Big Data is an environment for producing, collaborating, and sharing data aimed at scientific research. It is particularly important for research projects related to bioinformatics and georeferencing. Ideally, each specialty should have its specific database portfolio tailored to diseases of interest.

Big Data also functions as a teaching and training network, aligning with one of the core focuses of Pequeno Príncipe, which is dedicated to health assistance, teaching, and research.

As the field of bioinformatics continues to evolve, the significance of Big Data becomes increasingly apparent. The enormous amount of genetic information available in genomic databases presents both opportunities and challenges. By leveraging advanced data analysis techniques, researchers can extract valuable insights from these vast datasets, uncover patterns, identify genetic variations associated with diseases, accelerate drug discovery processes, and improve treatments.

Machine learning algorithms, for example, can be trained on massive genomic datasets to develop predictive models for disease diagnosis and prognosis. The integration of multiple datasets from diverse sources enables researchers to perform comprehensive analyses and generate detailed biological networks, shedding light on complex biological processes and interactions.

From 2001 to 2024, the volume of data increased exponentially.

Over time, new computing scripts have emerged. The most commonly used databases are mainly from the United States and some European countries. The following graph shows the exponential growth in data volume.

Big Data in GeneBank

The objectives of the Pelé Pequeno Príncipe Research Institute in using Big Data are:

• To introduce students to concepts, practices, methods, and emerging technologies in the areas of bioinformatics, georeferencing, and computational biology. These activities have the collaboration of Professor Mauro Castro, from the Federal University of Paraná.
• To develop or utilize available data mining pipelines to help researchers understand the mechanisms of pediatric diseases and how they spread across different regions.
• To contribute to the development of new diagnostic and treatment methods, as well as the discovery of new medicines through scientific studies.
• To improve the health and quality of life of children and adolescents, not only in Brazil but worldwide, through the results of research projects.

The five independent Big Data groups at the Pelé Pequeno Príncipe Research Institute share similar goals applied to various areas of biology, medicine, and spatial epidemiology, exploring datasets generated by the Institute’s own research or by authors located in other countries.

The Research Institute counts on important partnerships in the development of scientific studies related to Big Data, all of which are nationally and internationally renowned. They include:

• Behring Foundation;
• Pequeno Príncipe Hospital;
• Pequeno Príncipe College, the educational unit of Pequeno Príncipe Complex;
• Federal University of Paraná (UFPR), Brazil;
• Institut de Pharmacologie Moléculaire et Cellulaire (IPMC), France;
• Thales Group, France;
• St. Jude Children’s Research Hospital, USA;
• Federal University of São Paulo, Brazil;
• U.S. Food and Drug Administration (FDA);
• U.S. National Institutes of Health (NIH);
• Georgetown University Medical Center, USA;
• COVID Human Genetic Effort, a global initiative;
• Paraná Sanitation Company (Sanepar), Brazil; and
• Oswaldo Cruz Foundation (Fiocruz), Brazil.

Learn more about some terms related to Big Data.

With information from the Brazilian Ministry of Management and Innovation in Public Services; the Secretariat of Digital Government, of the Brazilian Federal Government; IBM; Pontifical Catholic University of Paraná (PUCPR, abbreviation in Portuguese); the U.S. National Cancer Institute; the U.S. National Library of Medicine; and the American Medical Informatics Association (AMIA).

Algorithm

It is a set of instructions designed to achieve specific computational tasks, making it an essential element in computer programming. Algorithms can be used for various purposes, such as performing calculations or retrieving information from databases.

Artificial Intelligence (AI)

It is a field of computer science that aims to develop systems or algorithms capable of performing tasks that normally require human intelligence. AI uses techniques such as machine learning, natural language processing, and computer vision to acquire knowledge, learn from data, and make autonomous decisions. Its objective is to simulate the capacity for reasoning, adaptation, and problem-solving, contributing to advancements in areas, such as automation, health, and transportation.

Big Data

A large volume of data, which includes not only structured data (such as tables), but also semi-structured or unstructured data, such as images, texts, and sounds. This data has the potential to be explored in an interrelated manner to obtain information. Given the complexity and volume of data, it requires substantial processing power.

Bioinformatics

It is a multidisciplinary field that combines biology, computer science, and statistics. It has evolved rapidly due to the exponential growth of genomic data. Bioinformatics tools can provide useful insights to answer some questions about diseases, including those that affect children and adolescents.

Data-driven

Data-driven processes guide decision-making and organizational planning through data utilization.

Data pipeline

A data pipeline is a system in which raw data from various sources are reunited, transformed, and transferred to a data storage space for analysis. Various types of data pipelines exist, each designed for specific activities, such as batch processing or streaming data.

Data science

It is a multidisciplinary field that involves computational, statistical, and mathematical techniques. It aims to solve complex problems using large datasets.

Georeferencing

In epidemiology and healthcare, georeferencing plays a crucial role in analyzing disease spatial patterns, identifying high-risk areas, and implementing effective interventions. Researchers use georeferenced epidemiological data to visualize the spatial distribution of disease rates and risk factors on maps, identify geographical clusters, and examine potential environmental factors contributing to disease outbreaks.

Health informatics

Health informatics (HI), also known as medical informatics, applies principles of computer and information science to advance life sciences research, health professional education, public health, and patient care. It is a multidisciplinary field that has its focus on health technologies to improve human health and healthcare services. To put this into practice, it uses biomedical data and tools involving computer, cognitive, and social sciences.

Information Technology (IT)

It is a set of technological resources for obtaining, processing, and generating information that is made accessible through communication networks. Through the application of software development resources, it provides functionalities to the hardware, which, integrated into the communications system, offers services to society.

Internet of Things

The expression is used to designate advances in connectivity and interaction between various types of everyday objects equipped with sensors and internet communication capabilities. This creates a common virtual environment and allows remote control, the use of automatic commands, and even integration between them. This connectivity also generates a large amount of data in people’s daily lives.

LGPD

The Brazilian General Data Protection Law (LGPD, abbreviation in Portuguese), Law No. 13,709/2018, establishes rules for the collection, storage, processing, and sharing of personal data in the country, ensuring greater protection for all people.

Machine learning (ML)

Machine learning is a branch of artificial intelligence (AI) and computer science that focuses on using data and algorithms to enable machines to imitate the way that humans learn. ML contributes to decision-making and model optimization processes, gradually improving its accuracy.

The Cancer Genome Atlas Program

The Cancer Genome Atlas (also known as TCGA) is a landmark cancer genomics program that molecularly characterized over 20,000 primary cancers and matched normal samples spanning 33 cancer types. Created in 2006, it has a multidisciplinary team made up of researchers from several institutions. This joint effort between the U.S. National Cancer Institute and the U.S. National Human Genome Research Institute has generated petabytes of genomic, epigenomic, transcriptomic, and proteomic data. These data have contributed to improved diagnosis and treatments and are available to the global research community. To learn more about the TCGA, please click here.

Translational medicine

Translational medicine seeks to bring scientific discoveries into healthcare practice and provide feedback to research based on the main challenges that healthcare professionals face daily. In this way, it provides several clinical benefits, such as new forms of diagnosis and treatment; multidisciplinary knowledge between areas; greater accessibility to healthcare for the community; lower cost and greater effectiveness of medications, which also generates economic value; and support for the development of public policies.