&lt;p&gt;OSacc: Gene Expression-Based Survival Analysis Web Tool For Adrenocortical Carcinoma&lt;/p&gt;

Xie, Longxiang; Wang, Qiang; Nan, Fangmei; Ge, Lite; Dang, Yifang; Sun, Xiaoxiao; Li, Ning; Dong, H.; Han, Yali; Zhang, Guosen; Zhu, Wan; Guo, Xiangqian

doi:10.2147/cmar.s215586

Cited by 19 publications

(19 citation statements)

References 30 publications

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“…Therefore, building benchmark databases for model validation is urgently needed. One solution is to start building cancer patients' databases for prognosis analysis [101][102][103][104][105][106][107][108][109][110].…”

Section: Challenges In the Application Of Deep Learning In Cancer Promentioning

confidence: 99%

The Application of Deep Learning in Cancer Prognosis Prediction

Zhu

Xie

Han

et al. 2020

Cancers

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260

161

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Deep learning has been applied to many areas in health care, including imaging diagnosis, digital pathology, prediction of hospital admission, drug design, classification of cancer and stromal cells, doctor assistance, etc. Cancer prognosis is to estimate the fate of cancer, probabilities of cancer recurrence and progression, and to provide survival estimation to the patients. The accuracy of cancer prognosis prediction will greatly benefit clinical management of cancer patients. The improvement of biomedical translational research and the application of advanced statistical analysis and machine learning methods are the driving forces to improve cancer prognosis prediction. Recent years, there is a significant increase of computational power and rapid advancement in the technology of artificial intelligence, particularly in deep learning. In addition, the cost reduction in large scale next-generation sequencing, and the availability of such data through open source databases (e.g., TCGA and GEO databases) offer us opportunities to possibly build more powerful and accurate models to predict cancer prognosis more accurately. In this review, we reviewed the most recent published works that used deep learning to build models for cancer prognosis prediction. Deep learning has been suggested to be a more generic model, requires less data engineering, and achieves more accurate prediction when working with large amounts of data. The application of deep learning in cancer prognosis has been shown to be equivalent or better than current approaches, such as Cox-PH. With the burst of multi-omics data, including genomics data, transcriptomics data and clinical information in cancer studies, we believe that deep learning would potentially improve cancer prognosis.

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Section: Challenges In the Application Of Deep Learning In Cancer Promentioning

confidence: 99%

The Application of Deep Learning in Cancer Prognosis Prediction

Zhu

Xie

Han

et al. 2020

Cancers

Self Cite

260

161

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“…LOGpc is a web server that contains a large number of datasets for survival analysis, which provides 13 types of survival terms for 28,098 cancer patients from 26 types of malignant tumors, including OSlms, OSblca, OSkirc and other 23 online prognostic tools (14)(15)(16)(17)(18)(19)(20)(21). These patient samples were collected mainly from TCGA and GEO cohorts.…”

Section: Logpcmentioning

confidence: 99%

Comprehensive Review of Web Servers and Bioinformatics Tools for Cancer Prognosis Analysis

Zheng

Zhang

et al. 2020

Front. Oncol.

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Prognostic biomarkers are of great significance to predict the outcome of patients with cancer, to guide the clinical treatments, to elucidate tumorigenesis mechanisms, and offer the opportunity of identifying therapeutic targets. To screen and develop prognostic biomarkers, high throughput profiling methods including gene microarray and next-generation sequencing have been widely applied and shown great success. However, due to the lack of independent validation, only very few prognostic biomarkers have been applied for clinical practice. In order to cross-validate the reliability of potential prognostic biomarkers, some groups have collected the omics datasets (i.e., epigenetics/transcriptome/proteome) with relative follow-up data (such as OS/DSS/PFS) of clinical samples from different cohorts, and developed the easy-to-use online bioinformatics tools and web servers to assist the biomarker screening and validation. These tools and web servers provide great convenience for the development of prognostic biomarkers, for the study of molecular mechanisms of tumorigenesis and progression, and even for the discovery of important therapeutic targets. Aim to help researchers to get a quick learning and understand the function of these tools, the current review delves into the introduction of the usage, characteristics and algorithms of tools, and web servers, such as LOGpc, KM plotter, GEPIA, TCPA, OncoLnc, PrognoScan, MethSurv, SurvExpress, UALCAN, etc., and further help researchers to select more suitable tools for their own research. In addition, all the tools introduced in this review can be reached at http://bioinfo.henu.edu.cn/WebServiceList.html.

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“…OSpaad is developed as we previously reported. [10][11][12][13] OSpaad is developed in a Windows server system. Front-end application was | 305 "pancreatic cancer," "prognostic biomarker," "survival analysis," and "gene expression."…”

Section: Development Of Ospaadmentioning

confidence: 99%

OSpaad: An online tool to perform survival analysis by integrating gene expression profiling and long‐term follow‐up data of 1319 pancreatic carcinoma patients

Zhang

Wang

Yang

et al. 2020

Molecular Carcinogenesis

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Pancreatic carcinoma (PC) is a type of highly lethal malignant tumor that has unfavorable outcomes. One major challenge in improving clinical outcomes is to identify novel biomarkers for prognosis. In this study, we developed an online consensus survival tool for pancreatic adenocarcinoma (OSpaad), which allows researchers and clinicians to analyze the prognostic value of selected genes in PC. OSpaad contains 1319 unique PC cases that have both gene expression data and correspondent clinical data from seven individual cohorts and provides four survival terms including overall survival, disease‐specific survival, disease‐free interval, progression‐free interval for prognosis evaluation. To meet the different research needs, OSpaad allows users to limit survival analysis in subgroups by selecting different terms of clinical confounding factors such as TNM stage, sex, smoking time, lymph invasion, and race. Moreover, we showed that 97% (116 out of 120) previously reported prognostic biomarkers, including ERBB2, TP53, EGFR and so forth, were validated and confirmed their prognostic significance in OSpaad, demonstrating the well performance of survival analysis in OSpaad. OSpaad is a user‐friendly online tool with a straightforward interface allowing clinicians and basic research scientists with even a limited bioinformatics background to easily screen and evaluate the prognostic value of genes in a large PC cohort. This online tool can be accessed at http://bioinfo.henu.edu.cn/PAAD/PAADList.jsp.

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<p>OSacc: Gene Expression-Based Survival Analysis Web Tool For Adrenocortical Carcinoma</p>

Cited by 19 publications

References 30 publications

The Application of Deep Learning in Cancer Prognosis Prediction

The Application of Deep Learning in Cancer Prognosis Prediction

Comprehensive Review of Web Servers and Bioinformatics Tools for Cancer Prognosis Analysis

OSpaad: An online tool to perform survival analysis by integrating gene expression profiling and long‐term follow‐up data of 1319 pancreatic carcinoma patients

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