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- W4214623009 abstract "Computational intelligence (CI) is one of the most popular and advanced fields that mimics human intelligence and transforms numerous aspects of the healthcare industry. For the past two decades, CI has grown extensively and reached a new height in clinical research on cancer. The subfields of artificial intelligence, like deep learning and machine learning, are also applied to tackle various scientific problems. Cancer is one of the most aggressive and dreaded diseases, which every year costs more than 10 million lives globally, and it is expected that these tolls may reach 16.4 million by 2040. Hence, meticulous prognosis and early diagnosis of this disease are very significant to increase the chance of patient survival. In recent years, breakthroughs in various AI-based stratagems for precision oncology, digital pathology, and next-generation sequencing (NSG) are driving the need for new prognostic and predictive assays that facilitate the stratification and selection of patients for treatment. This chapter briefly includes the applications of CI and its subfields in cancer biomarkers and therapeutic target prediction. However, we explore how these applications have assisted in solving biomedical problems with unprecedented accuracy and deep learning. We also demonstrated how these data could be run on natural and mathematical algorithms to derive a classification of cancer genomes, identify potential biomarkers, therapeutic targets, drugs, and draw scientifically sound conclusions based on them. By providing a fresh viewpoint on how computational tools might aid enhance cancer detection, this chapter can help make significant advances in the medical disciplines and scientific community." @default.
- W4214623009 created "2022-03-02" @default.
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- W4214623009 date "2022-01-01" @default.
- W4214623009 modified "2023-09-25" @default.
- W4214623009 title "Computational Intelligence: A Step Forward in Cancer Biomarker Discovery and Therapeutic Target Prediction" @default.
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- W4214623009 doi "https://doi.org/10.1007/978-981-16-9221-5_14" @default.
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