Create a PowerPoint presentation and then write the research paper
PowerPoint should include the research question, the methods used, the main findings, and why these findings are significant. Research the topic and design a study utilizing and implementing the models in Python.
Idea/Topic: Utilizing machine learning and deep learning models for bioinformatics research in Python. Based on the protocol submitted for this assignment, students will work individually and conduct research related to bioinformatics.
Paper:
4-6 pages protocol highlighting the objective and research question and describing the study plan.
Individual, manuscript-style, double-spaced paper that (a) summarizes the study’s objective and rationale; (b) describes the methodology and study design used to meet the objective; (c) chooses a dataset related to bioinformatics; (d) searches the literature; (e) analyze the data using machine learning and/or deep learning approaches; and (e) interprets and draws inferences from the findings to inform bioinformatics practices and future research. The document, written in Microsoft Word, should include references and relevant tables and figures.
Manuscript-style Report:
Your report should follow the structure of a scientific paper, including sections for Introduction, Literature Review, Methods, Results, and Discussion. Be sure to clearly describe your research question, the machine learning methods used, your results, and what your results mean in the context of your research question. Make sure your report is thoroughly proofread and formatted consistently.
Objectives
This assignment aims to apply the machine learning methods learned in class to a new research topic of your choosing. This project will include data selection and analysis, drafting a manuscript-style report, and preparing a video presentation.
Directions/Data Selection:
Identify and choose appropriate datasets to work with for your research project. Your data can be sourced from public repositories or collected directly if it adheres to ethical guidelines and data privacy regulations. Be sure to provide detailed information about your dataset, including where it came from, how it was collected, and what it represents.
Data Analysis:
Carry out a detailed analysis of your dataset using one or more machine learning methods covered in the course. Be sure to document your preprocessing steps, including how you dealt with missing or outlier data. Your analysis should be thorough and aim to answer your proposed research question.
Tables and Figures:
Illustrate your analysis with tables and figures to summarize your findings. These can include graphs of your data, tables of your results, or diagrams of your machine-learning models. All figures and tables should be appropriately labeled, and any statistical analysis should be clearly explained.
Evaluation:
Your project will be evaluated on the selection and application of machine learning methods, the quality of your data analysis, the clarity and completeness of your report, and the presentation of your findings. Be sure to demonstrate a clear understanding of the machine learning methods you’ve used and to communicate your results and their significance in your report and presentation.
Attached is a guideline for the paper. Explore external databases, like CDC.gov, for datasets that could be adapted to your project. I did provide guidelines for NHANES.
If your topic is highly specific (e.g., triple-negative breast cancer markers), consider narrowing your focus to something more manageable.
