Microsoft Excel or similar spreadsheet software.

Objectives: Clinico-Genomic Data (CGD) acquired through routine clinical practice has the potential to improve our understanding of clinical oncology. However, these data often reside in heterogeneous and semi-structured data, resulting in prolonged time-to-analyses. Materials and Methods: We created GENETEX: an R package and Shiny application for text mining genomic reports from EHR and direct import into REDCap®. Results: GENETEX facilitates the abstraction of CGD from EHR and streamlines capture of structured data into REDCap®. Its functions include natural language processing of key genomic information, transformation of semi-structured data into structured data and importation into REDCap. When evaluated with manual abstraction, GENETEX had >99% agreement and captured CGD in approximately one-fifth the time. Conclusions: GENETEX is freely available under the Massachusetts Institute of Technology license and can be obtained from GitHub. GENETEX is executed in R and deployed as...

Objective: The Huntsman Cancer Institute (HCI) Research Informatics Shared Resource (RISR), a software and database development core facility, sought to address a lack of published operational best practices for research informatics cores. It aimed to use those insights to enhance effectiveness after an increase in team size from 20 to 31 full-time equivalents coincided with a reduction in user satisfaction. Materials and Methods: RISR migrated from a water-scrum-fall model of software development to agile software development practices, which emphasize iteration and collaboration. RISR’s agile implementation emphasizes the product owner role, which is responsible for user engagement and may be particularly valuable in software development that requires close engagement with users like in science. Results: All RISR’s software development teams implemented agile practices in early 2020. All project teams are led by a product owner who serves as the voice of the user on the development te..., We used Huntsman Cancer Institute (HCI)'s annual user survey of its shared resources to evaluate the impact of the Research Informatics Shared Resource (RISR)'s new structure in its first year. The survey is administered by the HCI Research Administration office and is distributed through Survey Monkey to cancer center members and recent users of at least one HCI shared resource. While the survey asks many questions that applied to RISR, the questions that are the focus of this analysis are listed below:

Overall, how would you rate the quality of the service/product you received from the Research Informatics Shared Resource? Answers: Exceptional, high, average, poor, unacceptable Overall, how would you rate the turnaround time for receiving data, products or other services from the Research Informatics Shared Resource? Answers: Exceptional, high, average, poor, unacceptable

The user survey was open between September 11 and September 24. Thus, it provided feedback nine months after RIS..., , # Enhancing research informatics core user satisfaction through agile practices

https://doi.org/10.5061/dryad.00000004v

DATA OVERVIEW

The dataset contains a subset of the results from the Huntsman Cancer Institute (HCI, https://healthcare.utah.edu/huntsmancancerinstitute/ research shared resource annual user survey at the University of Utah (https://www.utah.edu/.

The survey is administered by the HCI Research Administration office and is distributed through Survey Monkey to cancer center members and recent users of at least one HCI shared resource. This dataset is from two questions asked about the HCI Research Informatics Shared Resource (RISR, https://risr.hci.utah.edu/:

1\. Overall, how would you rate the quality of the service/product you received from the Research Informatics Shared Resource? 

  Possible answers: exce...

This simulated dataset constitutes two files (after decompression), namely: sim_ergo_1600.csv and sim_pat_1600.csv.1. ergo.csv contains heart rate timeseries data for 1600 patients' ergometric tests. For each patient, 20 different ergometric tests were simulated. Each row in this file constitutes three field values: Ergo_ID, Heart Rate (BPM), and timestamp.2. pat.csv contains only four sample readings from each of the patient's 20 ergometric tests. Each row contains three values: patient_ID, Heart Rate, and timestamp. The goal is to link patients (identified by their patient_ID in the pat.csv file) to their corresponding ergometric tests (identified by their Ergo_ID in the ergo.csv file). This is done solely on matching the timestamp-value pairs from both files.The timeseries record linkage task described above is efficiently accomplished by the proposed tslink2 algorithm. tslink2 is implemented in C++ and is publicly availabe at https://github.com/ahmsoliman/tslink2Data is simulated such that correctly linked/matched identifiers follow the following formula:|Ergo_ID - patient_ID| mod 104 == 0The above formula is useful in evaluating the linkage algorithm performance.

The USDA Agricultural Research Service (ARS) recently established SCINet , which consists of a shared high performance computing resource, Ceres, and the dedicated high-speed Internet2 network used to access Ceres. Current and potential SCINet users are using and generating very large datasets so SCINet needs to be provisioned with adequate data storage for their active computing. It is not designed to hold data beyond active research phases. At the same time, the National Agricultural Library has been developing the Ag Data Commons, a research data catalog and repository designed for public data release and professional data curation. Ag Data Commons needs to anticipate the size and nature of data it will be tasked with handling.

The ARS Web-enabled Databases Working Group, organized under the SCINet initiative, conducted a study to establish baseline data storage needs and practices, and to make projections that could inform future infrastructure design, purchases, and policies. The SCINet Web-enabled Databases Working Group helped develop the survey which is the basis for an internal report. While the report was for internal use, the survey and resulting data may be generally useful and are being released publicly.

From October 24 to November 8, 2016 we administered a 17-question survey (Appendix A) by emailing a Survey Monkey link to all ARS Research Leaders, intending to cover data storage needs of all 1,675 SY (Category 1 and Category 4) scientists. We designed the survey to accommodate either individual researcher responses or group responses. Research Leaders could decide, based on their unit's practices or their management preferences, whether to delegate response to a data management expert in their unit, to all members of their unit, or to themselves collate responses from their unit before reporting in the survey.

Larger storage ranges cover vastly different amounts of data so the implications here could be significant depending on whether the true amount is at the lower or higher end of the range. Therefore, we requested more detail from 'Big Data users, 'those 47 respondents who indicated they had more than 10 to 100 TB or over 100 TB total current data (Q5). All other respondents are called 'Small Data users. 'Because not all of these follow-up requests were successful, we used actual follow-up responses to estimate likely responses for those who did not respond.

We defined active data as data that would be used within the next six months. All other data would be considered inactive, or archival.

To calculate per person storage needs we used the high end of the reported range divided by 1 for an individual response, or by G, the number of individuals in a group response. For Big Data users we used the actual reported values or estimated likely values.

Similarity measures based on the comparison of dense bit vectors of two-dimensional chemical features are a dominant method in chemical informatics. For large-scale problems, including compound selection and machine learning, computing the intersection between two dense bit vectors is the overwhelming bottleneck. We describe efficient implementations of this primitive as well as example applications using features of modern CPUs that allow 20–40× performance increases relative to typical code. Specifically, we describe fast methods for population count on modern x86 processors and cache-efficient matrix traversal and leader clustering algorithms that alleviate memory bandwidth bottlenecks in similarity matrix construction and clustering. The speed of our 2D comparison primitives is within a small factor of that obtained on GPUs and does not require specialized hardware.

Background: Pneumonia is the leading cause of death in children globally. In low- and middle-income countries the diagnosis of pneumonia relies heavily on an accurate assessment of respiratory rate, which can be unreliable in nurses and clinicians with less advanced training. In order to inform more accurate measurements, we investigate the repeatability of the RRate app used by nurses in district hospitals in Uganda. Methods: This planned secondary analysis included 3679 children aged 0-5 years. The dataset had two sequential measurements of respiratory rate using the RRate app. We measured the agreement between respiratory rate observations and clustering around fixed thresholds defined by WHO for fast breathing, which are 60 breaths per minute (bpm) for under two months (Age-1), 50 bpm for two to 12 months (Age-2), and 40 bpm for 12.1 to 60 months (Age-3). We then assessed the repeatability of the paired measurements using the Intraclass Correlation Coefficient (ICC). Results: The respiratory rate measurement took less than 15 seconds for 7,277 (98.9%) of the measurements. Despite respiratory rates clustering around the WHO fast-breathing thresholds, the breathing classification based on the thresholds was changed in only 12.6% of children. The mean (SD) respiratory rate by age group was 60 (13.1) bpm for Age-1, 49 (11.9) bpm for Age-2, and 38 (10.1) for Age-3, and the bias (Limits of Agreements) were 0.3 (-10.8 – 11.3), 0.4 (-8.5 – 9.3), and 0.1 (-6.8, 7.0) for Age-1, Age-2, and Age-3 respectively. Most importantly, the repeatability of the two respiratory rate measurements for the 3,679 children was high, with an ICC value (95% CI) of 0.95 (0.94 – 0.95). Discussion: The RRate measurements were both efficient and repeatable. The simplicity, repeatability, and efficiency of the RRate app used by healthcare workers in LMICs supports more widespread adoption for clinical use. NOTE for restricted files: If you are not yet a CoLab member, please complete our membership application survey to gain access to restricted files within 2 business days. Some files may remain restricted to CoLab members. These files are deemed more sensitive by the file owner and are meant to be shared on a case-by-case basis. Please contact the CoLab coordinator at sepsiscolab@bcchr.ca or visit our website.

According to Cognitive Market Research, the global Healthcare Informatics Patient Monitoring market size will be USD 51415.8 million in 2025. It will expand at a compound annual growth rate (CAGR) of 12.60% from 2025 to 2033.

North America held the major market share for more than 40% of the global revenue with a market size of USD 19023.85 million in 2025 and will grow at a compound annual growth rate (CAGR) of 10.4% from 2025 to 2033.
Europe accounted for a market share of over 30% of the global revenue with a market size of USD 14910.58 million in 2025 and will grow at a compound annual growth rate (CAGR) of 10.9% from 2025 to 2033.
APAC held a market share of around 23% of the global revenue with a market size of USD 12339.79 million in 2025 and will grow at a compound annual growth rate (CAGR) of 14.6% from 2025 to 2033.
South America has a market share of more than 5% of the global revenue with a market size of USD 1953.80 million in 2025 and will grow at a compound annual growth rate (CAGR) of 11.6% from 2025 to 2033.
The Middle East had a market share of around 2% of the global revenue and was estimated at a market size of USD 2056.63 million in 2025. and will grow at a compound annual growth rate (CAGR) of 11.9% from 2025 to 2033.
Africa had a market share of around 1% of the global revenue and was estimated at a market size of USD 1131.15 million in 2025. and will grow at a compound annual growth rate (CAGR) of 12.3% from 2025 to 2033.
Metropolitan and urban hospitals and healthcare facilities category is the fastest growing segment of the Healthcare Informatics Patient Monitoring industry

Market Dynamics of Healthcare Informatics Patient Monitoring Market

Key Drivers for Healthcare Informatics Patient Monitoring Market

Expansion of Remote Patient Monitoring (RPM) to Boost Market Growth

The market for healthcare informatics and patient monitoring is growing due in large part to the expansion of remote patient monitoring, or RPM. By allowing healthcare professionals to monitor patients outside of conventional clinical settings continuously, RPM improves the management of chronic diseases and lowers readmissions to hospitals. This technology allows vital sign data to be collected in real-time, giving doctors early information for preventative measures. The need for cost-effective treatment, the ageing of the population, and the incidence of chronic illnesses are all driving the adoption of RPM. Together with developments in wearable technology and wireless connectivity, RPM is revolutionizing the way patient care is delivered by making it more accessible, data-driven, and personalized. This is driving the global adoption of healthcare informatics solutions.

Rising Prevalence of Chronic Diseases to Boost Market Growth

The market for healthcare informatics and patient monitoring is expanding at a rapid pace due to the increased incidence of chronic diseases like diabetes, heart disease, and respiratory problems. Informatics-driven solutions are crucial for efficient management and prompt responses because many illnesses necessitate ongoing, long-term monitoring. Real-time tracking of critical health metrics is made possible by patient monitoring systems that are integrated with healthcare informatics, which lowers the risk of complications and hospital stays. Healthcare practitioners are increasingly using digital tools to enhance treatment coordination and outcomes as the worldwide burden of chronic illnesses rises as a result of ageing populations and lifestyle factors. This development is driving global demand for sophisticated patient monitoring systems and informatics platforms.

Restraint Factor for the Healthcare Informatics Patient Monitoring Market

High Implementation and Maintenance Expenses Will Limit Market Growth

The healthcare informatics and patient monitoring market's expansion is severely hampered by high installation and maintenance expenses. It takes a significant upfront investment in hardware, software, training, and IT infrastructure to implement modern informatics systems and patient monitoring technology. Continuing maintenance, software upgrades, and technical assistance further raise operational costs. These expenses may be unaffordable, particularly for rural or low-resource healthcare professionals and small facilities. Healthcare companies are unable to fully benefit from digital health solutions due to budgetary constraints...

The Next-Generation Sequencing (NGS) Informatics market is experiencing robust growth, projected to reach $904.4 million in 2025 and exhibiting a Compound Annual Growth Rate (CAGR) of 12.9% from 2025 to 2033. This expansion is fueled by several key drivers. The increasing adoption of NGS technologies across various applications, including genomics research, oncology, and pharmacogenomics, is a major catalyst. Furthermore, the decreasing cost of NGS sequencing and the concurrent rise in readily available data storage solutions are making NGS more accessible and practical for a wider range of users. Advances in cloud computing and bioinformatics tools are streamlining data analysis, enhancing the speed and efficiency of research. The development of sophisticated algorithms for variant calling, gene expression analysis, and pathway analysis further accelerates the interpretation and utilization of NGS data, leading to faster breakthroughs in disease understanding and treatment. The market's growth is also shaped by emerging trends. The integration of artificial intelligence (AI) and machine learning (ML) into NGS data analysis platforms is transforming the ability to identify patterns, predict outcomes, and personalize treatments. The increasing demand for personalized medicine is driving the development of specialized NGS informatics solutions tailored to specific diseases and patient populations. Furthermore, the growing collaboration between technology providers and healthcare organizations fosters innovation and accelerates the adoption of advanced NGS informatics tools. Despite these positive trends, challenges remain, including the need for robust data security measures, the complexity of NGS data analysis, and the ongoing need for skilled bioinformaticians to effectively interpret the results. However, the overall market outlook for NGS informatics remains exceptionally promising, indicating strong potential for continued growth and innovation in the coming years. Key players like Agilent Technologies, Illumina, and Thermo Fisher Scientific are actively shaping this evolving landscape.

Clinical Genomic Data Analysis Market Outlook

The global clinical genomic data analysis market size was valued at USD 1.5 billion in 2023 and is projected to reach USD 6.3 billion by 2032, growing at a compound annual growth rate (CAGR) of 17.2% during the forecast period. This market growth is driven by the increasing adoption of genomic sequencing technologies, advancements in bioinformatics, and the rising prevalence of chronic diseases that necessitate personalized medicine and targeted therapies.

A major growth factor for the clinical genomic data analysis market is the exponential increase in the volume of genomic data being generated. With the cost of sequencing dropping and the speed of sequencing increasing, more genomic data is being produced than ever before. This abundance of data requires sophisticated analysis tools and software to interpret and derive meaningful insights, driving the demand for advanced genomic data analysis solutions. Additionally, the integration of artificial intelligence and machine learning algorithms in genomics is further enhancing the capabilities of these analysis tools, enabling more accurate and faster data interpretation.

Another significant factor contributing to market growth is the rising incidence of genetic disorders and cancers, which necessitates comprehensive genomic analysis for accurate diagnosis and personalized treatment plans. Personalized medicine, which tailors medical treatment to the individual characteristics of each patient, relies heavily on the insights gained from genomic data analysis. As the understanding of the genetic basis of diseases deepens, the demand for clinical genomic data analysis is expected to surge, further propelling market growth.

The integration of NGS Informatics and Clinical Genomics is revolutionizing the field of personalized medicine. By leveraging next-generation sequencing (NGS) technologies, researchers and clinicians can now analyze vast amounts of genomic data with unprecedented speed and accuracy. This integration enables the identification of genetic variants that may contribute to disease, allowing for more precise diagnosis and the development of targeted therapies. As the capabilities of NGS technologies continue to expand, the role of informatics in managing and interpreting this data becomes increasingly critical. The seamless integration of NGS Informatics and Clinical Genomics is paving the way for more effective and personalized healthcare solutions, ultimately improving patient outcomes.

Government initiatives and funding in genomics research also play a crucial role in the expansion of the clinical genomic data analysis market. Many governments around the world are investing heavily in genomic research projects and infrastructure to advance medical research and improve public health outcomes. For instance, initiatives like the 100,000 Genomes Project in the UK and the All of Us Research Program in the US underscore the importance of genomics in understanding human health and disease, thereby boosting the demand for genomic data analysis tools and services.

Regional outlook reveals significant growth opportunities in emerging markets, particularly in the Asia Pacific region. Countries like China, India, and Japan are witnessing rapid advancements in healthcare infrastructure and increasing investments in genomics research. Additionally, favorable government policies and the presence of a large patient pool make this region a lucrative market for clinical genomic data analysis. North America continues to dominate the market due to high healthcare spending, advanced research facilities, and the early adoption of new technologies. Europe also shows steady growth with significant contributions from countries like the UK, Germany, and France.

Component Analysis

The component segment of the clinical genomic data analysis market is divided into software and services. The software segment encompasses various bioinformatics tools and platforms used for genomic data analysis. These tools are essential for the effective management, storage, and interpretation of the massive amounts of genomic data generated. The growing complexity of genomic data necessitates the use of robust software solutions that can handle large datasets and provide accurate insights. As a result, the software segment is expected to witness significant growth during the forecast period.

The services segment includes

Background: Substantial mortality occurs after hospital discharge in children younger than 5 years with suspected sepsis, especially in low-income countries. A better understanding of its epidemiology is needed for effective interventions to reduce child mortality in these countries. We evaluated risk factors for death after discharge in children admitted to hospital for suspected sepsis in Uganda, and assessed how these differed by age, time of death, and location of death. Methods: In this prospective observational cohort study, we recruited 0-60-month-old children admitted with suspected sepsis from the community to the paediatric wards of six Ugandan hospitals. The primary outcome was six-month post-discharge mortality among those discharged alive. We evaluated the interactive impact of age, time of death, and location of death on risk factors for mortality. Findings: 6,545 children were enrolled, with 6,191 discharged alive. The median (interquartile range) time from discharge to death was 28 (9-74) days, with a six-month post-discharge mortality rate of 5·5%, constituting 51% of total mortality. Deaths occurred at home (45%), in-transit to care (18%), or in hospital (37%) during a subsequent readmission. Post-discharge death was strongly associated with weight-for-age z-scores < -3 (adjusted risk ratio [aRR] 4·7, 95% CI 3·7–5·8 vs a Z score of >–2), referral for further care (7·3, 5·6–9·5), and unplanned discharge (3·2, 2·5–4·0). The hazard ratio of those with severe anaemia increased with time since discharge, while the hazard ratios of discharge vulnerabilities (unplanned, poor feeding) decreased with time. Age influenced the effect of several variables, including anthropometric indices (less impact with increasing age), anaemia (greater impact), and admission temperature (greater impact). Data Collection Methods: All data were collected at the point of care using encrypted study tablets and these data were then uploaded to a Research Electronic Data Capture (REDCap) database hosted at the BC Children’s Hospital Research Institute (Vancouver, Canada). At admission, trained study nurses systematically collected data on clinical, social and demographic variables. Following discharge, field officers contacted caregivers at 2 and 4 months by phone, and in-person at 6 months, to determine vital status, post-discharge health-seeking, and readmission details. Verbal autopsies were conducted for children who had died following discharge. Data Processing Methods: For this analysis, data from both cohorts (0-6 months and 6-60 months) were combined and analysed as a single dataset. We used periods of overlapping enrolment (72% of total enrolment months) between the two cohorts to determine site-specific proportions of children who were 0-6 and 6-60 months of age. These proportions were used to weight the cohorts for the calculation of overall mortality rate. Z-scores were calculated using height and weight. Hematocrit was converted to hemoglobin. Distance to hospital was calculated using latitude and longitude. Extra symptom and diagnosis categories were created based on text field in these two variables. BCS score was created by summing all individual components. Abbreviations: MUAC -mid upper arm circumference wfa – weight for age wfl – weight for length bmi – body mass index lfa – length for age abx - antibiotics hr – heart rate rr – respiratory rate antimal - antimalarial sysbp – systolic blood pressure diasbp – diastolic blood pressure resp – respiratory cap - capillary BCS - Blantyre Coma Scale dist- distance hos - hospital ed - education disch - discharge dis -discharge fu – follow-up pd – post-discharge loc - location materl - maternal Ethics Declaration: This study was approved by the Mbarara University of Science and Technology Research Ethics Committee (No. 15/10-16), the Uganda National Institute of Science and Technology (HS 2207), and the University of British Columbia / Children & Women’s Health Centre of British Columbia Research Ethics Board (H16-02679). This manuscript adheres to the guidelines for STrengthening the Reporting of OBservational studies in Epidemiology (STROBE). Study Protocol & Supplementary Materials: Smart Discharges to improve post-discharge health outcomes in children: A prospective before-after study with staggered implementation, NOTE for restricted files: If you are not yet a CoLab member, please complete our membership application survey to gain access to restricted files within 2 business days. Some files may remain restricted to CoLab members. These files are deemed more sensitive by the file owner and are meant to be shared on a case-by-case basis. Please contact the CoLab coordinator at sepsiscolab@bcchr.ca or visit our website.

Lab Informatics Market Outlook

The global lab informatics market size was valued at approximately USD 3.5 billion in 2023 and is projected to reach USD 6.8 billion by 2032, growing at a Compound Annual Growth Rate (CAGR) of 7.6% during the forecast period. The growth of this market is primarily driven by the increasing adoption of technologically advanced solutions in laboratories across various industries. The need for efficient data management and analytics in research and development is pushing laboratories to integrate informatics solutions, thereby fueling market expansion. In addition, the rising demand for automation in laboratories to enhance productivity and efficiency is significantly contributing to the market's growth trajectory.

One of the pivotal growth factors of the lab informatics market is the escalating demand for laboratory automation. With the rising complexity and volume of data generated in scientific research, laboratories are increasingly seeking automated solutions that can streamline operations and enhance the accuracy of data management. This trend is particularly evident in industries such as life sciences and pharmaceuticals, where the pressure to expedite drug discovery and development processes is immense. Automation in laboratories not only reduces the likelihood of human error but also frees up researchers to focus on critical analytical tasks, thus driving the adoption of lab informatics solutions.

Moreover, the integration of artificial intelligence and machine learning technologies within lab informatics systems is revolutionizing data analytics, providing a significant boost to market growth. These advanced technologies facilitate predictive analytics, allowing researchers to derive novel insights from vast datasets with greater precision and speed. The ability to forecast outcomes and identify trends in data is becoming increasingly valuable in sectors such as environmental testing and chemical industries, prompting more laboratories to invest in informatics solutions that offer these capabilities. Furthermore, advancements in cloud computing are enabling seamless integration and scalability of lab informatics systems, further enhancing their appeal to a broader range of end-users.

The growing emphasis on regulatory compliance and data integrity also serves as a crucial driver for the lab informatics market. Industries such as food and beverage and environmental testing are subject to stringent regulatory standards that necessitate meticulous data documentation and reporting. Lab informatics solutions are instrumental in ensuring compliance by providing robust audit trails, secure data storage, and streamlined reporting functionalities. As regulatory frameworks continue to evolve, the demand for informatics systems that can adapt and comply with these changes is likely to rise, thereby supporting market growth.

In terms of regional outlook, North America is anticipated to hold a prominent share of the lab informatics market owing to the presence of leading pharmaceutical companies and a high level of technology adoption. The region's well-established healthcare infrastructure and robust investment in research and development further contribute to its dominance. Meanwhile, the Asia Pacific region is expected to witness significant growth due to increasing investments in biotechnology and pharmaceutical research. Economic progress and supportive government policies in countries like China and India are creating lucrative opportunities for market expansion in this region. Europe, with its strong focus on innovation and sustainability, also presents promising growth prospects, particularly in sectors like environmental testing and food safety.

Product Type Analysis

When analyzing the lab informatics market by product type, Laboratory Information Management Systems (LIMS) emerge as a pivotal segment. LIMS are extensively utilized across various industries for their ability to efficiently manage laboratory data, from sample tracking to test result management. With the ongoing digital transformation in laboratories, LIMS are increasingly being integrated with other laboratory equipment and software, enhancing their functionality and appeal. The need for robust data management solutions in highly regulated industries such as pharmaceuticals and biotechnology has spurred the demand for LIMS, reinforcing its dominance in the product type segment.

Electronic Lab Notebooks (ELN) constitute another vital segment within the lab informatics market. ELNs are rapidly replacing traditional paper notebooks due to their superior data s

Liquid chromatography–mass spectrometry (LC–MS) metabolomics studies produce high-dimensional data that must be processed by a complex network of informatics tools to generate analysis-ready data sets. As the first computational step in metabolomics, data processing is increasingly becoming a challenge for researchers to develop customized computational workflows that are applicable for LC–MS metabolomics analysis. Ontology-based automated workflow composition (AWC) systems provide a feasible approach for developing computational workflows that consume high-dimensional molecular data. We used the Automated Pipeline Explorer (APE) to create an AWC for LC–MS metabolomics data processing across three use cases. Our results show that APE predicted 145 data processing workflows across all the three use cases. We identified six traditional workflows and six novel workflows. Through manual review, we found that one-third of novel workflows were executable whereby the data processing function could be completed without obtaining an error. When selecting the top six workflows from each use case, the computational viable rate of our predicted workflows reached 45%. Collectively, our study demonstrates the feasibility of developing an AWC system for LC–MS metabolomics data processing.

Articles from a literature search were identified that made an assertion (claim) about the annual rate of U.S. physicians who die of suicide. Data extracted included: article (or resource) type, title, authors, DOI or HTTP URI, publication year, claim (about annual physician suicide rate), data of last access of the article (e.g. for a webpage), and cited articles in support of the claim. Using Nanobench, a Java based end-user tool that allows for browsing and publishing of nanopublications, nanopublications representing the claims were created.

Diagnostic Informatics Market Outlook

The diagnostic informatics market size is projected to grow from USD 2.8 billion in 2023 to USD 5.9 billion by 2032, with a compound annual growth rate (CAGR) of 8.7%. This remarkable growth is driven by several factors, including the increasing adoption of advanced diagnostic technologies and the rising demand for accurate and timely diagnostic solutions. The implementation of digital health initiatives and the integration of artificial intelligence (AI) and machine learning (ML) in diagnostic processes are also contributing to the market's expansion.

One of the primary growth factors for the diagnostic informatics market is the increasing prevalence of chronic diseases worldwide. Conditions such as cancer, cardiovascular diseases, and diabetes necessitate early and precise diagnosis, thus driving the demand for efficient diagnostic informatics solutions. The aging global population further exacerbates this demand, as older individuals are more susceptible to chronic diseases, thereby emphasizing the need for advanced diagnostic systems. Additionally, increased awareness and emphasis on preventive healthcare are encouraging the adoption of these technologies.

Technological advancements in the healthcare sector are another significant driver of market growth. The integration of AI and ML algorithms enhances the accuracy and efficiency of diagnostic processes, reducing error rates and improving patient outcomes. Moreover, the continuous development of digital imaging and data analytics tools allows for more comprehensive and swift analysis of diagnostic data. These innovations not only streamline diagnostic workflows but also facilitate personalized medicine by enabling precise diagnostics tailored to individual patient profiles.

Government initiatives and funding also play a crucial role in the growth of the diagnostic informatics market. Many governments are investing in healthcare infrastructure and digital health initiatives to improve patient care and outcomes. For instance, initiatives aimed at promoting electronic health records (EHRs) and health information exchanges (HIEs) are supporting the adoption of diagnostic informatics solutions. Additionally, favorable reimbursement policies for diagnostic procedures are encouraging healthcare providers to integrate these systems into their practice.

Regionally, North America is anticipated to dominate the diagnostic informatics market, driven by well-established healthcare infrastructure and significant investment in healthcare IT solutions. Europe follows closely, with substantial growth potential attributed to increasing government support and the rising adoption of advanced diagnostic technologies. The Asia-Pacific region is expected to exhibit the highest growth rate, spurred by the rapid expansion of healthcare facilities and increasing investments in healthcare technology. Emerging economies in Latin America and the Middle East & Africa are also projected to witness steady growth due to improving healthcare infrastructure and rising awareness about digital health solutions.

Product Type Analysis

The diagnostic informatics market is segmented into software, hardware, and services. The software segment is expected to hold the largest market share, owing to the growing adoption of advanced diagnostic software solutions in healthcare facilities. These software solutions include EHR systems, laboratory information systems (LIS), radiology information systems (RIS), and imaging software. The increasing need for efficient data management and analysis in diagnostics is driving the demand for these software solutions. Moreover, the integration of AI and ML in diagnostic software is enhancing the accuracy and speed of diagnostics, further boosting the segment's growth.

The hardware segment, which includes diagnostic devices and imaging equipment, is also experiencing significant growth. The demand for high-quality diagnostic imaging devices such as MRI machines, CT scanners, and ultrasound systems is increasing, driven by the need for accurate and non-invasive diagnostic procedures. Advancements in imaging technology, such as the development of portable and point-of-care devices, are further propelling the growth of the hardware segment. Additionally, the continuous innovation in diagnostic equipment to improve image quality and reduce radiation exposure is contributing to market expansion.

The services segment encompasses a wide range of offerings, including installation, maintenance, training, and consulting services. The g

Bahrain Real Effective Exchange Rate Index: Annual data was reported at 76.000 2000=100 in 2007. This records a decrease from the previous number of 81.700 2000=100 for 2006. Bahrain Real Effective Exchange Rate Index: Annual data is updated yearly, averaging 101.088 2000=100 from Dec 1980 (Median) to 2007, with 28 observations. The data reached an all-time high of 177.039 2000=100 in 1984 and a record low of 76.000 2000=100 in 2007. Bahrain Real Effective Exchange Rate Index: Annual data remains active status in CEIC and is reported by Central Informatics Organization. The data is categorized under Global Database’s Bahrain – Table BH.M011: Nominal and Real Effective Exchange Rate Index: 2000=100.

Aberrant protein phosphorylation plays important roles in cancer-related cell signaling. With the goal of achieving multiplexed, comprehensive, and fully automated relative quantitation of site-specific phosphorylation, we present a simple label-free strategy combining an automated pH/acid-controlled IMAC procedure and informatics-assisted SEMI (sequence, elution time, mass-to-charge, and internal standard) algorithm. The SEMI strategy effectively increased the number of quantifiable peptides more than 4-fold in replicate experiments (from 262 to 1171, p < 0.05, false discovery rate = 0.46%) by using a fragmental regression algorithm for elution time alignment followed by peptide cross-assignment in all LC−MS/MS runs. In addition, the strategy demonstrated good quantitation accuracy (10−12%) for standard phosphoprotein and variation less than 1.9 fold (within 99% confidence range) in proteome scale and reliable linear quantitation correlation (R2 = 0.99) with 4000-fold dynamic concentrations, which was attributed to our reproducible experimental procedure and informatics-assisted peptide alignment tool to minimize system variations. In an attempt to explore metastasis-associated phosphoproteomic alterations in lung cancer, this approach was used to delineate differential phosphoproteomic profiles of a lung cancer metastasis model. Without sample fractionation, the SEMI algorithm enabled quantification of 1796 unique phosphopeptides (false discovery rate = 0.56%) corresponding to 854 phosphoproteins from a series of non-small cell lung cancer lines with varying degrees of in vivo invasiveness. Nearly 40% of the phosphopeptides showed >2-fold change in highly invasive cells; validation of phosphoprotein subsets by Western blotting not only demonstrated the consistency of data obtained by our SEMI strategy but also revealed that such dramatic changes in the phosphoproteome result mostly from translational or post-translational regulation. Mapping of these differentially expressed phosphoproteins in multiple cellular pathways related to cancer invasion and metastasis suggests that the site and degree of phosphorylation might have distinct patterns or functions in the complex process of cancer progression.

According to Cognitive Market Research, the global Biobanking Service market size is USD 3,152.2 million in 2024. It will expand at a compound annual growth rate (CAGR) of 7.50% from 2024 to 2031.

North America held the major market share for more than 40% of the global revenue with a market size of USD 1260.88 million in 2024 and will grow at a compound annual growth rate (CAGR) of 5.7% from 2024 to 2031.
Europe accounted for a market share of over 30% of the global revenue with a market size of USD 945.66 million.
Asia Pacific held a market share of around 23% of the global revenue with a market size of USD 725.01 million in 2024 and will grow at a compound annual growth rate (CAGR) of 9.5% from 2024 to 2031.
Latin America had a market share for more than 5% of the global revenue with a market size of USD 157.61 million in 2024 and will grow at a compound annual growth rate (CAGR) of 6.9% from 2024 to 2031.
Middle East and Africa had a market share of around 2% of the global revenue and was estimated at a market size of USD 63.04 million in 2024 and will grow at a compound annual growth rate (CAGR) of 7.2% from 2024 to 2031.
The Regenerative Medicine held the highest Biobanking Service market revenue share in 2024.

Market Dynamics of Biobanking Service Market

Key Drivers for Biobanking Service Market

Increasing Research and Development Initiatives to Increase the Demand Globally

Increasing research and development (R&D) initiatives are driving the biobanking service market due to their pivotal role in advancing biomedical and clinical research. Biobanks provide critical infrastructure for storing and managing biological samples such as tissues, cells, and fluids, along with associated clinical and genetic data. The demand for biobanking services is growing as researchers and pharmaceutical companies intensify efforts to discover new biomarkers, develop personalized therapies, and conduct large-scale epidemiological studies. Moreover, government initiatives and funding support for research in areas like precision medicine, oncology, and regenerative medicine further propel the market. Biobanks facilitate collaboration between academia, healthcare providers, and biotechnology firms, accelerating the translation of scientific discoveries into clinical applications and improving healthcare outcomes globally.

Advancements in Biorepository Techniques to Propel Market Growth

Advancements in biorepository techniques are pivotal in driving the biobanking service market forward. These techniques encompass improvements in sample processing, storage, retrieval, and quality management within biorepositories. Enhanced automation, robotics, and state-of-the-art cryopreservation technologies are streamlining operations and ensuring the long-term viability of biological samples. Moreover, advancements in data management and integration of sophisticated informatics solutions enable efficient cataloging, tracking, and sharing of biobank data. These innovations not only optimize workflow efficiency but also enhance sample integrity and accessibility, thereby supporting a wide range of biomedical research applications. As the demand for high-quality biospecimens increases across academic, pharmaceutical, and clinical research sectors, biobanks leveraging these advancements are poised to play a crucial role in advancing precision medicine and personalized healthcare solutions.

Restraint Factor for the Biobanking Service Market

High Cost of Equipment to Limit the Sales

The high cost of equipment represents a significant restraint in the biobanking service market. Biobanks require specialized equipment for sample collection, processing, storage, and analysis, all of which can be expensive to procure and maintain. This financial barrier can limit the establishment and expansion of biobanks, particularly in resource-constrained settings or for smaller research institutions. Moreover, ongoing operational costs associated with equipment maintenance, calibration, and compliance with regulatory standards add to the financial burden. The high initial investment and recurring expenses may deter organizations from investing in biobanking infrastructure, thereby restricting the growth of biobanking services globally. Addressing these cost challenges through technological innovations, economies of scale, and collaborative funding models could help mitigate these barriers and foster broader ac...

Key information about Bahrain Unemployment Rate

• Bahrain Unemployment Rate increased to 5.90% in Dec 2020, from the previously reported figure of 4.66% in Dec 2019.
• Bahrain Unemployment Rate is updated yearly, available from Dec 1991 to Dec 2020, with an average rate of 4.15%.
• The data reached an all-time high of 16.40% in Dec 2006 and a record low of 3.34% in Dec 2015.
• The data is reported by reported by Central Informatics Organization.
• In the latest reports, Bahrain Population reached 1.50 million people in Jun 2021.

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