October marked Breast Cancer Awareness Month, a global initiative to promote regular screenings, early detection, and effective treatment for breast cancer. At Quantori, we are dedicated to developing innovative solutions that enhance cancer data analysis, support more precise scientific insights, and eventually improve patient outcomes. In this blog, we will outline the landscape of breast cancer precision medicine and highlight our work with large-scale cancer datasets.

Quantori's mission to empower Orion on its journey to build a Scalable Research Data Platform for the modern AI age

In the rapidly evolving landscape of drug discovery, the fusion of multimodal data with artificial intelligence (AI) and machine learning (ML) technologies is setting the stage for groundbreaking advancements. This synergy is not only expediting traditional processes but also paving the way for innovative approaches to complex, man-power intensive challenges. Here, we delve into four transformative use cases, illustrating the power of this integration in revolutionizing drug discovery.

Multi-omics analysis is expected to significantly impact biomedical research, leading to a new era of personalized medicine with improved diagnostic and treatment options for patients. This perspective comes from the fact that biological processes, like disease progression, involve complex interactions of biomolecules across various omic layers.

In recent years, the rapid growth of AI technologies has resulted in more organizations adopting machine learning techniques to solve complex business problems. However, the process of building and deploying ML models at scale can be challenging, involving numerous complex steps such as data preparation, feature engineering, model training, and deployment.

No matter how hard you try to progress your research, without enough data, you’ll probably get stuck at some point. This is particularly crucial in the healthcare sector. To extract therapeutic insights from medical records, researchers typically need to work with many hundreds to thousands of patient cases, gathering and processing data from various sources. When the initial evidence at their disposal is limited, the conclusions may become distorted and biased.

Building Data And Technology Foundation To Unlock Innovation

Efficient Data Management for CryoEM HPC in AWS Cloud

We have described RWD and RWD in our previous blog. There is strong promise and increasing use of Real World Evidence to accelerate data-driven decisions, improving not only the clinical trial experience, but healthcare in general. But, can we believe Real World Evidence?

Real World Data and Real World Evidence (RWD/RWE) is a hot topic in Pharma in the context of clinical trials.