Artificial Intelligence (AI) is the subject of a challenge and attention in the field of oncology and raises many promises for preventive diagnosis, but also fears, some of which are based on highly speculative visions for the classification and detection of tumors. A brain tumor that is malignant is a life-threatening disorder. Glioblastoma is the most prevalent kind of adult brain cancer and the 1 with the poorest prognosis, with a median survival time of less than a year. The presence of O⁶ -methylguanine-DNA methyltransferase (MGMT) promoter methylation, a particular genetic sequence seen in tumors, has been proven to be a positive prognostic indicator and a significant predictor of recurrence. This strong revival of interest in AI is modeled in particular to major technological advances which have significantly increased the performance of the predicted model for medical decision support. Establishing reliable forecasts remains a significant challenge for electronic health records (EHRs). By enhancing clinical practice, precision medicine promises to improve healthcare delivery. The goal is to produce improved prognosis, diagnosis, and therapy through evidence-based sub stratification of patients, transforming established clinical pathways to optimize care for each patient’s individual requirements. The abundance of today’s healthcare data, dubbed “big data,” provides great resources for new knowledge discovery, potentially advancing precision treatment. The latter necessitates multidisciplinary initiatives that will use the knowledge, skills, and medical data of newly established organizations with diverse backgrounds and expertise. The aim of this paper is to use magnetic resonance imaging (MRI) images to train and evaluate your model to detect the presence of MGMT promoter methylation in this competition to predict the genetic subtype of glioblastoma based transfer learning. Our objective is to emphasize the basic problems in the developing disciplines of radiomics and radiogenomics, as well as to illustrate the computational challenges from the perspective of big data analytics.

Generative text summarization can condense large volumes of information into a concise summary. It helps users quickly grasp the main points of a text without having to read the entire document. Machine learning (ML) plays a pivotal role in this domain, offering significant advantages in information processing and comprehension. In this paper we present CoMod, an abstractive method for generating the context of a document, from its content and that of the referenced documents, if any. CoMod analyzes the intricate patterns and relationships within a document’s content, thereby extracting and inferring the underlying context. The context generation process involves using a word linearization process as well as a Markov model, specifically a Bigram model, to predict the likelihood of word sequences. The Markov model is trained on a corpus of text and used to generate coherent sentences based on the probabilities of transitioning from one word to another. The word linearization model has been trained on general datasets. Markov tables allows to adapt the generated context to a specific domain and can be built on the fly in CoMod. The approach was compared to other methods and demonstrated very encouraging capabilities by outperforming other approaches tested on the same datasets. It thus confirms the potential of generative methods in the field of automatic text summarization and their ability of leveraging the power of machine learning for context generation to revolutionize information management, boosting productivity, scalability and knowledge discovery in various domains.