PatientsLikeMe’s Biocomputing team was just started a little over a year ago with the goal of bringing artificial intelligence / deep learning to biological omics data. We are in the extraordinary position of building a massive health data set (DigitalMeTM), and are adding longitudinal multiple-modality omics data to our unique patient reported health data, to discover biomedical insights and ultimately help patients navigate their path to a healthier state. We are looking for highly motivated biologists to derive insights from these data.
This would be an opportunity to build the future of the organization from the ground up. We are collecting regular measurements from healthy people, as well as patients with neurological and autoimmune conditions, pain and fatigue conditions, and mood disorders, among others. The data collected from a wide array of omics platforms will be analyzed together with patient-reported data collected from the PatientsLikeMe platform that capture details about the patient’s experience with their condition as well as information about symptoms, treatments, etc.
We are looking to understand and capture information in a computable format about how individuals respond to different interventions (drugs, dietary, wellness), and make predictions about their disease trajectory. The multi-disciplinary nature of the project and extensive systems biology experience of the group in academia and industry also affords great opportunity for a candidate’s growth and development.
Under supervision of a senior member of the molecular translation group, the Research Associate will be called upon to:
- Delve deeply into the literature to understand the molecular space of a particular condition (or conditions), and the function of biomolecules that have been measured on any of the molecular measurement platforms we are working with
- Identify and evaluate new molecular measurement technologies as they become available
- Think about how to best integrate different kinds of molecular data – e.g., can we connect an individual’s genome to proteomic, metabolomic or transcriptomic changes we see in blood plasma? How? What are the rules of biology that govern these interactions?
- Work in close collaboration with other parts of the organization, both within the Biocomputing group (the analytics and computational biology team, the knowledge management team), and outside it (clinicians that encode data on the PatientsLikeMe platform, the product team)
The Research Associate may be asked to:
- Analyze one individual’s data and their biological story through time, then see how that is connected to their experience of health or clinical measurements that we may have about them
- Identify biomarkers that are associated with disease activity, or predict response to treatment
- Build causal models of what different organs in the body do and how we are measuring those functions
Education: Bachelor’s or Master’s Degree in Molecular Biology or a related field (e.g., biology, biochemistry, molecular biology, cell biology, bioengineering, immunology)
Required skills and experience:
- Have professional work experience relevant to this role. Candidates for this role typically have 0 – 5 years of experience.
- Understand systems-level concepts, like machine-learning or causal network building
- Be a systems-level thinker, and enjoy delving into the literature and solving biological mysteries through the use of prior knowledge
- Possess the ability to be creative and the motivation to pursue research questions independently
- Be comfortable working as part of a team in a highly dynamic environment
- Love science, and the pursuit of truth in data
- Execute ideas with integrity
- Be a team player with excellent communication skills
Preferred, but not required, skills include:
- Experience with mass spectrometry
- Deep immunology experience
- Prior experience working with omics data
Must be local to our Cambridge office, or willing to relocate.
PatientsLikeMe is both a patient network that improves lives and a real-time research platform that advances medicine. Through the network, patients connect with others who have the same disease or condition and track and share their own experiences. In the process, they generate real-world data that help researchers, pharmaceutical companies, regulators, providers, and non-profits develop more effective products, services and care. With more than 500,000 members, PatientsLikeMe is a trusted source for real-world disease information and a clinically robust resource that has published more than 60 peer-reviewed research studies.