Sources report that, 1 in 8 women in the United States will be diagnosed with breast cancer in her lifetime. Furthermore, the long-term mental health outcomes of radiation therapy and chemotherapy, a common practice in the field of oncology, for cancer patients are a significant concern. Research shows that the global prevalence of anxiety among cancer patients is 17%–69%, and the global prevalence of anxiety among the general population is 31.9%, implying that the mental health outcome may be more prevalent among cancer patients. More specifically, Living Beyond Breast Cancer (LBBC) states more than 40% of people diagnosed with breast cancer experience anxiety. Even survivors may face long-term psychological effects such as anxiety, depression, and a fear of cancer recurrence, which can impact their day-to-day life. Finally, the American Cancer Society (ACS) states some cancer treatments can cause cognitive effects in current patients and survivors, such as “chemo brain” or brain fog, which may lead to difficulties with concentration, memory, and multitasking. Using NIH All of Us data, This study seeks to further highlight and examine the associations between cancer therapy and procedures and the mental health outcome of anxiety as it relates to breast cancer patients using various regression techniques.

Caseinolytic peptidase P (ClpP) is a multimeric serine protease found in many prokaryotes, as well as in the mitochondria of eukaryotic cells and in chloroplasts. In prokaryotes, ClpP is essential for maintaining protein homeostasis, and its disruption can affect the virulence and infectivity of various pathogens.1 In eukaryotes—particularly in humans—ClpP plays a crucial role in protein quality control by degrading denatured and misfolded proteins, thereby preserving the integrity of the respiratory chain and sustaining oxidative phosphorylation.2 Due to these multifaceted functions, ClpP is an attractive target for both anticancer and antibiotic drug development. Although several compounds, such as acyldepsipeptides (ADEP), have been developed to target ClpP, bacterial resistance continues to pose a significant challenge to effective antibiotic therapy.3 Here in this project, different computational drug discovery approaches will be implemented to discover novel antibiotics for ClpP. More specifically, ligand based and structure based drug discovery approaches will be utilized to discover candidate drug for ClpP. First, different chemical bioassay will be used to curate the biochemical data that is available for different ClpP gene from different species including prokaryotes and eukaroytes. These dataset will be used to train different machine learning model to predict the bioactivity of unknown compounds. Finally these models will be tested against different chemical databases including ( Enamine, Mcule)4-5 to predict the activity of unknown compound for ClpP. Our model will focuses of two approaches, such as identification of bioactive molecule for ClpP and predicting the IC50 ( biochemical descriptor) to determine the activity of a molecule. Overall, these approaches will allow to screen chemical compounds active to ClpP.  

This capstone project explores the relationship between anxiety and lung cancer diagnosis, addressing a gap in research where psychological factors have often been overlooked. Using survey data from Kaggle and the UCI Machine Learning Repository, I built a logistic regression model to see if people who report experiencing anxiety are more likely to also report a lung cancer diagnosis. After accounting for factors like age, gender, smoking habits, and chronic disease, the results demonstrated that individuals reporting anxiety were over twice as likely to report having lung cancer. Overall, the model demonstrated solid performance, with consistent results observed across key demographic and behavioral subgroups, including smokers and individuals over 60. These findings suggest that mental health, particularly anxiety, may play a larger role in physical health outcomes than we often consider. By bringing psychological factors into the conversation, this research encourages a more holistic approach to how we think about cancer risk.