The next generation of cancer therapies may be discovered by melding data science technologies with cancer research. It can be used to speed up research, provide additional insights and therapeutic alternatives, and eventually improve patient care.
- Cloud Computing
Cloud computing provides almost infinite resources for storing, analyzing, and displaying data. Genomic databases can be analyzed without a local infrastructure to house the enormous data volumes. Since machine learning relies on a huge number of samples, the cloud can support it by enabling access to big data sets from many sources. Cloud computing can do more than just store data; it may also run algorithms developed by academics directly on the data. This allows other academics to utilize the algorithms and get insights from their output without requiring a high level of technical expertise.
- High Performance Computing
High-performance computing (HPC) employs processors comparable to those found in laptops, but instead of a single CPU, it connects hundreds of workstations with many CPUs and cores. A massive amount of data can be processed simultaneously by using HPC. It has the ability to significantly speed up the discovery of cancer medicines by identifying patterns in vast datasets that are too big for human examination. HPC’s can aid in better understanding the complexities of cancer development, the identification of innovative and successful therapies, and the elucidation of patterns in large and complicated data sets, all of which contribute to our knowledge of cancer.
- Digital Technology
Devices based on digital technology such as smartphone apps, wearable devices, and telehealth are becoming more common. There is an increasing possibility to collect rich mobile sensor data in real-world contexts continually, passively, and with low effort. Gathering data from cancer patients’ mobile sensors might aid in the tracking of digital biomarkers related to symptoms, quality of life, physical functioning, and also the incidence of adverse events.
- Artificial Intelligence
Artificial intelligence has revolutionized cancer genomics research. The vast applications of AI include detection and diagnosis of cancer, subtype categorization, identifying individuals at the risk of cancer, therapy optimization, and identifying novel therapeutic targets for drug development. AI integration in cancer care has the potential to enhance diagnostic accuracy and speed, help clinical decision-making, and result in improved health outcomes.
- Digital Twins
Digital twins, which have been used effectively in various industries, have the potential to pave the way for advancements in cancer care and research. Scientists hope to utilize digital twins to anticipate the effect of new medications on cancer patients using computer models or “digital twins” without harming actual individuals by combining computational science and medicine. Digital twins will aid in predicting the progression of cancer and how it will respond to treatment.