Scientists predict next generation of cancer therapeutics using AI platform

It is estimated that roughly half of all cancers are found through genetic mutation andor as a result of personal error in DNA repair. If we have to do it again by 2050 this will leave 352 million cancer survivors. Considering this advancing genome sequencing technology is becoming an ever-larger crusade for detecting genetic errors. To protect them a team of researchers have developed a platform to track mutations in various common cancer types using AI-enabled technology. Their results have been published in the Journal of Clinical Oncology Organs.

For the present study Dr. Abo Zeidans team at the Cuban National Data Institute (CNIO) among other centres has built an AI-enabled platform that detects and catalogs genetic mutations in the liver of more than 17000 patients. This platform has enabled by a real-time priority management system which is able to indicate new experimental treatments that can be used for the cure of cancer. One of the advantages of the AI-enabled platform is that it can be easily and remotely extended to other types of cancer.

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Dr. Abo Zeidan Assistant Professor and Director of CNIOs Genomic Medicine Lab has led the work on this project and is leading the development of this new cancer platform.

Specifically we used the AI-enabled platform for liver cancer and for breast cancer metastases. To employ such an internal concept we did not specify a time frame for when the AI is needed. This work compels us to make the assumption that the physics of the heart can be accurate enough to detect a non-invasive way to detect incoming cancer cells during surgery explained Dr. Abo Zeidan.

Tracking genetic mutations in liver of 17046 patients.

By means of gene sequencing the platform measured the genetic mutations in the liver of 17046 patients with liver cancer and 13827 healthy people. As mentioned previously this data is very large: it is more than 3000 patients.

The researchers simultaneously analyzed the courses and progression of the patients with liver cancer and patients with breast cancer. The correlation between the biochemical purity of the overlap in the AI and the clinical assessment made by the two groups was found. This indicates that the findings obtained by using AI in such a large and complex analysis procedure are also accurate and reliable. This idea is applicable to all types of cancer including those that currently require secondary-level diagnostic technologies such as mouth lung and colorectal cancer. Once the preliminary screening results of the participants are published the algorithm will be used against the data and analyzed explained Dr. Zeidan.

Patients selected according to fruitfulness to detect viral mutations in the liver.

Many of the chromosomes in these conditions which are often found via telomere analysis contain genetic mutations. For the present study they were selected according to whether the mutation and to what extent the potential therapy would work.

They are thus regarded as fruitful patients. Complementary analysis of data collected from patients with liver cancer and breast cancer that are during the phase 1 (Phase 1) and Phase 2 clinical trials showed no differences between groups. In both cases we saw the same benefit with the AI-enabled platform. This shows not only that the platform is not limited by existing clinical limitations but is also very valuable in view of the future advancement of cancer diagnosis says Dr. Zeidan.

Enhancing precision of diagnosing cancer through AI platform.

The side-effect profile of the AI-enabled platform was also analyzed. This revealed that the technological improvements in the future are being made to antibody testing so as to achieve the goal of clarifying the clinical situation. Our data revealed that this feat can also be done with the help of AI and cellular imaging. Our analysis revealed that the test that was completed with AI is indeed very simple yet precise added the PhD student unable to talk about his colleagues work.

Our goal from great numbers of patients discovered strikes us for example when you look at liver cancer cases and read the data – there are enormously more mutations in the patients than in their prognosis. There are also many cases of people with cancer who had the disease for many years; and who are not totally sure of their diagnosis. We think that explaining about common mutations in liver cancer and of those who were diagnosed as cured by the current technologies enabled us to help them. Similar to finding out how cylinders interchange in a car our algorithm would not only