Role Of Big Data & (COPD) Phenotypes And ML Cluster Analyses – Potential Topics For PhD Scholars - Phdassistance

1. Role of Big Data & ChronicObstructive Pulmonary Disease (COPD) Phenotypes and ML Cluster Analyses – Potential Topics for PhDScholars An Academic presentationby Dr. Nancy Agnes, Head, Technical Operations,Phdassistance Group www.phdassistance.com Email:info@phdassistance.com

2. TODAY'SDISCUSSION Outline InBrief Introduction Application of machine learning - Recent research Big data - Role in COPDanalysisbf: Conclusion

3. 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 diseasephenotypes.

4. Chronic obstructive pulmonary disease (COPD), a leading cause of death worldwide, is a heterogeneous and multisystemiccondition. It includes diseases like asthma, emphysema and chronic bronchitis(Nikalaou2020). It is marked by persistent respiratory symptoms and restricted airflow caused by airway and/or alveolarabnormalities. Significant exposure to harmful particles or fumes is usually the cause of these abnormalities (Corlateanu2020). Contd.... Introduction

5. To understand this condition better, physicians have classified patients into phenotypes based on symptomatic features, including symptom severity and history ofexacerbations. The growth and application of machine learning (ML) algorithms inMedical Researchcan potentially help advance this classification procedure (Nikalaou2020). Thisreviewsummarizestheuseofmachinelearningalgorithmsandclusteranalysesin COPD phenotypes.

6. The last decade has seen substantial growth in the use of Machine Learningin Medicine andResearch. Application of machine learning - Recent research The scope of ML algorithms was explored to identify the heterogeneity of certainconditions. Mathematical models are being developed using genomic, transcriptomic, and proteomic data to predict or differentiate disease phenotypes (Tang 2020). Contd....

7. COPD phenotypic classification has progressed from the classic phenotypes of emphysema, chronic bronchitis, and asthma to a plethora of phenotypes that represent the disease'sheterogeneity. 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'Brien2020). Contd....

9. Boddulari et al. conductedaDeep Learning and Machine Learning basedanalysis using spirometry data to identify the structural phenotypes ofCOPD. 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 (Bodduluri2020). Contd....

10. In another study, researchers evaluated the possible clinical clusters in COPD patients at two study centres inBrazil. A total number of 301 patients were included in this study and methods like Ward and K-means wereapplied. They were able to identify four different clinical clusters in the COPD population (Zucchi2020). Contd....

12. Network-based methods have also been used to study biomarkers ofCOPD. Sex-specific gene co-expression patterns have been discovered using correlation- based networkapproaches. PANDA (Passing Attributes between Networks for Data Assimilation) reported sex- specific differential targeting of several genes, with mitochondrial pathways being enriched in women (DeMeo2021).

13. The application of BigData in the Studyof heterogenic conditions is of utmostimportance. Big data - Role inCOPD 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 COPDstudy. COPD Genetic Researchhas 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 CTscans. Contd....

14. 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 (Toews2015). A research undertaken in England signified the importance of BigData and Machine LearninginCOPD. The researchers successfully sub-classified COPD patients into five clusters based on the demography, risk of death, comorbidity andexacerbations. They applied cluster analysis methods on large-scale electronic health record (EHR) data (Pikoula2019).

15. The appropriate application of large medical datasets or big data and machine learning analysis can play a vital role in the improving management ofCOPD. The adoption of these techniques can further facilitate the classification of individuals with different responses totherapy. 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 thesetools. FutureWork

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