摘要翻译：
肿瘤蛋白P53与人类一半以上的癌症有关，恶性肿瘤的预测不仅对癌症的早期发现起着重要作用，而且对发现有效的癌症预防和治疗也起着重要的作用，但目前尚无预测肿瘤蛋白P53突变的方法，而肿瘤蛋白P53突变是导致乳腺癌、血液、皮肤、肝脏、肺、膀胱癌等发病率较高的原因。本研究利用FASTA、BLAST、CLUSTALW和TP53等生物信息学工具，通过检测肿瘤蛋白P53的恶性突变，提出了一种预测癌前病变的新方法。实施和应用这种通过肿瘤蛋白P53突变预测癌前病变的新方法，当使用更具体的参数/特征提取预测结果时，显示出有效的结果，即当用户增加从数据库中获得的结果的过滤器数量时，给出更具体的诊断和分类。此外，通过预测突变的肿瘤蛋白P53检测癌前病变，将通过避免接触毒素、辐射或通过改变他们的食物、环境甚至生活节奏来监测他们自己，从而减少一个人未来的癌症。此外，预测癌症前的新方法将有助于如果有任何治疗可以给该人治疗突变的肿瘤蛋白p53。指标项（正常同源性TP53基因、肿瘤蛋白P53、癌基因实验室、GC和AT含量、FASTA、BLAST、ClustalW）
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英文标题：
《New Approach for Prediction Pre-cancer via Detecting Mutated in Tumor
  Protein P53》
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作者：
Ayad Ghany Ismaeel
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最新提交年份：
2013
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分类信息：

一级分类：Computer Science        计算机科学
二级分类：Computational Engineering, Finance, and Science        计算工程、金融和科学
分类描述：Covers applications of computer science to the mathematical modeling of complex systems in the fields of science, engineering, and finance. Papers here are interdisciplinary and applications-oriented, focusing on techniques and tools that enable challenging computational simulations to be performed, for which the use of supercomputers or distributed computing platforms is often required. Includes material in ACM Subject Classes J.2, J.3, and J.4 (economics).
涵盖了计算机科学在科学、工程和金融领域复杂系统的数学建模中的应用。这里的论文是跨学科和面向应用的，集中在技术和工具，使挑战性的计算模拟能够执行，其中往往需要使用超级计算机或分布式计算平台。包括ACM学科课程J.2、J.3和J.4（经济学）中的材料。
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一级分类：Quantitative Biology        数量生物学
二级分类：Other Quantitative Biology        其他定量生物学
分类描述：Work in quantitative biology that does not fit into the other q-bio classifications
不适合其他q-bio分类的定量生物学工作
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英文摘要：
  Tumor protein P53 is believed to be involved in over half of human cancers cases, the prediction of malignancies plays essential roles not only in advance detection for cancer, but also in discovering effective prevention and treatment of cancer, till now there isn't approach be able in prediction the mutated in tumor protein P53 which is caused high ratio of human cancers like breast, Blood, skin, liver, lung, bladder etc. This research proposed a new approach for prediction pre-cancer via detection malignant mutations in tumor protein P53 using bioinformatics tools like FASTA, BLAST, CLUSTALW and TP53 databases worldwide. Implement and apply this new approach of prediction pre-cancer through mutations at tumor protein P53 shows an effective result when used more specific parameters/features to extract the prediction result that means when the user increase the number of filters of the results which obtained from the database gives more specific diagnosis and classify, addition that the detecting pre-cancer via prediction mutated tumor protein P53 will reduces a person's cancers in the future by avoiding exposure to toxins, radiation or monitoring themselves at older ages by change their food, environment, even the pace of living. Also that new approach of prediction pre-cancer will help if there is any treatment can give for that person to therapy the mutated tumor protein P53. Index Terms (Normal Homology TP53 gene, Tumor Protein P53, Oncogene Labs, GC and AT content, FASTA, BLAST, ClustalW)
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PDF链接：
https://arxiv.org/pdf/1310.2182