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Exploration of Lymph Node-Negative Breast Cancers by Support Vector Machines, Naïve Bayes, and Decision Trees: A Comparative Study
Published in Saravanan Krishnan, Ramesh Kesavan, B. Surendiran, G. S. Mahalakshmi, Handbook of Artificial Intelligence in Biomedical Engineering, 2021
J. Satya Eswari, Pradeep Singh
Various statistical and computational tools have been used for classification of cancer at molecular level (Eswari et al., 2017, Eswari and Dhagat, 2018). A discrimination of acute lymphoblastic leukemia with acute myeloid leukemia was proposed by Golub et al. (1999). This discrimination was based in a weighted voting scheme, which identified 50 genes. They also predicted the membership of new leukemia cases. The same data set was utilized by Mukherjee et al. to develop a support vector machine (SVM) classifier for classification of samples into acute lymphoblastic leukemia with acute myeloid leukemia (Vuong et al., 2014). Khan et al. classified different forms of small round blue cell tumors using artificial neural network and classified them into 96 genes (Batista et al., 2004). A 70-gene predictor was developed by Veer et al. for the prediction of breast cancer (Sehgal et al., 2011). A tree-based method for classification of leukemia and lymphoma was presented in the works of Zhang et al. (2003). They used tree-based method and constructed random forests for classification of cancers. All of these methods require the usage of statistical techniques for classification of tumors and, hence, pose a hindrance in determination of nonlinear relationships between genes. In contrast, complex models do not provide relationships between genes responsible for tumors. To evaluate gene expression data, naïve Bayes (NB), SVM, and decision trees (DTs) have been rigorously tested for their capability in making a distinction among cancers belonging to various diagnostic categories.
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Published in Valerio Voliani, Nanomaterials and Neoplasms, 2021
Joseph M. Caster, Artish N. Patel, Tian Zhang, Andrew Wang
Dual drug delivery with coencapuslation of drugs in particles is an attractive application of nanoparticle drug delivery. Optimal drug ratios and sequences of drug exposure can be defined in vitro but it is very difficult to maintain these spatial and temporal delivery characteristics on a cellular level in vivo. Nanoparticles can be formulated to deliver drugs sequentially at specific molar ratios within the tumor microenvironment. This allows for maximal synergy not achieved with conventional drug delivery methods. As an example, acute myeloid leukemia (AML) is frequently treated with a regimen of daunorubicin and cytarabine. Celator Pharmaceuticals is currently developing CPX-351, a promising liposomal formulation which codelivers cytarabine and daunorubicin in a 5:1 fixed molar ratio. Two completed phase II studies have demonstrated improved efficacy of CPX-351 compared to the standard regimen, particularly in high risk AML patients, and there are several additional phase II trials ongoing [8–10].
Big Data and Transcriptomics
Published in Shampa Sen, Leonid Datta, Sayak Mitra, Machine Learning and IoT, 2018
Sudharsana Sundarrajan, Sajitha Lulu, Mohanapriya Arumugam
Golub et al. studied 38 bone marrow samples from acute leukemic patients. The acute leukemia patients were grouped into acute myeloid leukemia (AML) and acute lymphoblastic leukemia (ALL) categories. The investigators used supervised analysis for class prediction using 50 genes, which were differentially expressed between 11 and 27 AML and ALL samples, respectively. The predictor of 50 genes was utilized to test set of 34 new leukemic samples. 29 of 34 samples were correctly classified. The genes used for the prediction were involved in cell cycle, cell adhesion, transcription, and oncogenes, which were found to be involved in cancer pathogenesis. The second part of the same study used class discovery on the initial 38 leukemic samples to determine the efficiency of the global gene expression analysis in distinguishing AML and ALL. They used SOM to distinguish between the two groups. 24 of the 25 ALL samples clustered together in one group and 10 out of 13 AML samples were clustered in a second class. The results of the class discovery studies indicated that it is possible to discover the diagnostic classes of the cancer when morphological tests were unavailable whereas the biological and clinical information were available.11
A Comparison of Different Bayesian Models for Leukemia Data
Published in American Journal of Mathematical and Management Sciences, 2022
Maria Rafique, Sajid Ali, Ismail Shah, Bilal Ashraf
Leukemia is a type of blood cancer that starts within the bone marrow and results in high numbers of abnormal blood cells, which generate tumors that influence the blood, bone marrow, and lymphoid system. These abnormal blood cells are called impacts or leukemia cells. The general symptoms of leukemia include, feeling tired, fever, an expanded hazard of contaminations, and bruising. These symptoms happen due to the need of regular blood cells to the body and can be detected by blood tests or bone marrow biopsy. The exact cause of leukemia is still unknown. A combination of hereditary variables and natural (non-inherited) components are thought to play a part. Hazard variables like smoking, ionizing radiation, a few chemicals (such as benzene), earlier chemotherapy, and down disorder are also thought to play role in the development of this disease. Individuals with a family history of leukemia are at higher risk of this disease. There are four main types of leukemia: (i) Acute lymphoblastic leukemia (ALL), (ii) Acute myeloid leukemia (AML), (iii) Chronic lymphocytic leukemia (CLL), and (iv) Chronic myeloid leukemia (CML).
Overview of biological mechanisms of human carcinogens
Published in Journal of Toxicology and Environmental Health, Part B, 2019
Nicholas Birkett, Mustafa Al-Zoughool, Michael Bird, Robert A. Baan, Jan Zielinski, Daniel Krewski
Cyclophosphamide is an antineoplastic agent that is widely used in cancer treatment for its immunosuppressive properties. Metabolism of this drug has been extensively studied with respect to carcinogenicity. The parent compound is not carcinogenic by itself and requires hepatic metabolic activation. The two primary metabolites with carcinogenic potential are phosphoramide mustard and acrolein. Cyclophosphamide induces cancer of the bladder and acute myeloid leukemia.
Non-ionizing radiation as possible carcinogen
Published in International Journal of Environmental Health Research, 2022
Shiwangi Gupta, Radhey Shyam Sharma, Rajeev Singh
Despite a suitable animal model of childhood leukemia rodents have been used extensively in studies of adult leukemia (McCormick et al. 1999). Kheifets et al. (2017) performed a case-referent study, with all childhood leukemia cases aged younger than 16. 87% of cases could be matched to a birth certificate and reported risk in intermediate and highest exposure group with an odds ratio of 1.50 and the increased value of OR were found among children who had a site visit. Similarly, Amoon et al. (2018) observed migration from the residence on risk estimates. However, the results of both the studies are not significant with previous analysis showing an increased incidence risk in children exposed to levels >0.4 microtesla and risk of childhood leukemia. Huss et al. (2018) selected several lymphatic cancers in Cohort study, using a job-exposure matrix from 1990 to 2008. 3.1 million workers in the cohort were classified based on exposure as high, medium, only low medium based on job titles from censuses in year 1990–2000. High exposed jobs the HR in men for myeloid leukemia were 1.31 and 1.26 for acute myeloid leukemia. Huss combined their study and previous studies and found a risk of 1.21. However, the risk of acute myeloid leukemia was increased when workers exposed to high levels of radiation for longer period of time. Shojaeifard et al. (2018) investigated in vivo electromagnetic exposure effects in Wister rats on blood factors and he concluded that exposure of mature rats to radiation caused significant changes in platelets, haematocrit and hemoglobin, Red blood cells as compared to the control group. Even though results are inconsistent but more extensive studies show that the risk of leukemia and lymphoma are strongly connected with childhood exposure to radiation scope for further research. Other than childhood cancers except leukemia have not been studied so much so far to draw any facts about the actuality and level of the risk.