis generation systems evaluation, which takes into account knowledge dynamics, semantics and impact. All code and datasets are available at: https://github.com/IlyaTyagin/Dyport .

Advancements in medical research: Exploring Fourier Transform Infrared (FTIR) spectroscopy for tissue, cell, and hair sample analysis.

BACKGROUND: Fourier Transform Infrared (FTIR) spectroscopy has emerged as a powerful analytical tool in medical research, offering non-invasive and precise examination of the molecular composition of biological samples. The primary objective of this review is to underscore the benefits of FTIR spectroscopy in medicinal research, emphasizing its ability to delineate molecular fingerprints and assist in the identification of biochemical structures and key peaks in biological samples. METHODS: This review comprehensively explores the diverse applications of FTIR spectroscopy in medical investigations, with a specific focus on its utility in analyzing tissue, cells, and hair samples. Various sources, including Google Scholar, PubMed, WorledCat and Scopus, were utilized to conduct this comprehensive literature review. RESULTS: Recent advancements showcase the versatility of FTIR spectroscopy in elucidating cellular and molecular processes, facilitating disease diagnostics, and enabling treatment monitoring. Notably, FTIR spectroscopy has found significant utility in clinical assessment, particularly in screening counterfeit medicines, owing to its user-friendly operation and minimal sample preparation requirements. Furthermore, customs officials can leverage this technique for preliminary analysis of suspicious samples. CONCLUSION: This review aims to bridge a gap in the literature and serve as a valuable resource for future research endeavors in FTIR spectroscopy within the medical domain. Additionally, it presents fundamental concepts of FTIR spectroscopy and spectral data interpretation, highlighting its utility as a tool for molecular analysis using Mid-Infrared (MIR) radiation.

A practical guide to data management and sharing for biomedical laboratory researchers.

Effective data management and sharing have become increasingly crucial in biomedical research; however, many laboratory researchers lack the necessary tools and knowledge to address this challenge. This article provides an introductory guide into research data management (RDM), and the importance of FAIR (Findable, Accessible, Interoperable, and Reusable) data-sharing principles for laboratory researchers produced by practicing scientists. We explore the advantages of implementing organized data management strategies and introduce key concepts such as data standards, data documentation, and the distinction between machine and human-readable data formats. Furthermore, we offer practical guidance for creating a data management plan and establishing efficient data workflows within the laboratory setting, suitable for labs of all sizes. This includes an examination of requirements analysis, the development of a data dictionary for routine data elements, the implementation of unique subject identifiers, and the formulation of standard operating procedures (SOPs) for seamless data flow. To aid researchers in implementing these practices, we present a simple organizational system as an illustrative example, which can be tailored to suit individual needs and research requirements. By presenting a user-friendly approach, this guide serves as an introduction to the field of RDM and offers practical tips to help researchers effortlessly meet the common data management and sharing mandates rapidly becoming prevalent in biomedical research.

Myths and Methodologies: Standardisation in Human Physiology Research-Should We Control the Controllables?

The premise of research in human physiology is to explore a multifaceted system whilst identifying one or a few outcomes of interest. Therefore, the control of potentially confounding variables requires careful thought regarding the extent of control and complexity of standardisation. One common factor to control prior to testing is diet, as food and fluid provision may deviate from participants' habitual diets, yet a self-report and replication method can be flawed by under-reporting. Researchers may also need to consider standardisation of physical activity, whether it be through familiarisation trials, wash-out periods, or guidance on levels of physical activity to be achieved before trials. In terms of pharmacological agents, the ethical implications of standardisation require researchers to carefully consider how medications, caffeine consumption and oral contraceptive prescriptions may affect the study. For research in females, it should be considered whether standardisation between- or within-participants in regards to menstrual cycle phase is most relevant. The timing of measurements relative to various other daily events is relevant to all physiological research and so it can be important to standardise when measurements are made. This review summarises the areas of standardisation which we hope will be considered useful to anyone involved in human physiology research, including when and how one can apply standardisation to various contexts.

Data mining and machine learning in HIV infection risk research: An overview and recommendations.

In the contemporary era, the applications of data mining and machine learning have permeated extensively into medical research, significantly contributing to areas such as HIV studies. By reviewing 38 articles published in the past 15 years, the study presents a roadmap based on seven different aspects, utilizing various machine learning techniques for both novice researchers and experienced researchers seeking to comprehend the current state of the art in this area. While traditional regression modeling techniques have been commonly used, researchers are increasingly adopting more advanced fully supervised machine learning and deep learning techniques, which often outperform the traditional methods in predictive performance. Additionally, the study identifies nine new open research issues and outlines possible future research plans to enhance the outcomes of HIV infection risk research. This review is expected to be an insightful guide for researchers, illuminating current practices and suggesting advancements in the field.

Click Chemistry: Reaction Rates and Their Suitability for Biomedical Applications.

Click chemistry has become a commonly used synthetic method due to the simplicity, efficiency, and high selectivity of this class of chemical reactions. Since their initial discovery, further click chemistry methods have been identified and added to the toolbox of click chemistry reactions for biomedical applications. However, selecting the most suitable reaction for a specific application is often challenging, as multiple factors must be considered, including selectivity, reactivity, biocompatibility, and stability. Thus, this review provides an overview of the benefits and limitations of well-established click chemistry reactions with a particular focus on the importance of considering reaction rates, an often overlooked criterion with little available guidance. The importance of understanding each click chemistry reaction beyond simply the reaction speed is discussed comprehensively with reference to recent biomedical research which utilized click chemistry. This review aims to provide a practical resource for researchers to guide the selection of click chemistry classes for different biomedical applications.

Challenges in IBD Research 2024: Novel Technologies.

Novel technology is one of the five focus areas of the Challenges in Inflammatory Bowel Disease (IBD) Research 2024 document. Building off the Challenges in IBD Research 2019 document, the Foundation aims to provide a comprehensive overview of current gaps in IBD research and deliver actionable approaches to address them with a focus on how these gaps can lead to advancements in interception, remission, and restoration for these diseases. The document is the result of a multidisciplinary collaboration from scientists, clinicians, patients, and funders and represents a valuable resource for patient-centric research prioritization. Specifically, the Novel Technologies section focuses on addressing key research gaps to enable interception and improve remission rates in IBD. This includes testing predictions of disease onset and progression, developing novel technologies tailored to specific phenotypes, and facilitating collaborative translation of science into diagnostics, devices, and therapeutics. Proposed priority actions outlined in the document include real-time measurement of biological changes preceding disease onset, more effective quantification of fibrosis, exploration of technologies for local treatment of fistulas, and the development of drug delivery platforms for precise, location-restricted therapies. Additionally, there is a strong emphasis on fostering collaboration between various stakeholders to accelerate progress in IBD research and treatment. Addressing these research gaps necessitates the exploration and implementation of bio-engineered novel technologies spanning a spectrum from materials to systems. By harnessing innovative ideas and technologies, there's a collective effort to enhance patient care and outcomes for individuals affected by IBD.

Technology drives medical progress, solving clinical challenges and enhancing patient care in inflammatory bowel disease (IBD). Collaborative efforts focus on addressing research gaps to improve interception, restoration, and remission rates, utilizing innovative technologies for better patient outcomes.

Searching COVID-19 Clinical Research Using Graph Queries: Algorithm Development and Validation.

BACKGROUND: Since the beginning of the COVID-19 pandemic, >1 million studies have been collected within the COVID-19 Open Research Dataset, a corpus of manuscripts created to accelerate research against the disease. Their related abstracts hold a wealth of information that remains largely unexplored and difficult to search due to its unstructured nature. Keyword-based search is the standard approach, which allows users to retrieve the documents of a corpus that contain (all or some of) the words in a target list. This type of search, however, does not provide visual support to the task and is not suited to expressing complex queries or compensating for missing specifications. OBJECTIVE: This study aims to consider small graphs of concepts and exploit them for expressing graph searches over existing COVID-19-related literature, leveraging the increasing use of graphs to represent and query scientific knowledge and providing a user-friendly search and exploration experience. METHODS: We considered the COVID-19 Open Research Dataset corpus and summarized its content by annotating the publications' abstracts using terms selected from the Unified Medical Language System and the Ontology of Coronavirus Infectious Disease. Then, we built a co-occurrence network that includes all relevant concepts mentioned in the corpus, establishing connections when their mutual information is relevant. A sophisticated graph query engine was built to allow the identification of the best matches of graph queries on the network. It also supports partial matches and suggests potential query completions using shortest paths. RESULTS: We built a large co-occurrence network, consisting of 128,249 entities and 47,198,965 relationships; the GRAPH-SEARCH interface allows users to explore the network by formulating or adapting graph queries; it produces a bibliography of publications, which are globally ranked; and each publication is further associated with the specific parts of the query that it explains, thereby allowing the user to understand each aspect of the matching. CONCLUSIONS: Our approach supports the process of query formulation and evidence search upon a large text corpus; it can be reapplied to any scientific domain where documents corpora and curated ontologies are made available.

A practical guide to bioimaging research data management in core facilities.

Bioimage data are generated in diverse research fields throughout the life and biomedical sciences. Its potential for advancing scientific progress via modern, data-driven discovery approaches reaches beyond disciplinary borders. To fully exploit this potential, it is necessary to make bioimaging data, in general, multidimensional microscopy images and image series, FAIR, that is, findable, accessible, interoperable and reusable. These FAIR principles for research data management are now widely accepted in the scientific community and have been adopted by funding agencies, policymakers and publishers. To remain competitive and at the forefront of research, implementing the FAIR principles into daily routines is an essential but challenging task for researchers and research infrastructures. Imaging core facilities, well-established providers of access to imaging equipment and expertise, are in an excellent position to lead this transformation in bioimaging research data management. They are positioned at the intersection of research groups, IT infrastructure providers, the institution´s administration, and microscope vendors. In the frame of German BioImaging - Society for Microscopy and Image Analysis (GerBI-GMB), cross-institutional working groups and third-party funded projects were initiated in recent years to advance the bioimaging community's capability and capacity for FAIR bioimage data management. Here, we provide an imaging-core-facility-centric perspective outlining the experience and current strategies in Germany to facilitate the practical adoption of the FAIR principles closely aligned with the international bioimaging community. We highlight which tools and services are ready to be implemented and what the future directions for FAIR bioimage data have to offer.