Progression of the students in the different exercises of the biological data science courses at the University of Mons, Belgium for the academic year 2018-2019.

Activity of the students was recorded to monitor their individual progression in asynchronous exercises. The courses were taught in flipped classroom by Philippe Grosjean (philippe.grosjean@umons.ac.be) and Guyliann Engels (guyliann.engels@umons.ac.be) the University of Mons. These authors designed almost all the teaching material, the exercises, and the related software.

How to use these data?

The README file provides detailed information on the purpose, collection and management of the data. The data are presented in tabular format in CSV files. Metadata in the `datapackage.json` document the different tables and their fields. It is in the Frictionless data format (https://frictionlessdata.io). You can get a view of a part of these metadata by uploading the file `datapackage.json` into the inline data package creator at https://create.frictionlessdata.io. There is a large set of libraries and tools for different programming languages available at https://frictionlessdata.io/tooling/libraries/. Otherwise, any CSV library should import the data in your favourite software. Please, note that encoding is UTF8. For R, the {learnitdown} package provides specific functions to import these data and/or convert them in a SQLite database (https://www.sciviews.org/learnitdown/).

For any question, send an email at sdd@sciviews.org.

The size of the Computational Biology Industry market was valued at USD XX Million in 2023 and is projected to reach USD XXX Million by 2032, with an expected CAGR of 13.33% during the forecast period. The computational biology industry is booming, driven by the growth in volumes of biological data generated by advancing genomics, proteomics, and systems biology. It involves an interdisciplinary approach that links biology, computer science, and mathematics to analyze complicated biological systems and processes-deemed indispensable for drug discovery, personalized medicine, and agricultural biotechnology. The rising incidence of chronic diseases necessitates targeted therapies and precise diagnostics, thereby becoming a key driver for market growth. The tools of computational biology, which include bioinformatics software, machine learning algorithms, and modeling simulations, enable the extraction of meaningful insights from vast datasets, accelerating the pace of scientific discovery. Technological advancements are further enhancing the functionality of computational biology. The way biological data is interpreted in terms of analysis is undergoing a fundamental shift with AI and machine learning being increasingly integrated in data analysis. Moreover, cloud computing makes it easy for researchers to share data as well as collaborate, making innovation in this field flourish. Geographical center, North America, strong existence of research institutions, biotechnology firms, and investments by funding in life sciences research. Asia-Pacific is emerging, with increased investments in the healthcare and biotechnology sectors and growing importance of personalized medicine. Essentially, the overall industry of computational biology would seem to have excellent chances for sustained expansion based on the further advancing nature of technology, be it a need to gain a clearer sense of incredible data sizes or the overall emphasis to expand focus around precision health solutions. Biological science continually advancing, through computation will unlock new sights, it will be driving an innovation engine across every single domain of healthcare delivery services. Recent developments include: February 2023: The Centre for Development of Advanced Computing (C-DAC) launched two software tools critical for research in life sciences. Integrated Computing Environment, one of the products, is an indigenous cloud-based genomics computational facility for bioinformatics that integrates ICE-cube, a hardware infrastructure, and ICE flakes. This software will help securely store and analyze petascale to exascale genomics data., January 2023: Insilico Medicine, a clinical-stage, end-to-end artificial intelligence (AI)-driven drug discovery company, launched the 6th generation Intelligent Robotics Lab to accelerate its AI-driven drug discovery. The fully automated AI-powered robotics laboratory performs target discovery, compound screening, precision medicine development, and translational research.. Key drivers for this market are: Increase in Bioinformatics Research, Increasing Number of Clinical Studies in Pharmacogenomics and Pharmacokinetics; Growth of Drug Designing and Disease Modeling. Potential restraints include: Lack of Trained Professionals. Notable trends are: Industry and Commercials Sub-segment is Expected to hold its Highest Market Share in the End User Segment.

Progression of the students in the different exercises of the biological data science courses at the University of Mons, Belgium for the academic year 2020-2021.

Activity of the students was recorded to monitor their individual progression in asynchronous exercises. The courses were taught in flipped classroom by Philippe Grosjean (philippe.grosjean@umons.ac.be) and Guyliann Engels (guyliann.engels@umons.ac.be) the University of Mons. These authors designed almost all the teaching material, the exercises, and the related software. The courses were also taught at the Campus Charleroi by Raphaël Conotte (raphael.conotte@umons.ac.be) that also contributed to a part of the learnr exercises and of the inline course.

How to use these data?

The README file provides detailed information on the purpose, collection and management of the data. The data are presented in tabular format in CSV files. Metadata in the `datapackage.json` document the different tables and their fields. It is in the Frictionless data format (https://frictionlessdata.io). You can get a view of a part of these metadata by uploading the file `datapackage.json` into the inline data package creator at https://create.frictionlessdata.io. There is a large set of libraries and tools for different programming languages available at https://frictionlessdata.io/tooling/libraries/. Otherwise, any CSV library should import the data in your favourite software. Please, note that encoding is UTF8. For R, the {learnitdown} package provides specific functions to import these data and/or convert them in a SQLite database (https://www.sciviews.org/learnitdown/).

For any question, send an email at sdd@sciviews.org.

Notable resources for learning and teaching Bioconductor.

The biological data visualization market is experiencing robust growth, driven by the exponential increase in biological data generated through advanced research techniques like genomics, proteomics, and advanced imaging. The market, estimated at $2.5 billion in 2025, is projected to exhibit a Compound Annual Growth Rate (CAGR) of 15% from 2025 to 2033, reaching an estimated $7.8 billion by 2033. This expansion is fueled by several key factors. Firstly, the rising demand for sophisticated data analysis tools to interpret complex biological datasets is a major driver. Secondly, increasing investments in life sciences research from both public and private sectors are contributing to market growth. Finally, the development of user-friendly software and cloud-based solutions is making biological data visualization accessible to a wider range of researchers and institutions. Key players like Thermo Fisher Scientific, QIAGEN, and Agilent Technologies are actively shaping the market landscape through continuous innovation and strategic acquisitions. The market segmentation is largely driven by application (e.g., drug discovery, genomics research, clinical diagnostics) and technology (e.g., 2D/3D visualization software, interactive dashboards, AI-powered analytics). While the North American market currently holds the largest share, strong growth is anticipated in the Asia-Pacific region driven by increasing research activities and infrastructure development. However, challenges such as the high cost of advanced software and the need for skilled professionals to effectively utilize these tools pose potential restraints to market growth. Overcoming these limitations through the development of cost-effective solutions and robust training programs will be crucial for unlocking the full potential of this rapidly evolving market. Furthermore, future growth hinges on the seamless integration of biological data visualization tools with other analytical platforms and the development of innovative visualization techniques to address the complexities of big biological data.

Current and future data analysis needs of National Science Foundation (NSF) Biological Sciences Directorate (BIO) principal investigators (PIs) by the NSF BIO division.

Interactive visual analysis of biological high-throughput data in the context of the underlying networks is an essential task in modern biomedicine with applications ranging from metabolic engineering to personalized medicine. The complexity and heterogeneity of data sets require flexible software architectures for data analysis. Concise and easily readable graphical representation of data and interactive navigation of large data sets are essential in this context. We present BiNA - the Biological Network Analyzer - a flexible open-source software for analyzing and visualizing biological networks. Highly configurable visualization styles for regulatory and metabolic network data offer sophisticated drawings and intuitive navigation and exploration techniques using hierarchical graph concepts. The generic projection and analysis framework provides powerful functionalities for visual analyses of high-throughput omics data in the context of networks, in particular for the differential analysis and the analysis of time series data. A direct interface to an underlying data warehouse provides fast access to a wide range of semantically integrated biological network databases. A plugin system allows simple customization and integration of new analysis algorithms or visual representations. BiNA is available under the 3-clause BSD license at http://bina.unipax.info/.

The global Biological Data Analysis Services market is experiencing robust growth, driven by the increasing volume of biological data generated from high-throughput technologies like next-generation sequencing and advanced imaging techniques. The market's expansion is further fueled by the rising demand for personalized medicine, the growing adoption of bioinformatics tools and cloud-based solutions, and increasing investments in research and development across various sectors including pharmaceutical, biotechnology, and academic research. Key application areas such as biomarker identification, biological modeling, and image analysis are witnessing significant traction, contributing substantially to the market's overall growth. The diverse range of services offered, encompassing statistical data analysis and programming, data visualization, and structural biology, caters to the varied needs of researchers and organizations. Segments like biomarker identification and biological modeling are anticipated to exhibit faster growth compared to others owing to their crucial role in drug discovery and development. North America and Europe currently dominate the market, owing to established research infrastructure and higher healthcare expenditure, but the Asia-Pacific region is projected to show rapid growth due to increasing investments in life sciences research and development, and the expanding biotechnology sector. Competitive landscape analysis reveals a mix of large multinational corporations and specialized service providers. While established players like Eurofins Scientific leverage their extensive network and resources, smaller specialized companies are focusing on niche areas such as specific bioinformatics solutions or particular biological data types, offering innovative and tailored services. This competition is driving innovation and improvement in the quality and accessibility of biological data analysis services. Restraints to market growth include the high cost of advanced analytical tools and the need for specialized expertise to handle complex datasets. However, ongoing technological advancements and the development of user-friendly software are mitigating these challenges. Over the forecast period (2025-2033), continued innovation, particularly in AI and machine learning driven analysis, is expected to further fuel market expansion, leading to improved efficiency and affordability of biological data analysis.

Question Paper Solutions of chapter Biological Data of Bioinformatics, 7th Semester , B.Tech in Computer Science & Engineering (Artificial Intelligence and Machine Learning)

The original contributions presented in the study are included in the article and online through the TAME Toolkit, available at: https://uncsrp.github.io/Data-Analysis-Training-Modules/, with underlying code and datasets available in the parent UNC-SRP GitHub website (https://github.com/UNCSRP). This dataset is associated with the following publication: Roell, K., L. Koval, R. Boyles, G. Patlewicz, C. Ring, C. Rider, C. Ward-Caviness, D. Reif, I. Jaspers, R. Fry, and J. Rager. Development of the InTelligence And Machine LEarning (TAME) Toolkit for Introductory Data Science, Chemical-Biological Analyses, Predictive Modeling, and Database Mining for Environmental Health Research. Frontiers in Toxicology. Frontiers, Lausanne, SWITZERLAND, 4: 893924, (2022).

Biological Data Visualization Market Outlook

The global biological data visualization market size was valued at approximately USD 800 million in 2023 and is expected to reach USD 2.2 billion by 2032, growing at a Compound Annual Growth Rate (CAGR) of 12%. The rising volume of biological data generated through various research activities and the increasing need for advanced analytical tools are key factors driving this market's growth. The integration of artificial intelligence and machine learning in data visualization tools, combined with the growing application of biological data visualization in personalized medicine, are also significant growth drivers.

One of the primary growth factors of the biological data visualization market is the exponential increase in biological data generation due to advancements in high-throughput technologies such as next-generation sequencing (NGS), mass spectrometry, and microarray technology. These technologies produce vast amounts of data that require sophisticated visualization tools for proper analysis and interpretation. Without effective visualization, the potential insights and discoveries within this data may remain untapped, underscoring the market's critical role in modern biological research.

Additionally, the increasing prevalence of complex diseases and the subsequent demand for personalized medicine are fueling the demand for advanced data visualization tools. Personalized medicine relies heavily on the analysis of genetic, proteomic, and other biological data to tailor treatments to individual patients. Effective visualization tools facilitate the interpretation of this complex data, enabling healthcare providers to make informed clinical decisions. This trend is expected to drive substantial growth in the biological data visualization market over the forecast period.

Moreover, there is a growing adoption of cloud-based visualization solutions. Cloud deployment offers significant advantages, including scalability, cost-effectiveness, and accessibility from various locations. This is particularly beneficial for academic and research institutions and smaller biotech companies with limited resources. The integration of cloud computing with advanced visualization tools is expected to further propel market growth, as it allows for more efficient handling and analysis of large datasets.

From a regional perspective, North America currently holds the largest market share, driven by significant investments in research and development, advanced healthcare infrastructure, and high adoption rates of advanced technologies. Europe follows closely, with substantial growth attributed to government support for research initiatives and a strong presence of pharmaceutical and biotech companies. The Asia Pacific region is anticipated to witness the highest CAGR, owing to increasing investments in biotech research, growing healthcare infrastructure, and expanding adoption of advanced technologies in countries like China and India.

In the realm of Life Sciences Analytics, the role of data visualization is becoming increasingly pivotal. Life Sciences Analytics involves the use of data-driven insights to enhance research and development, clinical trials, and patient care. By leveraging advanced visualization tools, researchers and healthcare professionals can gain a deeper understanding of complex biological data, leading to more informed decisions and innovative solutions. The integration of Life Sciences Analytics with data visualization not only facilitates the interpretation of vast datasets but also accelerates the discovery of new patterns and correlations, ultimately advancing the field of personalized medicine.

Component Analysis

The biological data visualization market by component is segmented into software and services. Software solutions constitute the bulk of the market, providing tools that are essential for processing and visually representing complex biological data. These software tools range from basic data plotting programs to advanced systems incorporating machine learning algorithms for predictive modeling. The demand for these tools is driven by their ability to handle large datasets, provide user-friendly interfaces, and offer real-time data visualization capabilities, which are crucial for both research and clinical applications.

In contrast, the services segment, although smaller, plays a crucial role in the market. Services include co

The COVID-19 pandemic has shown that bioinformatics--a multidisciplinary field that combines biological knowledge with computer programming concerned with the acquisition, storage, analysis, and dissemination of biological data--has a fundamental role in scientific research strategies in all disciplines involved in fighting the virus and its variants. It aids in sequencing and annotating genomes and their observed mutations; analyzing gene and protein expression; simulation and modeling of DNA, RNA, proteins and biomolecular interactions; and mining of biological literature, among many other critical areas of research. Studies suggest that bioinformatics skills in the Latin American and Caribbean region are relatively incipient, and thus its scientific systems cannot take full advantage of the increasing availability of bioinformatic tools and data. This dataset is a catalog of bioinformatics software for researchers and professionals working in life sciences. It includes more than 300 different tools for varied uses, such as data analysis, visualization, repositories and databases, data storage services, scientific communication, marketplace and collaboration, and lab resource management. Most tools are available as web-based or desktop applications, while others are programming libraries. It also includes 10 suggested entries for other third-party repositories that could be of use.

Digital Biology Market Outlook

In 2023, the global market size for Digital Biology was estimated at $4.2 billion and is projected to reach $15.6 billion by 2032, growing at a CAGR of 15.4% over the forecast period. The primary growth factor driving this market is the increasing integration of digital tools and technologies in biological research and applications. As the field of biology continues to evolve, the adoption of digital solutions offers unprecedented capabilities in data analysis, simulation, and modeling.

One of the key growth factors for the Digital Biology market is the accelerating pace of technological advancements in bioinformatics and computational biology. The introduction of high-throughput sequencing technologies and advanced data analytics tools has revolutionized the way biological data is collected, processed, and interpreted. This technological progression enables more accurate and faster analysis, which is critical for the development of personalized medicine, advanced research, and innovative biotechnological products. Such advancements are likely to further fuel the demand for digital biology solutions in the coming years.

Another significant factor contributing to the growth of the Digital Biology market is the increasing investment in life sciences research and development. Governments, private organizations, and academic institutions worldwide are investing heavily in R&D activities to discover new drugs, understand complex biological systems, and develop sustainable agricultural practices. These investments are driving the need for sophisticated digital biology tools that can handle complex datasets, model biological processes, and provide insights that were previously unattainable. As funding and support for biological research continue to rise, the demand for digital biology solutions is expected to grow correspondingly.

Moreover, the growing emphasis on personalized medicine and healthcare is also a major driver of market growth. Personalized medicine aims to tailor medical treatment to the individual characteristics of each patient, which requires a deep understanding of genetic, environmental, and lifestyle factors. Digital biology tools provide the necessary computational power and analytical capabilities to process vast amounts of biological data, identify patterns, and predict outcomes. This capability is essential for the development of targeted therapies and precision medicine, making digital biology an indispensable tool in modern healthcare.

Biosimulation Technology is emerging as a transformative force within the digital biology landscape. By enabling the virtual testing and modeling of biological processes, biosimulation technology allows researchers to predict the behavior of biological systems under various conditions. This capability is particularly valuable in drug development, where biosimulation can reduce the time and cost associated with clinical trials by identifying promising drug candidates and optimizing their formulations before they reach the testing phase. Furthermore, biosimulation technology supports the advancement of personalized medicine by simulating how individual patients might respond to specific treatments, thus paving the way for more tailored and effective healthcare solutions.

Regionally, North America holds a significant share of the Digital Biology market, driven by the presence of a robust healthcare infrastructure, a high level of technological adoption, and substantial investment in research and development. The Asia Pacific region is expected to witness the highest growth rate, with a CAGR of 17.1%, due to increasing government initiatives, rising healthcare expenditure, and growing awareness about the benefits of digital biology. Europe also represents a substantial market share, attributed to the strong presence of pharmaceutical companies and research institutes in the region.

Component Analysis

The Digital Biology market is segmented into software, hardware, and services. The software segment holds the largest market share due to the increasing demand for bioinformatics software, data analysis tools, and simulation models. As biological data becomes increasingly complex, the need for sophisticated software solutions capable of handling large datasets and providing accurate results is paramount. These software solutions enable researchers to model biological processes, analyze genetic data, and simulate drug interactions, making them indispensable tools in

Figures and survey data from forthcoming pre-print:

In a 2016 survey of 704 National Science Foundation (NSF) Biological Sciences Directorate principle investigators (BIO PIs), nearly 90% indicated they are currently or will soon be analyzing large data sets. BIO PIs considered a range of computational needs important to their work—including high performance computing (HPC), bioinformatics support, multi-step workflows, updated analysis software, and the ability to store, share, and publish data. Previous studies in the U.S. and Canada emphasized infrastructure needs. However, BIO PIs said the most pressing unmet needs are training in data integration, data management, and scaling analyses for HPC – acknowledging that data science skills will be required to build a deeper understanding of life.

Abstract for poster on using synthetic biology to introduce students to meaningful data mining, analysis, and application to engineering novel biological constructs.

The project aims to improve fisheries-related catch, effort and biological data collection and analysis in the TCI. It also aims to develop capacity to undertake long-term science-driven fishery assessments locally, through the provision of a state-of-the-art fisheries laboratory, and relevant fisheries training. This report does not intend to duplicate the project proposal or other project outputs and is therefore relatively short and focussed primarily on the workshop outcomes.

Presentation made by Lou Gross et al. as part of the "Bringing Research Data to the Ecology Classroom: Opportunities, Barriers, and Next Steps” Session at the Ecological Society of America annual meeting, August 8th, 2017, Portland Oregon

The U.S. Computational Biology Market size was valued at USD 1.62 billion in 2023 and is projected to reach USD 3.83 billion by 2032, exhibiting a CAGR of 13.1 % during the forecasts period.The computational biology market in the United States encompasses the use of mathematical modelling, computational simulation, and data analysis techniques to investigate biological systems and processes. This field plays a crucial role in advancing research across diverse areas such as genomics, proteomics, drug discovery, personalized medicine, and systems biology. Computational biology integrates biological data with computational algorithms to unravel complex biological phenomena and forecast outcomes. In genomics, computational biology is pivotal for analysing extensive genomic data to comprehend genetic variations, gene expression patterns, and their implications for health and disease. This aids in identifying biomarkers, understanding disease mechanisms, and developing targeted therapies. For drug discovery, computational biology accelerates the discovery and refinement of drug candidates through methods like virtual screening, molecular docking, and predictive modelling of drug interactions with biological targets. The market encompasses various stakeholders, including pharmaceutical companies, biotechnology firms, academic research institutions, and government agencies that invest in computational tools and expertise. Advances in machine learning, artificial intelligence, and big data analytics further drive innovation in computational biology, enhancing its capabilities in predictive modelling and personalized healthcare solutions. In the United States, robust regulatory frameworks ensure that computational biology applications meet stringent standards for accuracy, reproducibility, and safety, facilitating their integration into clinical practice and pharmaceutical development. Recent developments include: In May 2023, Genialis unveiled the launch of Genialis Expressions 3.0. The software is designed to accelerate the process of discovering clinical and translational biomarkers. It also focuses on complex biological mechanisms for novel disease treatment methodologies. , In February 2023, Accenture announced its investment in Ocean Genomics, an AI-driven biotechnology company focusing on the development of advanced computational platforms. This deal was planned to support biotechnology companies for the discovery and development of personalized medicines. . Key drivers for this market are: Increasing focus on personalized medicine, where treatments are tailored to individual genetic profiles and health characteristics, drives demand for computational biology tools to analyze genomic data, identify biomarkers, and predict treatment responses.. Potential restraints include: Biological systems are inherently complex, with intricate interactions and dependencies that are challenging to model accurately using computational approaches alone. . Notable trends are: Increasing integration of AI and machine learning algorithms in computational biology for predictive modeling, pattern recognition in biological data, drug repurposing, and identification of novel biomarkers and therapeutic targets..

The idea of a fictional model or a fiction in a model has been the driving force for this dissertation. In order to get a better hold of what it is that models are, I look at two prominent views and ultimately argue for model pluralism, first by analyzing physical models and then a particular case study in Systems Biology. Perhaps what we think models are depends on how we conceive of them, and it seems plausible that their ontology can change over time. The idea of fictions in models became a useful tool for dealing with a tension in discussions around biological sex in humans. It seems that the sex binary is useful in many cases, but that we have no empirical evidence for its existence. It is as though what the science says has been confused by what it assumes. In chapter 2 I argue that the sex binary is a useful fiction that stitches together levels of biological description. In order to do so I look at a model of gene-culture coevolution and a research project that involves model organisms. Finally, in chapter 3, I argue that we treat the sex binary as though it is a credentialed fiction, but that it cannot be. In this case, a credentialed fiction is both an approximation and an explanation, and I argue that the sex binary is neither an approximation of biological sex nor an explanation of it. These two chapters together should help resolve the above tension and let us hold simultaneously that the sex binary is useful while not tracking anything in biology.

Integrated acoustic and trawl surveys are used to assess the distribution, biomass, and biology of Pacific hake along the Pacific coasts of the United States and Canada. Scientists from the Northwest Fisheries Science Center (NWFSC) and Department of Fisheries and Oceans-Canada are responsible for conducting the survey. The survey consists of a series of transects that are oriented generally east-west, and are spaced at a nominal 10-nautical mile interval. Sea depth at the nearshore end of individual transects is typically 50 m; offshore extents are typically at a depth of 1,500 m. Geographical coverage extends from near Morro Bay, CA north to Dixon Entrance. Acoustic data are collected during daylight hours with a Simrad EK60 scientific echo sounder coupled with the ER60 software system. Trawl samples from pelagic and bottom trawls are used to classify the observed backscatter layers to species and size composition and to collect specimens of Pacific hake and other organisms. Analysis of acoustic data involves identification and delineation of backscatter layers that are attributed to Pacific hake. The biomass estimate and length-at-age composition of Pacific hake generated from this survey are used in analysis and management of the stock. This survey is conducted on a biennial basis. There is a firm deadline for producing the biomass estimate in the middle of the December following the survey. Biological data collected from FSCS during catch processing.