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Kate Harrison is a senior science writer and is responsible for the creation of custom-written projects. She holds a PhD in virology from the University of Edinburgh. Before working at Technology Networks, she was involved in developing vaccines for neglected tropical diseases, and held a lectureship position teaching immunology.
As the fields of life science and healthcare research progress, they are producing increasingly large amounts of complex data that must be carefully analyzed to produce reliable results. Datasets too large and varied to analyze with conventional methods are known as “big data”, and can often hold the key to leaps forward in scientific knowledge and understanding.
This infographic explores the strategies and techniques required for big data analytics, such as machine learning, and how these data are contributing to advancements across life sciences and healthcare.
Download this infographic to explore:
How big data are generated
The specialized tools and techniques required for analysis
Real-world applications of data analytics
,Big Data and Data Analytics
in Life Science Research
Written by Kate Harrison | Designed by Luiza Augusto
consuming.
Datasets that are too large and varied
Life science and healthcare research produce
to analyze with conventional methods are known
enormous amounts of data. These data must be
as “big data”. These require more sophisticated
analyzed in order to test hypotheses and discover
analysis strategies such as machine learning (ML)
meaningful patterns and relationships. These
and advanced statistical techniques but can
insights can then be used to understand biological
contribute to huge leaps forward. For example,
systems and develop new therapeutics.
during the COVID-19 pandemic, data collected from
The amount of data produced by modern
all over the world allowed public health bodies to
experiments has increased dramatically, making
assess virus transmission, predict outbreaks and
analysis more and more complex and time
develop preventative measures in real time
.1
This infographic will explore how big data is collected and analyzed in life science
research, and how these data are contributing to advancements in areas such as
genomics, proteomics and personalized medicine.
How are big data generated?
Across life sciences, big datasets are generated from a wide range of sources, including structured, semi
structured
and unstructured data.
Sources of big data include:
FILE 01
FILE 02
Experimental and research data
Clinical trial data
Often from high-throughput
Including study protocols,
screenings, for example, drug target
demographics and adverse
identification or small molecule
events.
screenings.
FILE 03
FILE 04
Real-world data
“Omics” data
Data collected outside of controlled
Large datasets generated from
clinical trials, such as patient health
“omics” fields, such as genomics,
records or fitness trackers.
metabolomics and proteomics.
Tools and techniques for analysis
The most important consideration, and one of the main challenges, is handling such large amounts of
information. Ultimately, the goal is to derive useful insights and value from these datasets, which requires
specific tools and techniques. These include:
Specialized frameworks
Deep learning
and databases
A type of ML algorithm that
can be used to identify
Conventional databases
patterns after being trained
and storage systems
on large amounts of data,
can’t handle the size and
e.g., predicting the structure
variety of large datasets, so
and functions of a specifi
specialized programs are
protein sequence, or
needed to store, search and
the effects of mutations
manage petabytes (1,000
on specific protein
terabytes) worth of data.
interactions
.2
Neural language
Sequence alignment
processing (NLP)
and analysis tools
NLP can be used to mine
Bioinformatics tools
large literature databases
that analyze genomic
to look for relationships and
sequences can be used to
produce knowledge graphs,
identify genetic markers
guiding new research and
and compare genetic
therapeutic development
sequences, helping
.3
to identify potential
therapeutic targets.
Data visualization platforms
Visualization is key in big data analysis, as it can turn complex, unfathomable data
into something that can be easily comprehended, interpreted and digested.
In the real world: Applications of data analytics
Although big data is still a relatively recent concept, big data analytics has already contributed to
significant advances in several different fields
Genomics4
Drug discovery and development
• Algorithmic tools can be used to
• Data analytics can be used to sort
assemble and annotate genomes
through large data sets to discover
following next-generation
disease biomarkers and novel
sequencing.
therapeutic targets.
• Allows the alignment of sequences
• Predictive models can screen millions
to a reference sequence to improve
of compounds to identify the best
the accuracy of variant calling and
therapeutics for further testing, based
single nucleotide polymorphism
on protein–protein interactions,
identification
stability, optimal formulations and
critical quality attributes.
.5
• Big data can be used for family
based
and population-based
• Models can analyze huge datasets
analysis, identifying mutations that
from multiple trial centres and
may contribute to disease.
rapidly draw insights for real-time
decision making.
Proteomics
Personalized medicine
• Proteomics experiments generate
• AI-powered systems can analyze
complex, multi-dimensional results
millions of journals and clinical trial
with millions of data points. ML
results, which can be used to suggest
models can predict the analytical
the most beneficial treatment
properties of a given peptide
options for cancer
.8
sequence.
.6
• A patient’s genetic data can
• Clinical proteomics data from
be assessed and compared to
millions of sources can be compiled
databases to study and identify risk
and visualized using knowledge
factors for particular diseases, even
graphs, such as the Clinical
before symptoms manifest
.9
Knowledge Graph, to help inform
• More accurate and exact diagnostics
clinical decision-making
.7
can ensure more precise dosing and
therapeutics, reducing waste.
.9
References
1.
Lv Y, Ma C, Li X, Wu M. Big data driven COVID-19 pandemic crisis management: Potential approach for global health. Arch Med Sci. 2021;17(3):829–837. doi:10.5114/
aoms/133522
2. Yousef M, Allmer J. Deep learning in bioinformatics. Turk J Biol. 2023;47(6):366–382. doi:10.55730/1300-0152.2671
3. Babaiha NS, Elsayed H, Zhang B, et al. A natural language processing system for the efficient updating of highly curated pathophysiology mechanism knowledge
graphs. Art Int Life Sci. 2023;4:100078. doi:10.1016/j.ailsci.2023.100078
4. He KY, Ge D, He MM. Big data analytics for genomic medicine. Int J Mol Sci. 2017;18(2):412. doi:10.3390/ijms18020412
5. Tummala SR, Gorrepati N. AI-driven predictive analytics for drug stability studies. J Pharma Insight Res. 2024;2(2):188–198. doi:10.5281/zenodo.11068492
6. Mann M, Kumar C, Zeng WF, Strauss MT. Artificial intelligence for proteomics and biomarker discovery. Cell Sys. 2021;12(8):759–770. doi:10.1016/j.cels.2021.06.006
7.
Santos A, Colaço AR, Nielsen AB, et al. A knowledge graph to interpret clinical proteomics data. Nat Biotechnol. 2022;40(5):692–702. doi:10.1038/s41587-021-01145-6
8. Park T, Gu P, Kim CH, et al. Artificial intelligence in urologic oncology: the actual clinical practice results of IBM Watson for Oncology in South Korea. Prost Int.
2023;11(4):218–221. doi:10.1016/j.prnil.2023.09.001
9. Badr Y, Kader LA, Shamayleh A. The use of big data in personalized healthcare to reduce inventory waste and optimize patient treatment. J Pers Med. 2024;14(4):383.
doi:10.3390/jpm14040383
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