Supplementary Information
S2. Academic record
Curriculum vitae, typeset as a degree table.
Three degrees, one flask. The bachelor year is the analyte. The master year is the first steep rise on Figure 1 of the article. The doctorate is the interval after . The Gold Medal sits on that rise.
Table S2. Degrees
| Period | Degree | Institution |
|---|---|---|
| Graduated 2019 | BSc - Biology · Biotechnology · BioinformaticsUniversity rank holder | Bangalore University, India |
| 2020 - 2022 | MSc, Applied Genetics & BioinformaticsCGPA 8.57/10 · First Class ExemplaryGold Medal - Vice Chancellor, Bangalore University (2022) · University first rank | Bangalore University, India |
| 2024 - Present | PhD, BioinformaticsParticipant, University of Lethbridge Mentorship Program · Mentoring undergraduate & graduate students, Kovalchuk lab (2025-present) | University of Lethbridge, Alberta, Canada |
S2.1 BSc
Biology, biotechnology, bioinformatics: the three-legged foundation everything else stands on.
Outcome → foundation for graduate specialization in genetics and bioinformatics.
S2.2 MSc
Thesis. Investigation of anti-arthritis compounds from Cinnamomum zeylanicum through in-silico methods
Virtual screening of ~3,000 candidate phytochemicals against rheumatoid-arthritis drug targets via molecular docking - top candidates outperformed standard RA drugs in silico binding affinity. Thesis grade: 9.5/10.
Majors. Genetics, Bioinformatics, Biostatistics, Biotechnology, Plant Biotechnology, Human Genetics.
Techniques. Molecular docking, Virtual / in-silico screening, Applied genetics, Biostatistics.
Computation. Cheminformatics pipelines, Binding-affinity scoring, Structure-activity screening.
Outcome → computation as a tool to accelerate discovery in complex disease biology.
S2.3 PhD
Supervisor: Dr. Igor Kovalchuk
Multi-omics data analysis for rheumatoid-arthritis biomarker discovery - transcriptomics and methylomics integrated via horizontal, vertical, and gene-level integration. Current project: sex-specific endocannabinoid-system biomarkers in RA.
Techniques. Transcriptomics & methylomics analysis, Multi-omics integration (horizontal, vertical, gene-level), Differential gene expression (RNA-seq & microarray), Mendelian randomization, WGCNA co-expression analysis, Hypothesis testing - t-test, ANOVA, chi-square, Kolmogorov-Smirnov, Correlation & regression - Pearson, Spearman, linear regression, Descriptive & distributional statistics.
Computation. R - DESeq2, limma, WGCNA, TwoSampleMR, Python - scikit-learn, pandas, Supervised & unsupervised ML - logistic regression, SVM, XGBoost, random forest, deep neural networks, Feature selection - Boruta, LASSO, Generative adversarial networks (synthetic biomedical data), Shiny web-application development, Data mining - BeautifulSoup, GEO/PubMed extraction.
Current direction → integrative computational biology.
S2.4 Certification
Certified Data Scientist (2023). Professional Certification. Statistical modelling, machine learning, and production analytics workflows.