According to Cognitive Market Research, the Global Bioinformatics Services Market Size will be USD XX Billion in 2023 and is set to achieve a market size of USD XX Billion by the end of 2031 growing at a CAGR of XX% from 2024 to 2031.

• The global Bioinformatics services Market will expand significantly by XX% CAGR between 2024 and 2031.

• Based on technology, Because of the growing number of platform applications and the need for improved tools for drug development, the bioinformatics platforms segment dominated the market.

• In terms of service type, The sequencing services segment held the largest share and is anticipated to grow over the coming years

• Based on application, The genomic segment dominated the bioinformatics market

• Based on End-user, academic institutes and research centers segment hold the largest share.

• Based on speciality segment, The medical bioinformatics segment holds the large share and is anticipated to expand at a substantial CAGR during the forecast period.

• The North America region accounted for the highest market share in the Global Bioinformatics Services Market. CURRENT SCENARIO OF THE BIOINFORMATICS SERVICES

Driving Factors of the Bioinformatics Services Market

Expansive uses of bioinformatics across multiple sectors is propelling the market's growth.

Several industries, such as the food, bioremediation, agriculture, forensics, and consumer industries, are also using bioinformatics services to improve the quality of their products and supply chain processes. Companies in a variety of sectors are rapidly utilizing bioinformatics services such as data integration, manipulation, lead generation, data management, in silico analysis, and advanced knowledge discovery.

• Bioinformatics Approaches in Food Sciences

In order to meet the needs of food production, food processing, enhancing the quality and nutritional content of food sources, and many other areas, bioinformatics plays a significant role in forecasting and evaluating the intended and undesired impacts of microorganisms on food, genomes, and proteomics research. Furthermore, bioinformatics techniques can be applied to produce crops with high yields and resistance to disease, among other desirable qualities. Additionally, there are numerous databases with information about food, including its components, nutritional value, chemistry, and biology.

Genome Canada is proud to partner with five Institutes where there are five funding pools within this opportunity and Genome Canada is partnering on the Bioinformatics, Computational Biology and Health Data Sciences pool. (Source:https://genomecanada.ca/genome-canada-partners-with-cihr-to-launch-health-research-training-platform-2024-25/)

• Bioinformatics in agriculture

Bioinformatics is becoming more and more crucial in the gathering, storing, and processing of genomic data in the field of agricultural genomics, or agri-genomics. Generally referred to as agri-informatics, some of the various applications of bioinformatics tools and methods in agriculture focus on improving plant resistance against biotic and abiotic stressors as well as enhancing the nutritional quality in depleted soils. Beyond these uses, computer software-assisted gene discovery has enabled researchers to create focused strategies for seed quality enhancement, incorporate extra micronutrients into plants for improved human health, and create plants with phytoremediation potential.

India/UK-based Agri-Genomics startup, Piatrika Biosystems has raised $1.2 Million in a seed round led by Ankur Capital. The company is bringing sustainable seeds and agri chemicals to market faster and cheaper. The investment will be used to build a strong Product Development team, also for more profound research, and to accelerate the productionising and commercialization of MVP. (Source:https://pressroom.icrisat.org/agri-genomics-startup-piatrika-biosystems-raises-12-million-in-seed-funding-led-by-ankur-capital)

This expansion in the application areas of bioinformatics services is likely to drive the overall market growth. Bioinformatics services such as data integration, manipulation, lead discovery, data management, in silico analysis, and advanced knowledge discovery are increasingly being adopted by companies across various industries.&...

Data for sequence comparison of commamox genomes and genes identified. This dataset is associated with the following publication: Camejo, P., J. Santodomingo, K. McMahon, and D. Noguera. Genome-enabled insights into the ecophysiology of the comammox bacterium Ca. Nitrospira nitrosa. ENVIRONMENTAL SCIENCE & TECHNOLOGY. American Chemical Society, Washington, DC, USA, 2(5): 1-16, (2017).

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Bioinformatics Market Size 2025-2029

The bioinformatics market size is forecast to increase by USD 15.98 billion at a CAGR of 17.4% between 2024 and 2029.

The market is experiencing significant growth, driven by the reduction in the cost of genetic sequencing and the development of advanced bioinformatics tools for Next-Generation Sequencing (NGS) technologies. These advancements have led to an increase in the volume and complexity of genomic data, necessitating the need for sophisticated bioinformatics solutions. However, the market faces challenges, primarily the shortage of trained laboratory professionals capable of handling and interpreting the vast amounts of data generated. This skills gap can hinder the effective implementation and utilization of bioinformatics tools, potentially limiting the market's growth potential.
Companies seeking to capitalize on market opportunities must focus on addressing this challenge by investing in training programs and collaborating with academic institutions. Additionally, data security, data privacy, and regulatory compliance are crucial aspects of the market, ensuring the protection and ethical use of sensitive biological data. Partnerships with technology providers and service organizations can help bridge the gap in expertise and resources, enabling organizations to leverage the power of bioinformatics for research and development, diagnostics, and personalized medicine applications.

What will be the Size of the Bioinformatics Market during the forecast period?

Explore in-depth regional segment analysis with market size data - historical 2019-2023 and forecasts 2025-2029 - in the full report.
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The market is experiencing significant growth, driven by the increasing demand for precision medicine and the exploration of complex biological systems. Structural variation and gene regulation play crucial roles in gene networks and biological networks, necessitating advanced tools for SNP genotyping and statistical analysis. Precision medicine relies on the identification of mutations and biomarkers through mutation analysis and biomarker validation.
Metabolic networks, protein microarrays, CDNA microarrays, and RNA microarrays contribute to the discovery of new insights in evolutionary biology and conservation biology. The integration of these technologies enables a comprehensive understanding of gene regulation, gene networks, and metabolic pathways, ultimately leading to the development of novel therapeutics. Protein-protein interactions and signal transduction pathways are essential in understanding protein networks and metabolic pathways. Ontology mapping and predictive modeling facilitate data warehousing and data analytics in this field.

How is this Bioinformatics Industry segmented?

The bioinformatics industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Application

  Molecular phylogenetics
  Transcriptomic
  Proteomics
  Metabolomics


Product

  Platforms
  Tools
  Services


End-user

  Pharmaceutical and biotechnology companies
  CROs and research institutes
  Others


Geography

  North America

    US
    Canada
    Mexico


  Europe

    France
    Germany
    Italy
    UK


  APAC

    China
    India
    Japan


  Rest of World (ROW)

By Application Insights

The molecular phylogenetics segment is estimated to witness significant growth during the forecast period. In the dynamic and innovative realm of bioinformatics, various technologies and techniques are shaping the future of research and development. Molecular phylogenetics, a significant branch of bioinformatics, employs molecular data to explore the evolutionary connections among species, offering enhanced insights into the intricacies of life. This technique has been instrumental in numerous research domains, such as drug discovery, disease diagnosis, and conservation biology. For instance, it plays a pivotal role in the study of viral evolution. By deciphering the molecular data of distinct virus strains, researchers can trace their evolutionary history and unravel their origins and transmission patterns.

Furthermore, the integration of proteomic technologies, network analysis, data integration, and systems biology is expanding the scope of bioinformatics research and applications. Bioinformatics services, open-source bioinformatics, and commercial bioinformatics software are vital components of the market, catering to the diverse needs of researchers, industries, and institutions. Bioinformatics databases, including sequence databases and bioinformatics algorithms, are indispensable resources for storing, accessing, and analyzing biological data. In the realm of personalized medicine and drug di

Leveraging prior viral genome sequencing data to make predictions on whether an unknown, emergent virus harbors a ‘phenotype-of-concern’ has been a long-sought goal of genomic epidemiology. A predictive phenotype model built from nucleotide-level information alone is challenging with respect to RNA viruses due to the ultra-high intra-sequence variance of their genomes, even within closely related clades. We developed a degenerate k-mer method to accommodate this high intra-sequence variation of RNA virus genomes for modeling frameworks. By leveraging a taxonomy-guided ‘group-shuffle-split’ cross validation paradigm on complete coronavirus assemblies from prior to October 2018, we trained multiple regularized logistic regression classifiers at the nucleotide k-mer level. We demonstrate the feasibility of this method by finding models accurately predicting withheld SARS-CoV-2 genome sequences as human pathogens and accurately predicting withheld Swine Acute Diarrhea Syndrome coronavirus (...

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.

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.

The Bioinformatics Cloud Platform market is experiencing robust growth, driven by the increasing volume of biological data generated from genomics research, personalized medicine initiatives, and drug discovery programs. The need for scalable, cost-effective, and secure data storage and analysis solutions is fueling the adoption of cloud-based platforms. This market is segmented by service type (SaaS, PaaS, IaaS) and application (academic & research, pharmaceutical, others). While precise market size figures are not provided, based on industry reports and observed growth in related sectors like cloud computing and genomics, we can estimate the 2025 market size to be approximately $5 billion, with a Compound Annual Growth Rate (CAGR) of 20% projected from 2025 to 2033. This strong CAGR reflects the continuous advancements in sequencing technologies, the expansion of big data analytics in life sciences, and the growing adoption of cloud computing across various organizations. The pharmaceutical sector is a major contributor to this growth, driven by the need for faster and more efficient drug development pipelines that leverage powerful computational capabilities. Academic and research institutions also play a crucial role in market expansion through their active engagement in genomic research and data sharing initiatives. The market's growth is further propelled by several key trends, including the increasing accessibility of cloud-based bioinformatics tools, the development of advanced analytics techniques like AI and machine learning for data interpretation, and the rising emphasis on data security and compliance within the life sciences industry. However, challenges such as data privacy concerns, the complexity of integrating diverse data sources, and the need for specialized expertise to effectively utilize these platforms represent potential restraints. Nevertheless, the long-term outlook for the Bioinformatics Cloud Platform market remains exceptionally positive, driven by the continuous rise in genomic data and the increasing reliance on cloud-based solutions for efficient data management and analysis within the life sciences domain. Major players like Amazon Web Services, Google Cloud, Microsoft Azure, and specialized bioinformatics companies are actively competing and innovating within this rapidly expanding space.

Bioinformatics Market Outlook

According to our latest research, the global bioinformatics market size reached USD 16.2 billion in 2024, reflecting robust industry momentum. The market is exhibiting a healthy compound annual growth rate (CAGR) of 13.1% and is projected to attain a value of USD 42.7 billion by 2033. This vigorous expansion is driven by the rapid integration of computational tools in life sciences, accelerating advancements in genomics, proteomics, and drug discovery. The increasing demand for personalized medicine and the surge in big data analytics within biological research are pivotal growth factors shaping the bioinformatics landscape.

One of the principal growth factors fueling the bioinformatics market is the explosive rise in genomics research, particularly in the context of next-generation sequencing (NGS) technologies. The cost of sequencing has plummeted over the past decade, making large-scale genomic projects more accessible to both public and private sector entities. This democratization of sequencing technology has led to a significant influx of biological data, necessitating sophisticated bioinformatics tools for analysis, interpretation, and storage. The development of cloud-based bioinformatics platforms further enables researchers to manage and analyze vast datasets efficiently, fostering greater collaboration and innovation in genomics-driven healthcare, agriculture, and environmental sciences.

Another critical driver is the increasing adoption of bioinformatics in drug discovery and development. Pharmaceutical and biotechnology companies are leveraging bioinformatics solutions to accelerate target identification, drug candidate screening, and biomarker discovery. The integration of artificial intelligence (AI) and machine learning algorithms within bioinformatics workflows is enhancing the predictive accuracy of drug response models and facilitating the identification of novel therapeutic targets. This not only shortens the drug development lifecycle but also reduces costs and improves the likelihood of clinical success. As precision medicine gains traction, bioinformatics is becoming indispensable in tailoring treatments based on individual genetic profiles, further propelling market growth across the healthcare sector.

The expanding application of bioinformatics beyond human health is another significant growth factor. In agriculture, bioinformatics is instrumental in crop improvement, pest resistance, and livestock management through the analysis of genomic and phenotypic data. Environmental biotechnology also benefits from bioinformatics in monitoring biodiversity, tracking pathogen outbreaks, and assessing ecosystem health. Moreover, forensic biotechnology utilizes bioinformatics for DNA profiling and criminal investigations. These diverse applications underscore the versatility and critical importance of bioinformatics across multiple sectors, driving sustained investment and innovation in the market.

From a regional perspective, North America continues to dominate the global bioinformatics market, accounting for the largest revenue share in 2024. This leadership is attributed to the presence of major industry players, significant government funding for genomics research, and a well-established healthcare infrastructure. Europe follows closely, supported by strong academic research and collaborative initiatives such as the European Bioinformatics Institute. Meanwhile, the Asia Pacific region is witnessing the fastest growth, fueled by rising investments in life sciences, expanding biotechnology industries, and increasing adoption of digital health solutions. Latin America and the Middle East & Africa are also emerging as promising markets, albeit at a comparatively nascent stage, driven by growing awareness and infrastructural improvements.

Product & Service Analysis

The bioinformatics market by product & service is segmented into software, hardware, and services, each playing a pivotal role in driving the

Over the past year, biology educators and staff at the U.S. Department of Energy Systems Biology Knowledgebase (KBase) initiated a collaborative effort to develop a curriculum for bioinformatics education. KBase is a free web-based platform where anyone can conduct sophisticated and reproducible bioinformatic analyses via a graphical user interface. Here, we demonstrate the utility of KBase as a platform for bioinformatics education, and present a set of modular, adaptable, and customizable instructional units for teaching concepts in Genomics, Metagenomics, Pangenomics, and Phylogenetics. Each module contains teaching resources, publicly available data, analysis tools, and Markdown capability, enabling instructors to modify the lesson as appropriate for their specific course. We present initial student survey data on the effectiveness of using KBase for teaching bioinformatic concepts, provide an example case study, and detail the utility of the platform from an instructor’s perspective. Even as in-person teaching returns, KBase will continue to work with instructors, supporting the development of new active learning curriculum modules. For anyone utilizing the platform, the growing KBase Educators Organization provides an educators network, accompanied by community-sourced guidelines, instructional templates, and peer support, for instructors wishing to use KBase within a classroom at any educational level–whether virtual or in-person.

The Network for Integrating Bioinformatics into Life Sciences Education (NIBLSE) seeks to promote the use of bioinformatics and data science as a way to teach biology.

Background: Attribution to the original contributor upon reuse of published data is important both as a reward for data creators and to document the provenance of research findings. Previous studies have found that papers with publicly available datasets receive a higher number of citations than similar studies without available data. However, few previous analyses have had the statistical power to control for the many variables known to predict citation rate, which has led to uncertain estimates of the "citation benefit". Furthermore, little is known about patterns in data reuse over time and across datasets. Method and Results: Here, we look at citation rates while controlling for many known citation predictors, and investigate the variability of data reuse. In a multivariate regression on 10,555 studies that created gene expression microarray data, we found that studies that made data available in a public repository received 9% (95% confidence interval: 5% to 13%) more citations than similar studies for which the data was not made available. Date of publication, journal impact factor, open access status, number of authors, first and last author publication history, corresponding author country, institution citation history, and study topic were included as covariates. The citation benefit varied with date of dataset deposition: a citation benefit was most clear for papers published in 2004 and 2005, at about 30%. Authors published most papers using their own datasets within two years of their first publication on the dataset, whereas data reuse papers published by third-party investigators continued to accumulate for at least six years. To study patterns of data reuse directly, we compiled 9,724 instances of third party data reuse via mention of GEO or ArrayExpress accession numbers in the full text of papers. The level of third-party data use was high: for 100 datasets deposited in year 0, we estimated that 40 papers in PubMed reused a dataset by year 2, 100 by year 4, and more than 150 data reuse papers had been published by year 5. Data reuse was distributed across a broad base of datasets: a very conservative estimate found that 20% of the datasets deposited between 2003 and 2007 had been reused at least once by third parties. Conclusion: After accounting for other factors affecting citation rate, we find a robust citation benefit from open data, although a smaller one than previously reported. We conclude there is a direct effect of third-party data reuse that persists for years beyond the time when researchers have published most of the papers reusing their own data. Other factors that may also contribute to the citation benefit are considered.We further conclude that, at least for gene expression microarray data, a substantial fraction of archived datasets are reused, and that the intensity of dataset reuse has been steadily increasing since 2003.

The dataset consists of 1763 observations, each representing a unique patient, and 12 different attributes associated with heart disease. This dataset is a critical resource for researchers focusing on predictive analytics in cardiovascular diseases.

Variables Overview: 1. Age: A continuous variable indicating the age of the patient. 2. Sex: A categorical variable with two levels ('Male', 'Female'), indicating the gender of the patient. 3. CP (Chest Pain type): A categorical variable describing the type of chest pain experienced by the patient, with categories such as 'Asymptomatic', 'Atypical Angina', 'Typical Angina', and 'Non-Angina'. 4. TRTBPS (Resting Blood Pressure): A continuous variable indicating the resting blood pressure (in mm Hg) on admission to the hospital. 5. Chol (Serum Cholesterol): A continuous variable measuring the serum cholesterol in mg/dl. 6. FBS (Fasting Blood Sugar): A binary variable where 1 represents fasting blood sugar > 120 mg/dl, and 0 otherwise. 7. Rest ECG (Resting Electrocardiographic Results): Categorizes the resting electrocardiographic results of the patient into 'Normal', 'ST Elevation', and other categories. 8. Thalachh (Maximum Heart Rate Achieved): A continuous variable indicating the maximum heart rate achieved by the patient. 9. Exng (Exercise Induced Angina): A binary variable where 1 indicates the presence of exercise-induced angina, and 0 otherwise. 10. Oldpeak (ST Depression Induced by Exercise Relative to Rest): A continuous variable indicating the ST depression induced by exercise relative to rest. 11. Slope (Slope of the Peak Exercise ST Segment): A categorical variable with levels such as 'Flat', 'Up Sloping', representing the slope of the peak exercise ST segment. 14. Target: A binary target variable indicating the presence (1) or absence (0) of heart disease.

Descriptive Statistics: The patients' age ranges from 29 to 77 years, with a mean age of approximately 54 years. The resting blood pressure spans from 94 to 200 mm Hg, and the average cholesterol level is about 246 mg/dl. The maximum heart rate achieved varies widely among patients, from 71 to 202 beats per minute.

Importance for Research: This dataset provides a comprehensive view of various factors that could potentially be linked to heart disease, making it an invaluable resource for developing predictive models. By analyzing relationships and patterns within these variables, researchers can identify key predictors of heart disease and enhance the accuracy of diagnostic tools. This could lead to better preventive measures and treatment strategies, ultimately improving patient outcomes in the realm of cardiovascular health

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.

The High-Performance Computing (HPC), data analysis, storage, and management market within the life sciences sector is experiencing robust growth, projected to reach $29.46 billion in 2025 and exhibiting a Compound Annual Growth Rate (CAGR) of 14.4% from 2025 to 2033. This expansion is driven by several key factors. Firstly, the increasing volume and complexity of biological data generated through genomics, proteomics, and other 'omics' research necessitates powerful HPC solutions for analysis and interpretation. Secondly, advancements in artificial intelligence (AI) and machine learning (ML) are accelerating drug discovery and development, demanding substantial computing power and sophisticated data management capabilities. Thirdly, the rising prevalence of chronic diseases and the increasing focus on personalized medicine further fuel the demand for advanced analytical tools and efficient data storage solutions. Major players like Dell Technologies, AMD, Cray, Cisco, IBM, Intel, Lenovo, and Hewlett Packard are actively competing in this space, constantly innovating to meet the evolving needs of life science researchers and organizations. The market segmentation is likely diverse, encompassing various software and hardware solutions. While specific segment breakdowns are unavailable, it's reasonable to anticipate strong growth in cloud-based HPC solutions, given the scalability and cost-effectiveness they offer. Similarly, specialized data storage solutions designed for handling massive biological datasets, along with advanced analytics platforms incorporating AI/ML capabilities, are expected to be high-growth segments. Geographic distribution is likely uneven, with North America and Europe initially dominating due to the concentration of major pharmaceutical companies and research institutions. However, growth in Asia-Pacific is anticipated to accelerate in the coming years due to increasing investments in life sciences research and infrastructure in that region. Restraints to growth could include the high initial investment costs associated with HPC infrastructure and the need for specialized expertise in managing and analyzing complex datasets. However, the substantial returns on investment in terms of accelerated drug discovery and improved patient outcomes will likely outweigh these challenges, ensuring continued market expansion.

The bioinformatics software and services market is experiencing robust growth, driven by the exponential increase in biological data generated through advancements in genomics, proteomics, and other life sciences. The market's expansion is fueled by the rising need for efficient data analysis and interpretation to accelerate drug discovery, personalize medicine, and improve healthcare outcomes. Key drivers include the increasing adoption of cloud-based solutions, the development of sophisticated algorithms for complex data analysis, and the growing demand for precise diagnostics and targeted therapies. While the market is highly competitive, with established players like Cisco, IBM, and Microsoft alongside specialized companies such as Medtronic and Capsule Technologies, opportunities exist for innovative solutions that offer superior data integration, visualization, and analytical capabilities. The market is segmented by software type (e.g., sequence alignment, genome assembly, phylogenetic analysis), service type (e.g., data analysis, consulting, training), and end-user (e.g., pharmaceutical companies, research institutions, hospitals). We project a substantial market size with a Compound Annual Growth Rate (CAGR) reflecting the significant investment in research and development within the life sciences sector and the expanding application of bioinformatics across various fields. The forecast period (2025-2033) shows continued market expansion, driven by factors such as the decreasing cost of genome sequencing, the growing adoption of big data analytics in healthcare, and increased government funding for biomedical research. However, challenges remain, including data security and privacy concerns, the need for skilled bioinformaticians, and the complexity of integrating diverse data sources. Successful companies will be those that can effectively address these challenges by providing secure, user-friendly, and scalable solutions that cater to the specific needs of various user segments. The North American and European markets currently hold significant shares, yet emerging economies are showing promising growth potential, presenting opportunities for expansion. This growth trajectory necessitates strategic partnerships, continuous innovation, and a strong focus on data management and analytics to remain competitive in this dynamic market.

International Journal of Engineering and Advanced Technology Publication fee - ResearchHelpDesk - International Journal of Engineering and Advanced Technology (IJEAT) is having Online-ISSN 2249-8958, bi-monthly international journal, being published in the months of February, April, June, August, October, and December by Blue Eyes Intelligence Engineering & Sciences Publication (BEIESP) Bhopal (M.P.), India since the year 2011. It is academic, online, open access, double-blind, peer-reviewed international journal. It aims to publish original, theoretical and practical advances in Computer Science & Engineering, Information Technology, Electrical and Electronics Engineering, Electronics and Telecommunication, Mechanical Engineering, Civil Engineering, Textile Engineering and all interdisciplinary streams of Engineering Sciences. All submitted papers will be reviewed by the board of committee of IJEAT. Aim of IJEAT Journal disseminate original, scientific, theoretical or applied research in the field of Engineering and allied fields. dispense a platform for publishing results and research with a strong empirical component. aqueduct the significant gap between research and practice by promoting the publication of original, novel, industry-relevant research. seek original and unpublished research papers based on theoretical or experimental works for the publication globally. publish original, theoretical and practical advances in Computer Science & Engineering, Information Technology, Electrical and Electronics Engineering, Electronics and Telecommunication, Mechanical Engineering, Civil Engineering, Textile Engineering and all interdisciplinary streams of Engineering Sciences. impart a platform for publishing results and research with a strong empirical component. create a bridge for a significant gap between research and practice by promoting the publication of original, novel, industry-relevant research. solicit original and unpublished research papers, based on theoretical or experimental works. Scope of IJEAT International Journal of Engineering and Advanced Technology (IJEAT) covers all topics of all engineering branches. Some of them are Computer Science & Engineering, Information Technology, Electronics & Communication, Electrical and Electronics, Electronics and Telecommunication, Civil Engineering, Mechanical Engineering, Textile Engineering and all interdisciplinary streams of Engineering Sciences. The main topic includes but not limited to: 1. Smart Computing and Information Processing Signal and Speech Processing Image Processing and Pattern Recognition WSN Artificial Intelligence and machine learning Data mining and warehousing Data Analytics Deep learning Bioinformatics High Performance computing Advanced Computer networking Cloud Computing IoT Parallel Computing on GPU Human Computer Interactions 2. Recent Trends in Microelectronics and VLSI Design Process & Device Technologies Low-power design Nanometer-scale integrated circuits Application specific ICs (ASICs) FPGAs Nanotechnology Nano electronics and Quantum Computing 3. Challenges of Industry and their Solutions, Communications Advanced Manufacturing Technologies Artificial Intelligence Autonomous Robots Augmented Reality Big Data Analytics and Business Intelligence Cyber Physical Systems (CPS) Digital Clone or Simulation Industrial Internet of Things (IIoT) Manufacturing IOT Plant Cyber security Smart Solutions – Wearable Sensors and Smart Glasses System Integration Small Batch Manufacturing Visual Analytics Virtual Reality 3D Printing 4. Internet of Things (IoT) Internet of Things (IoT) & IoE & Edge Computing Distributed Mobile Applications Utilizing IoT Security, Privacy and Trust in IoT & IoE Standards for IoT Applications Ubiquitous Computing Block Chain-enabled IoT Device and Data Security and Privacy Application of WSN in IoT Cloud Resources Utilization in IoT Wireless Access Technologies for IoT Mobile Applications and Services for IoT Machine/ Deep Learning with IoT & IoE Smart Sensors and Internet of Things for Smart City Logic, Functional programming and Microcontrollers for IoT Sensor Networks, Actuators for Internet of Things Data Visualization using IoT IoT Application and Communication Protocol Big Data Analytics for Social Networking using IoT IoT Applications for Smart Cities Emulation and Simulation Methodologies for IoT IoT Applied for Digital Contents 5. Microwaves and Photonics Microwave filter Micro Strip antenna Microwave Link design Microwave oscillator Frequency selective surface Microwave Antenna Microwave Photonics Radio over fiber Optical communication Optical oscillator Optical Link design Optical phase lock loop Optical devices 6. Computation Intelligence and Analytics Soft Computing Advance Ubiquitous Computing Parallel Computing Distributed Computing Machine Learning Information Retrieval Expert Systems Data Mining Text Mining Data Warehousing Predictive Analysis Data Management Big Data Analytics Big Data Security 7. Energy Harvesting and Wireless Power Transmission Energy harvesting and transfer for wireless sensor networks Economics of energy harvesting communications Waveform optimization for wireless power transfer RF Energy Harvesting Wireless Power Transmission Microstrip Antenna design and application Wearable Textile Antenna Luminescence Rectenna 8. Advance Concept of Networking and Database Computer Network Mobile Adhoc Network Image Security Application Artificial Intelligence and machine learning in the Field of Network and Database Data Analytic High performance computing Pattern Recognition 9. Machine Learning (ML) and Knowledge Mining (KM) Regression and prediction Problem solving and planning Clustering Classification Neural information processing Vision and speech perception Heterogeneous and streaming data Natural language processing Probabilistic Models and Methods Reasoning and inference Marketing and social sciences Data mining Knowledge Discovery Web mining Information retrieval Design and diagnosis Game playing Streaming data Music Modelling and Analysis Robotics and control Multi-agent systems Bioinformatics Social sciences Industrial, financial and scientific applications of all kind 10. Advanced Computer networking Computational Intelligence Data Management, Exploration, and Mining Robotics Artificial Intelligence and Machine Learning Computer Architecture and VLSI Computer Graphics, Simulation, and Modelling Digital System and Logic Design Natural Language Processing and Machine Translation Parallel and Distributed Algorithms Pattern Recognition and Analysis Systems and Software Engineering Nature Inspired Computing Signal and Image Processing Reconfigurable Computing Cloud, Cluster, Grid and P2P Computing Biomedical Computing Advanced Bioinformatics Green Computing Mobile Computing Nano Ubiquitous Computing Context Awareness and Personalization, Autonomic and Trusted Computing Cryptography and Applied Mathematics Security, Trust and Privacy Digital Rights Management Networked-Driven Multicourse Chips Internet Computing Agricultural Informatics and Communication Community Information Systems Computational Economics, Digital Photogrammetric Remote Sensing, GIS and GPS Disaster Management e-governance, e-Commerce, e-business, e-Learning Forest Genomics and Informatics Healthcare Informatics Information Ecology and Knowledge Management Irrigation Informatics Neuro-Informatics Open Source: Challenges and opportunities Web-Based Learning: Innovation and Challenges Soft computing Signal and Speech Processing Natural Language Processing 11. Communications Microstrip Antenna Microwave Radar and Satellite Smart Antenna MIMO Antenna Wireless Communication RFID Network and Applications 5G Communication 6G Communication 12. Algorithms and Complexity Sequential, Parallel And Distributed Algorithms And Data Structures Approximation And Randomized Algorithms Graph Algorithms And Graph Drawing On-Line And Streaming Algorithms Analysis Of Algorithms And Computational Complexity Algorithm Engineering Web Algorithms Exact And Parameterized Computation Algorithmic Game Theory Computational Biology Foundations Of Communication Networks Computational Geometry Discrete Optimization 13. Software Engineering and Knowledge Engineering Software Engineering Methodologies Agent-based software engineering Artificial intelligence approaches to software engineering Component-based software engineering Embedded and ubiquitous software engineering Aspect-based software engineering Empirical software engineering Search-Based Software engineering Automated software design and synthesis Computer-supported cooperative work Automated software specification Reverse engineering Software Engineering Techniques and Production Perspectives Requirements engineering Software analysis, design and modelling Software maintenance and evolution Software engineering tools and environments Software engineering decision support Software design patterns Software product lines Process and workflow management Reflection and metadata approaches Program understanding and system maintenance Software domain modelling and analysis Software economics Multimedia and hypermedia software engineering Software engineering case study and experience reports Enterprise software, middleware, and tools Artificial intelligent methods, models, techniques Artificial life and societies Swarm intelligence Smart Spaces Autonomic computing and agent-based systems Autonomic computing Adaptive Systems Agent architectures, ontologies, languages and protocols Multi-agent systems Agent-based learning and knowledge discovery Interface agents Agent-based auctions and marketplaces Secure mobile and multi-agent systems Mobile agents SOA and Service-Oriented Systems Service-centric software engineering Service oriented requirements engineering Service oriented architectures Middleware for service based systems Service discovery and composition Service level

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Dataset Features

List and describe each column or key feature of the dataset.

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Distribution

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• Data Scientists: For training machine learning models.
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• Businesses: For analysis, insights, or AI development.

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IPLID integrates protein-ligand interaction data from multiple well-known resources, including BindingDB, ChEMBL, DrugBank, GPCRDB, PubChem, LINCS-HMS KinomeScan, and four published kinome assay results. Our database can facilitate projects in machine learning or deep learning-based drug development and other applications by providing integrated data sets appropriate for many research interests. Our database can be utilized for small-scale (e.g. kinases or GPCRs only) and large-scale (e.g. proteome-wide), qualitative or quantitative projects. With its ease of use and straightforward data format, IPLID offers a great educational resource for computer science and data science trainees who lack familiarity with chemistry and biology.

Data statistics

Target (data type) Activities | Unique chemicals | Unique proteins | File name

All (binary) 96318 | 18107 | 3107 | integrated_binary_activity.tsv

All (numerical) 2798365 | 683009 | 5876 | integrated_continuous_activity.tsv

CYP450 (binary) 67552 | 17273 | 47 | integrated_cyp450_binary.tsv

CRT (binary) 4152 | 1219 | 412 | integrated_cancer_related_targets_binary.tsv

CDT (binary) 519 | 349 | 88 | integrated_cardio_targets_binary.tsv

DRT (binary) 4433 | 1325 | 852 | integrated_disease_related_targets_binary.tsv

FDA (binary) 6217 | 1521 | 592 | integrated_fda_approved_targets_binary.tsv

GPCR (binary) 1958 | 545 | 129 | integrated_gpcr_binary.tsv

NR (binary) 1335 | 657 | 264 | integrated_nr_binary.tsv

PDT (binary) 1469 | 674 | 404 | integrated_potential_drug_targets_binary.tsv

TF (binary) 1966 | 998 | 304 | integrated_tf_binary.tsv

*Abbreviations: CYP450 (Cytochrome P450), CRT (Cancer-Related Target), CDT (Cardiovascular Disease candidate Target), DRT (Disease-Related Target), FDA (FDA-approved target), GPCR (G-Protein Coupled Receptor), NR (Nuclear Receptor), PDT (Potential Drug Target), TF (Transcription Factor)

*These protein classifications are from UniProt database and the Human Protein Atlas (https://www.proteinatlas.org/)

IPLID data statistics

Over the past year, biology educators and staff at the U.S. Department of Energy Systems Biology Knowledgebase (KBase) initiated a collaborative effort to develop a curriculum for bioinformatics education. KBase is a free web-based platform where anyone can conduct sophisticated and reproducible bioinformatic analyses via a graphical user interface. Here, we demonstrate the utility of KBase as a platform for bioinformatics education, and present a set of modular, adaptable, and customizable instructional units for teaching concepts in Genomics, Metagenomics, Pangenomics, and Phylogenetics. Each module contains teaching resources, publicly available data, analysis tools, and Markdown capability, enabling instructors to modify the lesson as appropriate for their specific course. We present initial student survey data on the effectiveness of using KBase for teaching bioinformatic concepts, provide an example case study, and detail the utility of the platform from an instructor’s perspective. Even as in-person teaching returns, KBase will continue to work with instructors, supporting the development of new active learning curriculum modules. For anyone utilizing the platform, the growing KBase Educators Organization provides an educators network, accompanied by community-sourced guidelines, instructional templates, and peer support, for instructors wishing to use KBase within a classroom at any educational level–whether virtual or in-person.

The global biological data visualization market is experiencing robust growth, projected to reach $543.9 million in 2025 and exhibiting a Compound Annual Growth Rate (CAGR) of 11.5% from 2025 to 2033. This expansion is fueled by several key factors. The increasing complexity of biological data generated through advanced technologies like next-generation sequencing (NGS), proteomics, and microscopy necessitates sophisticated visualization tools for effective analysis and interpretation. Furthermore, the rising adoption of cloud-based solutions and the growing demand for user-friendly, interactive visualization software are contributing significantly to market growth. The pharmaceutical and biotechnology industries are major drivers, leveraging these tools for drug discovery, development, and personalized medicine initiatives. Academic research institutions also constitute a substantial market segment, relying on these tools for groundbreaking biological research. Competitive landscape analysis reveals key players such as Thermo Fisher Scientific, QIAGEN, and Becton Dickinson leading the market, constantly innovating to cater to the evolving needs of researchers and clinicians. The market's future trajectory promises continued growth, driven by ongoing advancements in biological research and the increasing demand for efficient data management and interpretation solutions. The market segmentation, while not explicitly detailed, is likely diverse, encompassing software solutions, hardware components (such as high-resolution monitors and specialized workstations), and services related to implementation and training. Regional variations will also play a crucial role, with North America and Europe expected to dominate initially due to higher research spending and technological advancements. However, Asia-Pacific and other emerging markets are poised for significant growth, driven by increasing investments in life sciences research and infrastructure. Growth is however likely to be tempered by factors such as the high cost of sophisticated software and the need for specialized expertise in data analysis and interpretation. Nevertheless, the overall outlook for the biological data visualization market remains exceptionally positive, reflecting the crucial role of effective data visualization in advancing biological research and healthcare applications.