Cancer Data
This robust dataset is extracted from the International Skin Imaging Collaboration (ISIC). Similar datasets are used for the annual ISIC Challenge, presenting an opportunity for the computer science community to produce algorithms that can outperform professional dermatology. The submitted dataset contains approximately 1,000 images of malignant melanomas, as well as approximately 1,000 images of benign melanomas.
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The medical community strives continually to improve the quality of care patients receive.
Predictions of prognosis are essential for doctors and patients to choose a course of treatment. Recent years
have witnessed the development of numerous new cancer survival prediction models. Most attempts to
predict the prognosis of people with malignant development rely on classification techniques. We could
experiment with significantly different results using only a subset of SEER (Surveillance, Epidemiology,
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The development of high throughput sequencing technologies i.e. Next Generation Sequencing (NGS) is revolutionizing the exploration of cancer. Though sequence datasets are highly complex, mutation can occur randomly in DNA or RNA sequences that can make cells sicker or less fit. The unusual growth and behavior of genes in cells cause cancer. Cancer-driver gene cells grow when mutation occurs. Identification of cancer driver genes is a critical and challenging issue for researchers.
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Gestational diabetes is a type of high blood sugar that develops during pregnancy. It can occur at any stage of pregnancy and cause problems for both the mother and the baby, during and after birth. The risks can be reduced if they are early detected and managed, especially in areas where only periodic tests of pregnant women are available. Intelligent systems designed by machine learning algorithms are remodelling all fields of our lives, including the healthcare system. This study proposes a combined prediction model to diagnose gestational diabetes.
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In early 2019, we developed a manually curated database named lncR2metasta to provide a comprehensive repository for the regulations of long non-coding RNAs (lncRNAs, an important ncRNA type) during various CMEs. We updated this database this year by supplementing other two important ncRNA types, microRNAs (miRNAs) and circular RNAs (circRNAs), for their involvement during various CMEs after a thorough manual curation from published studies.
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Blood indices of patients with different cancers and gastric diseases
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One of the industries that uses Machine Learning is Radiation Oncology
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Columns are genes, miRNAs, drugs, or cnv. Rows are patient identifiers or cell lines.
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This dataset is in support of my Research paper 'Detection of Pancreatic,Ovarian & Prostate Tumor, Cancer and Treatment by Ablation'.Due to computer crash, all work, datasets and old papers lost. Re-work may be submitted.
For Machine design, pls refer, open-access page 'Data and Designs of B-Machines'
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