Integration of multimodal RNA-seq data for prediction of kidney cancer survival

Schwartz, Matthew C.; Park, Martin; Phan, John H.; Wang, May D.

doi:10.1109/bibm.2015.7359913

Cited by 7 publications

(6 citation statements)

References 9 publications

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“…More elaborate methods are another option, such as the “SymmetricalUncertAttributeSetEval” (of the Waikato Environment for Knowledge Analysis) and the “Fast Correlation Based Feature” algorithm [ 29 , 30 ], and these methods are based on performance in terms of discriminatory power (ROC) or on an SVM model [ 30 ]. It is also worth mentioning the minimum redundancy maximum relevance method [ 42 ], feature selection by the “Fast Correlation Based Feature” algorithm or joint statistical measures and logistic regression [ 32 ]. Finally, Ping et al used the random forests algorithm for variable selection, after calculating the adjusted false discovery rate [ 33 ].…”

Section: Artificial Intelligence-based Predictors In Rccmentioning

confidence: 99%

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The Role of Artificial Intelligence in the Diagnosis and Prognosis of Renal Cell Tumors

Giulietti¹,

Cecati²,

Sabanovic³

et al. 2021

Diagnostics

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The increasing availability of molecular data provided by next-generation sequencing (NGS) techniques is allowing improvement in the possibilities of diagnosis and prognosis in renal cancer. Reliable and accurate predictors based on selected gene panels are urgently needed for better stratification of renal cell carcinoma (RCC) patients in order to define a personalized treatment plan. Artificial intelligence (AI) algorithms are currently in development for this purpose. Here, we reviewed studies that developed predictors based on AI algorithms for diagnosis and prognosis in renal cancer and we compared them with non-AI-based predictors. Comparing study results, it emerges that the AI prediction performance is good and slightly better than non-AI-based ones. However, there have been only minor improvements in AI predictors in terms of accuracy and the area under the receiver operating curve (AUC) over the last decade and the number of genes used had little influence on these indices. Furthermore, we highlight that different studies having the same goal obtain similar performance despite the fact they use different discriminating genes. This is surprising because genes related to the diagnosis or prognosis are expected to be tumor-specific and independent of selection methods and algorithms. The performance of these predictors will be better with the improvement in the learning methods, as the number of cases increases and by using different types of input data (e.g., non-coding RNAs, proteomic and metabolic). This will allow for more precise identification, classification and staging of cancerous lesions which will be less affected by interpathologist variability.

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Section: Artificial Intelligence-based Predictors In Rccmentioning

confidence: 99%

“…A study published in 2015 focused on prediction of kidney cancer survival (< or ≥5 years) using TCGA RNA-seq data of 220 patients [ 42 ]. It was a complex study because the authors tested different datasets for training machine learning algorithms.…”

Section: Artificial Intelligence-based Predictors In Rccmentioning

confidence: 99%

The Role of Artificial Intelligence in the Diagnosis and Prognosis of Renal Cell Tumors

Giulietti¹,

Cecati²,

Sabanovic³

et al. 2021

Diagnostics

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“…Many groups have tried to use TCGA data to predict the prognosis of patients affected by various tumors using machine learning approaches, with varying levels of success. 3 - 8 …”

Section: Introductionmentioning

confidence: 99%

“…Many groups have tried to use TCGA data to predict the prognosis of patients affected by various tumors using machine learning approaches, with varying levels of success. [3][4][5][6][7][8] Random Forest 9 is a simple yet effective Machine Learning algorithm that proved to be a successful predictor when using structured data such as RNA expression analysis. 10 It has low overfitting and a simple feature importance scoring function that is based on the Mean Decrease in Impurity function (Gini Importance).…”

Section: Introductionmentioning

confidence: 99%

SurviveAI: Long Term Survival Prediction of Cancer Patients Based on Somatic RNA-Seq Expression

et al. 2022

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Motivation: Prediction of cancer outcome is a major challenge in oncology and is essential for treatment planning. Repositories such as The Cancer Genome Atlas (TCGA) contain vast amounts of data for many types of cancers. Our goal was to create reliable prediction models using TCGA data and validate them using an external dataset. Results: For 16 TCGA cancer type cohorts we have optimized a Random Forest prediction model using parameter grid search followed by a backward feature elimination loop for dimensions reduction. For each feature that was removed, the model was retrained and the area under the curve of the receiver operating characteristic (AUC-ROC) was calculated using test data. Five prediction models gave AUC-ROC bigger than 80%. We used Clinical Proteomic Tumor Analysis Consortium v3 (CPTAC3) data for validation. The most enriched pathways for the top models were those involved in basic functions related to tumorigenesis and organ development. Enrichment for 2 prediction models of the TCGA-KIRP cohort was explored, one with 42 genes (AUC-ROC = 0.86) the other is composed of 300 genes (AUC-ROC = 0.85). The most enriched networks for both models share only 5 network nodes: DMBT1, IL11, HOXB6, TRIB3, PIM1. These genes play a significant role in renal cancer and might be used for prognosis prediction and as candidate therapeutic targets. Availability And Implementation: The prediction models were created and tested using Python SciKit-Learn package. They are freely accessible via a friendly web interface we called surviveAI at https://tinyurl.com/survivea i.

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“…In medicine, predictive data analytics is a crucial challenge to improve the diagnosis and the monitoring of patients. Machine learning for predictive data analytics in medicine is now used in many fields: oncology (Adegoke et al., 2017; Ammad‐Ud‐Din et al., 2017; Armero et al., 2016; Borisov et al., 2017; Coley et al., 2017; Hoogendoorn et al., 2016; Kim & Cho, 2015; Nagarajan & Upreti, 2017; Schwartzi et al., 2015), neurology (Ertuğrul et al., 2016; Jeon et al., 2017; Khan et al., 2014; Kim et al., 2015; Kramer et al., 2017; Tripoliti et al., 2013; Xia et al., 2015; Yuvaraj et al., 2014), geriatric (Deschamps et al., 2016; Fabris et al., 2016; Ivascu et al., 2017; Kabeshova et al., 2016); Wan et al., 2015), epidemiology (Khanna & Sharma, 2018; Modu et al., 2017; Wang et al., 2016), pharmacology (Bakal et al., 2018; Bendtsen et al., 2017; Huang et al., 2017; Luo et al., 2015; Oztaner et al., 2015), … (Alghamdi et al., 2016; Delibašić et al., 2018; Hu et al., 2017; Jarmulski et al., 2018; Jing et al., 2016; Montoye et al., 2017; Oztekin et al., 2018; Saleh et al., 2017; Sanz et al., 2017).…”

Section: Introductionmentioning

confidence: 99%

Machine learning for predictive data analytics in medicine: A review illustrated by cardiovascular and nuclear medicine examples

Jamin

Ábrahám

Humeau-Heurtier

2020

Clin Physio Funct Imaging

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With the evidence-based medicine, doctors have settings and data from which they can evaluate the risk-benefit ratio of a treatment. They can also analyse different information to make risk-based choices. With the emergence of new technologies in medicine, a large amount of data is recorded offering interesting perspectives with machine learning for predictive data analytics. However, the collection, storage and use of data raise ethical and security issues. Internet of things (IoT) is a field that groups all the systems that can be identified, perform measurements and do tasks while being open on the Internet. It is an emerging field (Stojkoska & Trivodaliev, 2017; Whitmore et al., 2015) that leads to various IoT applications in healthcare (Islam et al., 2015; Yin et al., 2016). Pavel et al. (2013) proposed to divide the future of IoT health care into four parts: patient-based applications, physician-based applications, treatment and prevention

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Integration of multimodal RNA-seq data for prediction of kidney cancer survival

Cited by 7 publications

References 9 publications

The Role of Artificial Intelligence in the Diagnosis and Prognosis of Renal Cell Tumors

The Role of Artificial Intelligence in the Diagnosis and Prognosis of Renal Cell Tumors

SurviveAI: Long Term Survival Prediction of Cancer Patients Based on Somatic RNA-Seq Expression

Machine learning for predictive data analytics in medicine: A review illustrated by cardiovascular and nuclear medicine examples

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