In Cancer Precision Medicine, Deep Machine Learning Enables | 98819
International Research Journals

In Cancer Precision Medicine, Deep Machine Learning Enables Multi-Data Type Processing And Prediction Biomarker Advancement


Sanjiv Kumar*

Gene-environment interactions that change cellular homeostasis are associated with cancer progression. It is possible to significantly enhance diagnosis and treatment by using biomarkers as early indicators of illness appearance and development. Data-driven biomarker discoveries have been made possible by the large omics datasets produced by high-throughput profiling technologies like microarrays, RNA sequencing, whole-genome shotgun sequencing, nuclear magnetic resonance, and mass spectrometry. Traditionally, linear parametric modelling has been the only statistical technique used to identify features with differential expression as molecular markers. Oncogene heterogeneity, epigenetic alterations, and high levels of polymorphism necessitate biomarker-assisted, individualised treatment plans. In recent years, more and more research into numerous diseases has been conducted using deep learning, a key component of machine learning. ML and DL techniques combined for performance improvement across Precision medicine is starting to benefit from the robust ensemble-learning prediction models produced by multi-omics datasets. This study focuses on how ML/DL techniques have recently evolved to offer integrated approaches to finding cancer-related biomarkers and their application in precision medicine. Molecular biomarkers are physiological indications that can reveal molecular changes brought on by disease, help predict how a disease will appear, and pinpoint disease-related molecular targets. To reduce mortality in cancer pathology, it is essential to use the right biomarkers for early diagnosis and prognosis. Genetic variations, the presence of oncogenes, and epigenetic factors complicate the early diagnosis and prognosis of cancer. In recent years, data integration technologies that increased diagnostic precision and therapeutic efficacy have benefited patient clinical care. Artificial intelligence is the intelligence of machines that can sense, synthesise, and infer knowledge, as opposed to the intelligence of animals and humans.

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