Biology subjects

Fuse, M.

Publications and source records attributed to Fuse, M..

3 recordsLinked to original sources

bioRxiv2025

Undergraduate GPA does not predict success in PhD programs for cohorts of MS students at two minority-serving institutions

Master of Science (MS) research training programs funded by organizations such as the NIH, the NSF as well as private corporations represent the potential for significant interventions for student success, especially for marginalized or underrepresented and first-generation students, to bridge the gap between undergraduate studies and doctoral programs. These students often face serious challenges during their undergraduate years, such as navigating unfamiliar academic systems, balancing demanding coursework with work responsibilities, and fulfilling family obligations. These and other systemic pressures can impede their academic progress and opportunities for research experience. We therefore asked how their undergraduate GPA impacted success in the PhD in terms of (i) acceptance, (ii) completion, and (iii) time to degree after participating in a funded MS research training program. We examined data collected at San Francisco State University and California State University, Los Angeles over a 30-year period because they (i) had similar student demographics, (ii) were institutions with strong MS degrees, and (iii) had an infrastructure (established offices) to coordinate various training programs. We found that high GPA did not predict greater success in entering or completing the PhD, or in time to degree. This study, therefore, demonstrates that undergraduates from diverse groups with lower undergraduate GPA levels can benefit from a structured MS research training program in reaching their goal of becoming a PhD scientist. Moreover, it indicates that more holistic approaches in admissions are required, including limiting the use of a GPA below 3.0 as an early filter to eliminate applicants. These MS programs appear to play a crucial role as a bridge to PhD studies and are instrumental in enhancing diversity within STEM fields. We suggest that a strategy for success is to provide infrastructure in the form of a coordinated office housing the training grants as a means of structuring mentorship and professional development for under-represented and first generation students.

scientific communication and education↗

bioRxiv2023

Using a decision tree to predict COVID case numbers: a tutorial for beginners

This manuscript describes the development of a module that is part of a learning platform named "NIGMS Sandbox for Cloud-based Learning" https://github.com/NIGMS/NIGMS-Sandbox. The overall genesis of the Sandbox is described in the editorial NIGMS Sandbox at the beginning of this Supplement. This module delivers learning materials on machine learning and decision tree concepts in an interactive format that uses appropriate cloud resources for data access and analyses. Machine learning (ML) is an important tool in biomedical research and can lead to improvements in diagnosis, treatment, and prevention of diseases. During the COVID pandemic ML was used for predictions at the patient and community levels. Given its ubiquity, it is important that future doctors, researchers and teachers get acquainted with ML and its contributions to research. Our goal is to make it easier for everyone to learn about machine learning. The learning module we present here is based on a small COVID dataset, videos, annotated code and the use of Google Colab or the Google Cloud Platform (GCP). The benefit of these platforms is that students do not have to set up a programming environment on their computer which saves time and is also an important democratization factor. The module focuses on learning the basics of decision trees by applying them to COVID data. It introduces basic terminology used in supervised machine learning and its relevance to research. Our experience with biology students at San Francisco State University suggests that the material increases interest in ML.

scientific communication and education↗

bioRxiv2022

SCIP: A self-paced, community-based summer coding program creates community and increases coding confidence

In 2020, many students lost summer opportunities due to the COVID-19 pandemic. We wanted to offer students an opportunity to learn computational skills and be part of a community while they were stuck at home. Because the pandemic was very isolating, it was important to support students to learn and build community online. We used lessons learned from literature and our own experience to design, run and test an online program for students called the Science Coding Immersion Program (SCIP). In our program, students worked in small teams for 8 hours a week spread over the week, with one participant as the team leader and Zoom host. Teams worked on an online R or Python class at their own pace with support on Slack from the organizing team. For motivation and career advice, we hosted a weekly webinar with guest speakers. We used pre- and post-program surveys to determine how different aspects of the program impacted students. We were able to recruit a large and diverse group of participants who were happy with the program, found community in their team, and improved their coding confidence. We hope that our work will inspire others to start their own version of SCIP.

scientific communication and education↗