Janaki Kolla
Grand Rapids,United States
Summary
Motivated college student, currently enrolled, friendly worker, and seeking opportunity to apply skills in clinical research, and other computer applications to meet challenging work assignments and support the team. Excellent at communicating, writing, and editing. Keen attention to detail and focused on meeting deadlines. Dependable and punctual with a clean driving record. Work collaboratively with team members and provide excellent service. Recent graduate with foundational knowledge in informatics and hands-on experience gained through academic projects and internships. Demonstrates strong teamwork, problem-solving, and time-management skills. Prepared to start career and make meaningful contributions with commitment and drive.
Overview
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1
Certification
Education
Master of Science in Health informatics and Bioinformatics -
Grand Valley State University
Grand Rapids, MI09.2024
Skills
- Hardworking skills
- Writing and Understanding skills
- Committed to work
- Practical skills
- Time Management
- Excellent Communication
- C, Python, and R
- CSS, HTML, matplotlib, MongoDB, numpy, Pandas, SPSS, SQL, and Tableau
- BLAST, clustalW, EHR, FASTA, KEGG, NCBI, and PDB
Certification
- Certified in the CITI RCR(Responsible Conduct of Research training) program from Grand Valley State University, Michigan.
- Certified in Entrepreneurship, Digital Marketing, Creative Writing, Ethical Hacking, C language, and Model United Nations Workshops in India.
- Also certified in Basics in C Programming and Java Programming courses.
Additional Information
In my master's, I have learned basics about different software tools, such as R language, SQL queries, Redis, Pandas, Numpy, Mongo DB, matplotlib, Regex, NoSQL Database, PyMongo, etc. Have knowledge on computational methods in Bioinformatics like Sequence alignment, BLAST, PDB, Phylogenetic analysis, Structure prediction, Clustering of microarray analysis( Gene Expression data), and Biological databases like Genbank, DDBJ, Swissprot, EMBL, NCBI, KEGG, etc
Coursework
- Introduction to Health and Bioinformatics, The study explores bioinformatics methodologies in the health care systems, which focuses on data representation, system evaluation, and project management practices for effective decision-making using SPSS.
- Knowledge Discovery and Data Mining, Computational strategies, including sequence alignment and phylogenetic trees, are utilized in knowledge discovery for structure prediction in bioinformatics and health using R, and Python.
- Clinical Information Systems, The overview provides a crucial component of laboratory systems, which are integral to the delivery of health care, public health information systems, and contemporary advances.
- Information visualization, User interfaces and techniques for visualization are crucial in enhancing retrieval, knowledge representation, and decision-making processes in various fields using Tableau.
- Health Administration and Services, The overview provides a comprehensive analysis of the current structure and organizational theories within the United States health care system.
- Information Management and Science, Database concepts are crucial in large-scale analysis, enabling efficient retrieval and storage of data for efficient modeling and analysis using MongoDB, Numpy, Matplotlib, and Pandas
Publications
In silico optimization of anthocyanin extraction from gladious flower extracts and evaluation of its antioxidant potential, Chintagunta, A.D., Kumar, N.S.S., Kolla, J., Biomass Conv. Bioref., 2022, https://doi.org/10.1007/s13399-022-03653-0
Projects
Visualizing Insights for Enhanced Diabetes Care and Prevention, The project mainly focuses on analysis of various visualizations regarding the pregnancy women and their health conditions. Analyzed medical dataset comprising health measurements and diabetes test results for a patient population. Employed machine learning models in Python to predict diabetes likelihood based on risk factors. Used various libraries in python to enhance the relationship and trends between health indicators and diabetes outcomes. Identified high-risk demographic groups through integration of statistical analysis. Project aimed to unlock transformative insights from patient data to guide improvements in diabetes screening, prevention, and treatment programs. Heart Disease Classification/Prediction using Machine Learning, The project involves evaluating the machine learning model's ability to categorize cardiac disease using various python libraries like SVC, scikit-learn, XGBClassifier, matplotlib, seaborn as SNS, plotly, and pandas. Used EDA for statistical and visual techniques to understand the variable distribution, correlation identification, and exploration of trends related to heart disease risk factors. Used Python for analysis and modeling of dataset containing cardiac patient health measurements. Engineered features from raw variables including age, cholesterol, blood pressure, BMI, pregnancies, and diabetes. Metrics such as accuracy, precision, recall, and F1 score are used to evaluate models' performance on the testing dataset. Compared performance of Logistic Regression, and Decision tree. Tuned hyperparameters and ensemble modeling to optimize predictive accuracy. Best model able to predict heart disease diagnosis with 95% precision and 97% recall on test set. Quantified feature importances to determine strongest heart disease risk factors. Research aims to build effective screening tool and allow personalized cardiology interventions.
Timeline
Master of Science in Health informatics and Bioinformatics -
Grand Valley State University