In the last two decades, we have welcomed the genomic revolution with the hope that its novel early-detection tests will improve patients’ quality of life and potentially help prevent diseases from evolving. In recent years, the genomic revolution was extended by the generation of mass genome-sequenced data, the unlimited cloud computational resources, and the progress in artificial intelligence’s ability to process large-scale datasets such as genomics sequences.
So why aren’t diagnosis challenges solved on a daily basis? Why aren’t various disease therapy targets easily identified? Why can’t we predict whether or not a patient will respond to a certain therapy? Many diseases harbor broad genomic heterogeneity, but assuming we have large-scale patient data, biological assays results, and the analytical tools to derive insights, why aren't we there yet? What can we do better? I believe that tightening the communication between the five expertises: wet-lab biologists, clinicians, bioinformaticians, and software and analytical engineers—including machine learners—can bring us further and faster.
To make things clear, the biotech community is facing big challenges wherein many diseases include a large variety of genomic profiles, and yet they are still able to make progress in identifying targets and delivering novel therapeutics modalities. One of the major contributors to COVID-19’s speedy insights was the multi-directional communication between all the contributors. The virus was immediately sequenced, and all scientists were diverted from their daily work to learn the virus and discuss ways to overcome it. This tight communication can be mimicked by a common shared platform for other diseases. The platform should capture multiple genomic sequence databases, the bioinformatics tools to process them in a harmonized way, and the sample clinical and biological information that has been reviewed and annotated by clinicians and biologists. This common platform will minimize the time that the biotech needs to focus on software development, allowing it to curate and harmonize public sample datasets and maximize the time it focuses on developing the novel biology platform for the relevant disease.
For that Oriel Research Therapeutics (ORT), my company built the ORT platform. The ORT platform includes sequence data harmonized by unified bioinformatics tools as well as clinical and biological data parsed by automated tools and reviewed by wet-lab biologists and clinicians. In addition, we offer off-the-shelf and custom proprietary machine learning models to advance the understanding of a disease’s genomic profile. The ORT platform is a software product that was built using cloud resources and is therefore accessible from anywhere around the world. It is modular and expandable to address ORT customers’ specific requests.
The ORT platform as a service (paas) is a major pillar in our mission to bring precision medicine to everyone and every location. Our goal is to see more of the biotech discoveries convert faster to the clinic. Please feel free to contact me or anyone on my team for any support you might need from the ORT’s platform at email@example.com . We will be more than happy to expedite your scientific and development work.