Role Of Big Data & (COPD) Phenotypes And ML Cluster Analyses – Potential Topics For PhD Scholars - Phdassistance
                     Role of Big Data & Chronic 
Obstructive  Pulmonary Disease 
(COPD)  Phenotypes and ML Cluster 
Analyses –  Potential Topics for PhD 
ASn cAchadeomicl aprersesntation by
Dr. Nancy Agnes, Head, Technical Operations, 
Phdassistance  Group www.phdassistance.com
Email: [email protected]
TODAY'S 
DISCUSSION
Outline
In Brief
Introduction
Application of machine learning - Recent research  
Big data - Role in COPD analysisbf:
Conclusion
In-Brief
Chronic obstructive pulmonary disease (COPD), a leading cause of death worldwide, is 
 a heterogeneous and multisystemic condition. Growth and application of Machine 
Learning  (ML) algorithms in Medical Research can potentially help advance this 
classification  procedure. Scope of ML algorithms was explored to identify the 
heterogeneity of certain  conditions. Mathematical models are being developed using 
genomic, transcriptomic, and  proteomic data to predict or differentiate disease 
phenotypes.
Introduction
Chronic obstructive pulmonary disease (COPD), a leading  
cause of death worldwide, is a heterogeneous and  
multisystemic condition.
It includes diseases like asthma, emphysema and chronic  
bronchitis (Nikalaou 2020).
It is marked by persistent respiratory symptoms and restricted  
airflow caused by airway and/or alveolar abnormalities.
Significant exposure to harmful particles or fumes is usually  
the cause of these abnormalities (Corlateanu 2020).
Contd....
To understand this condition better, physicians have classified patients into phenotypes  
based on symptomatic features, including symptom severity and history of exacerbations.
The growth and application of machine learning (ML) algorithms in Medical Research can 
 potentially help advance this classification procedure (Nikalaou 2020).
This review summarizes the use of machine learning algorithms and cluster analyses in
COPD phenotypes.
Application of  
machine  
The last decade has seen substantial growth in
learning - Recent the  use of Machine Learning in Medicine and 
 research Research.
The scope of ML algorithms was explored to identify  
the heterogeneity of certain conditions.
Mathematical models are being developed using  
genomic, transcriptomic, and proteomic data to  
predict or differentiate disease phenotypes (Tang  
2020).
Contd....
COPD phenotypic classification has progressed from the classic phenotypes of  
emphysema, chronic bronchitis, and asthma to a plethora of phenotypes that represent  
the disease's heterogeneity.
Over the last 10 years, new imaging modalities, high-performance systems for protein,  
gene, and metabolite assessment, and integrative approaches to disease classification 
 have contributed to the identification of a variety of phenotypes (O'Brien 2020).
Contd....

Boddulari et al. conducted a Deep Learning and Machine Learning based analysis
using spirometry data to identify the structural phenotypes of COPD.
The study was conducted on 8980 patients and applied techniques like
random  forest and full convolutional network (FCN).
They demonstrated the potential of machine learning approaches to
identify  patients for targeted therapies (Bodduluri 2020).
Contd....
In another study, researchers evaluated the possible clinical clusters in
COPD  patients at two study centres in Brazil.
A total number of 301 patients were included in this study and methods like Ward  
and K-means were applied.
They were able to identify four different clinical clusters in the COPD
population  (Zucchi 2020).
Contd....

Network-based methods have also been used to study biomarkers of COPD.
Sex-specific gene co-expression patterns have been discovered using correlation-  
based network approaches.
PANDA (Passing Attributes between Networks for Data Assimilation) reported sex- 
 specific differential targeting of several genes, with mitochondrial pathways being  
enriched in women (DeMeo 2021).
Big data -  
The application of B ig  DataintheStudy of
Role in COPD heterogenic  conditions is of utmost importance.
 Analysis
Analysis of large amounts of data at once using computing  
techniques can help in better understanding of complex  
diseases like COPD. Genetics, other Omics (e.g.,  
transcriptomics, proteomics, metabolomics, and epigenetics), 
 and imaging are all vital sources of big data in COPD 
study.
 COPD Genetic Research has already produced a large  
amount of Big Data. Another important source of Big Data in  
COPD research is imaging, which is usually done with chest  
CT scans.
Contd....
Network science offers methods for analyzing big data (Silverman 2020). Projects  
like COPD Gene (19,000 lung CT scans of 10,000 people) provide unprecedented  
opportunities to learn from massive medical image sets (Toews 2015).
A research undertaken in England signified the importance of B ig Data and 
Machine  L earning in COPD.
The researchers successfully sub-classified COPD patients into five clusters based  
on the demography, risk of death, comorbidity and exacerbations.
They applied cluster analysis methods on large-scale electronic health record (EHR)  
data (Pikoula 2019).
Future Work The appropriate application of large medical datasets or big  
data and machine learning analysis can play a vital role in the  
improving management of COPD.
The adoption of these techniques can further facilitate the  
classification of individuals with different responses to therapy.
That can also lead to personalized therapy for patients with  
COPD.
To conclude, ML algorithms and big data hold the potential  
to change the prognosis and management of COPD. However, 
 more elaborated research projects are needed to establish the 
 application of these tools.
Contact 
Us
UNITED KINGDOM
+44-1143520021
INDIA
+91-4448137070
EMAIL
[email protected]