Multidisciplinary research holds paramount importance in today's universities. It is seen to break down traditional disciplinary silos by fostering collaborations between researchers from different disciplines. As our ecosystem becomes increasingly complex with issues like climate change, public health crises, and global recessions, the demand for innovative technological solutions with insights from diverse fields is rising. Multidisciplinary research has become a catalyst for holistic exploration, allowing researchers to integrate varied perspectives and methodologies.
This study provides a streamlined yet comprehensive roadmap for effective thesis and dissertation writing for multidisciplinary research. It starts with guidance on how to select a research topic, reviews groundbreaking past multidisciplinary studies using case studies, dissects challenges faced by researchers in conducting multidisciplinary research, and ends with guidance for researchers looking to pursue it professionally or applying it in real-world settings. Whether a novice or experienced researcher, individuals will find valuable strategies and insights to enrich their multidisciplinary research journey.

Breast cancer is a significant health concern worldwide. Its prognosis and survival rate are greatly dependent on timely detection and accurate prediction of the progression. Many prediction models have been developed which take into consideration genomics, racial disparities, and tumor characteristics. However most of them focus on short-term outcomes. Long-term follow-up studies that assess breast cancer recurrence, late-stage complications, and survival beyond the initial treatment phase are essential for providing a more comprehensive picture of patient outcomes.

This study first reviews critical research which has been conducted in the past on breast cancer prediction and identifies their shortcomings. It also identiies the distribution pattern and risk factors. Then it uses two existing breast cancer datasets with over 1000 observations each, containing important variables such as demographics, tumor size, omics data, mutation count, cancer type, duration of treatment, among others. Survival analysis is applied to identify independent predictors of breast cancer survival, considering factors such as tumor characteristics, treatment modalities, and patient demographics. Furthermore, machine learning algorithms are employed to enhance predictive accuracy. Python software is used.

This study delves into the dynamics of stock price movements in response to financial results announcements. The objective is to investigate how stock prices of companies are affected on the announcement day and the subsequent seven days following the release of financial results. Using historical stock price data and financial results announcement dates, this study examines whether there is a discernible pattern in how stock prices react to different types of financial results.

The study includes banking and financial services, pharmaceutical, and power, healthcare, and FMCG sector stocks listed on the Bombay Stock Exchange and Nifty-50 indexes. Period of the data is April 2018 to March 2023.  Further, event window considered is announcement day (T), two days preceding the announcement day (T-1 and T-2), and 7 days following the announcement day (T+1, T+2, T+3, T+4, T+5, T+6, and T+7). Multiple statistical analysis methods are applied, such as trend analysis, T-Test and Anova using Python.