Multiple Myeloma is a cancer that forms in your plasma cells. Plasma cells are a type of white blood cell that help your body fight off infections. Multiple Myeloma causes cancer cells to accumulate in your bone marrow, crowding out healthy cells.
This disease is currently diagnosed in about 32,000 people in the United States every year. But experts estimate that by the age of 60, three to five percent of the population will have cells detectable in their blood that show signs of pre-myeloma.
It can take years - sometimes decades - for myeloma precursors to multiply to a point at which symptoms for the disease begin to manifest. For doctors, it’s often difficult to understand who should be considered for closer observation.
Oncologist Dr. Francesco Maura, working in the lab of Dr. Ola Landgren at Memorial Sloan Kettering, developed a computational algorithm to understand the first genetic drivers of pre-myeloma cells. Using the genetic information from samples collected from public databases, researchers reconstructed the life history of these blood cells before the myeloma developed.
By looking at myeloma acquisition rates over time, it was found that mutations of the cancer could be identified at different stages. During the progression of the Multiple Myeloma, there were periods where additional mutations could make the disease more aggressive. By being able to identify these periods, better treatment could be recommended.
ResoluteAI's Foundation service facilitates and accelerates the work of finding precise background information for this kind of research.
By typing “multiple myeloma” into the ResoluteAI platform, 47,115 publications are returned as results. Since many researchers may only be concerned with specific subjects like genetic information and mutation, the query can be further distilled. By conducting an Advanced Search, researchers can specify “Multiple Myeloma”, “gene”, and “mutation.” This Advanced Search returns 723 publications, 68 Clinical Trials, and 229 Grants.
Seeing the Big Picture
Many of the publications have been authored by scientists in a range of fields such as biochemistry, genetics, biology, chemical engineering, etc. Using ResoluteAI’s explorer analytics, researchers can see the complete overview of these 723 publications, so that they can understand the landscape of their research in a digestible, visual way.
Once a researcher determines that they are only interested in results from Medicine and Oncology, they can click into Medicine, then click into Oncology, surfacing a much more manageable 142 publications that are relevant to their research.
If a researcher would like to go back and browse papers in Hematology instead of Oncology, they can easily check to see what publications are available by clicking on Hematology. When research areas are so broad that they are likely to cover numerous topics across medicine, ResoluteAI’s robust technology allows researchers to navigate with ease.
Once researchers have the big picture, they can then easily check to see if there are any new algorithms using genetic data for other areas of Myeloma research.
ResoluteAI allows researchers to further define a broad term like ”algorithm”. This will yield results that are about Myeloma in which the research product was an algorithm:
After defining the terms, four papers surfaced that were directly related to algorithms and Multiple Myeloma. This is how ResoluteAI works in parallel with scientific minds to help guide and focus their research.
Conducting research on Multiple Myeloma or any other disease is a complex task. Multiple Myeloma is related to research in oncology, hematology, and other areas of genetics, medicine, and technology. ResoluteAI, the discovery engine for science, helps researchers navigate multiple areas of their research with easy-to-use graphic interfaces and intuitive query setups that allow them to see the big picture while maintaining the ability to cross-navigate search topics. ResoluteAI empowers researchers to connect to discover.