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Big Data Analytics in Bioinformatics - Coggle Diagram
Big Data Analytics in Bioinformatics
What is big data in bioinformatics?
The volume of data is growing fast in bioinformatics research. Big data sources are no longer limited to particle physics experiments or search-engine logs and indexes.
With digitization of all processes and availability of high throughput devices at lower costs, data volume is rising everywhere, including in bioinformatics research
Rising data volume supported by decreasing computing cost and increasing analytics throughput with growing big data technologies.
Availability of high volume of data is helpful for more accurate analytics
Technologies for capturing bio data are becoming cheaper and more effective, such as automated genome sequence, giving rise to this new era of big data in bioinformatics.
Comparison
SQL
row oriented database
Schema–oriented which means the structure of the data should be known in advance to ensure that the data adheres to the schema.
vertically scalable
SQL database can only be scaled by enhancing the horse power of the implementation hardware
costly
NO SQL
column oriented database
Work on both unstructured and unrelated data.
The better solutions are the crossover databases that have elements of both NoSQL and SQL.
Horizontal scalability
NoSQL database doesn't have some feature of the traditional database.
cheaper, faster and safer
MongoDB Design specification
Flexible schema
No formal process
No algorithms
No rules
Data Model Design
Embedded Data Model
may embed related data in a single structure or document.
-These schema are generally known as "denormalized" models, and take advantage of MongoDB's rich documents.
Example:
getting the details of employees in three different documents namely, Personal_details, Contact and, Address, you can embed all the three documents in a single one
Normalized Data Model
describe relationships using references between documents.
can refer the sub documents in the original document, using references.
Applications of Big Data analytics in Bioinformatics (research papers)
1. Personalized Medicine
:pen: Tools are been developed to reduce cost to
improve patient’s safety
and
healthcare quality
:pen: Big data is used to bring
personalized medicine to drug trials and research
, can potentially
reduce costs, allow the right drugs to be work faster, and improve outcomes at lower costs.
:pen: Helps in
faster drug development
with
easier margins
.
2. Industry
:pen: Bioinformatics involves the
application of data-rich computational and informatics methods
to support the scientific study of complex biological problems.
:pen: The prominent field of the bioinformatics visualisation indicates the design of visual image and the executionof effective software tools that
provide an accurate and deep understanding into complex biological data
:pen:
Data integration
systems use the mediation architecture to
provide integrated access to multiple data sources.
:pen: Recent
biological research mainly focuses on
statistical testing
of specific theory , many areas such as systems biology are increasingly bring an open-ended exploratory approach to theory, generation and large scale data analysis to understand exceedingly complex biological phenomena.
Reference
https://analyticsindiamag.com/nosql-vs-sql-database-type-better-big-data-applications/
https://www.tutorialspoint.com/mongodb/mongodb_data_modeling.htm
Big Data Analytics in Bioinformatics: A Machine Learning Perspective:
https://arxiv.org/pdf/1506.05101.pdf
