**Working with Us** Challenging. Meaningful. Life-changing. Those aren't words that are usually associated with a job. But working at Bristol Myers Squibb is anything but usual. Here, uniquely interesting work happens every day, in every department. From optimizing a production line to the latest breakthroughs in cell therapy, this is work that transforms the lives of patients, and the careers of those who do it. You'll get the chance to grow and thrive through opportunities uncommon in scale and scope, alongside high-achieving teams rich in diversity. Take your career farther than you thought possible. Bristol Myers Squibb recognizes the importance of balance and flexibility in our work environment. We offer a wide variety of competitive benefits, services and programs that provide our employees with the resources to pursue their goals, both at work and in their personal lives. Read more: careers.bms.com/working-with-us . **Position Summary** We are seeking a highly motivated student to join our group for a 10-week long summer internship program. A successful candidate will work with the team to develop a statistical learning method to infer regulon and protein activity that capture the effect of somatic alterations in regulatory proteins. Identifying causal drivers of cancer progression is essential for developing effective anti-cancer therapies. Our team has a strong background in inferring protein regulatory activity, which we will leverage in this project. We integrate multi-modal real-world patient data (RWD) to identify key drivers that drive and maintain patient states via regulating key oncogenic processes and transcriptional programs. These drivers represent best opportunity for therapeutic intervention due to their disease relevant regulatory roles. However, many existing approaches to identify these drivers rely solely on expression relationships between gene pairs, ignoring somatic alterations that are common in cancers and often independently affect regulator activity that is not captured in gene expression data. In this summer intern project, we propose to build on existing internal methods aimed at capturing transcriptional regulatory relationships. We will focus on enhancing and strengthening the signal across different biological contexts. Specifically, we will develop a statistical learning method to infer regulon and protein activity that capture the effect of somatic alterations in regulatory proteins. This new method may uncover novel therapeutic targets with clear patient selection, which are missed by existing methods. **Key Responsibilities**
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