Review paper on research direction towards cancer prediction and prognosis using machine learning and deep learning models

Nimmagadda, Satyanarayana Murthy; Bethala, Chaitanya

doi:10.1007/s12652-021-03147-3

Cited by 19 publications

(10 citation statements)

References 78 publications

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“…Therefore, it is urgent to explore the process of gliomas' molecular mechanism and seek new disease diagnoses and prognostic biomarkers. Various researchers have used machine learning to prove the oncological statistical studies, especially cancer prediction and prognosis [5]. For example, machine learning techniques based on the application of MRI derived radiomics to differentiate glioblastoma [6]; a machine learning model integrated the multi-omics data to predict breast cancer survival and progression [7].…”

Section: Introductionmentioning

confidence: 99%

Machine learning for the micropeptide encoded by LINC02381 regulates ferroptosis through the glucose transporter SLC2A10 in glioblastoma

Jiang

Yang

Xu³

et al. 2022

BMC Cancer

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Glioblastoma (GBM) is the most common primary intracranial tumor in the central nervous system, and resistance to temozolomide is an important reason for the failure of GBM treatment. We screened out that Solute Carrier Family 2 Member 10 (SLC2A10) is significantly highly expressed in GBM with a poor prognosis, which is also enriched in the NF-E2 p45-related factor 2 (NRF2) signalling pathway. The NRF2 signalling pathway is an important defence mechanism against ferroptosis. SLC2A10 related LINC02381 is highly expressed in GBM, which is localized in the cytoplasm/exosomes, and LINC02381 encoded micropeptides are localized in the exosomes. The micropeptide encoded by LINC02381 may be a potential treatment strategy for GBM, but the underlying mechanism of its function is not precise yet. We put forward the hypothesis: “The micropeptide encoded by LINC02381 regulates ferroptosis through the glucose transporter SLC2A10 in GBM.” This study innovatively used machine learning for micropeptide to provide personalized diagnosis and treatment plans for precise treatment of GBM, thereby promoting the development of translational medicine. The study aimed to help find new disease diagnoses and prognostic biomarkers and provide a new strategy for experimental scientists to design the downstream validation experiments.

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Section: Introductionmentioning

confidence: 99%

Machine learning for the micropeptide encoded by LINC02381 regulates ferroptosis through the glucose transporter SLC2A10 in glioblastoma

Jiang

Yang

Xu³

et al. 2022

BMC Cancer

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“…Studies on the missing value filling method (QL-RF) based on the Q learning and random forest and integrated classification model (QXB) based on the bagging framework using fusion quantum particle swarm optimization (QPSO) and XG Boost have also been further optimized [13]. Among them, QL-RF is superior to the traditional RF filling method under G-means, F1-measure, and AUC, and QXB is significantly superior to SMOTE-RF and SMOTE-XG Boost [14].…”

Section: Introductionmentioning

confidence: 99%

Improved AHP Model and Neural Network for Consumer Finance Credit Risk Assessment

2022

Advances in Multimedia

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With the rapid expansion of the consumer financial market, the credit risk problem in borrowing has become increasingly prominent. Based on the analytic hierarchy process (AHP) and the long short-term memory (LSTM) model, this paper evaluates individual credit risk through the improved AHP and the optimized LSTM model. Firstly, the characteristic information is extracted, and the financial credit risk assessment index system structure is established. The data are input into the AHP-LSTM neural network, and the index data are fused with the AHP so as to obtain the risk level and serve as the expected output of the LSTM neural network. The results of the prewarning model after training can be used for financial credit risk assessment and prewarning. Based on LendingClub and PPDAI data sets, the experiment uses the AHP-LSTM model to classify and predict and compares it with other classification methods. Experimental results show that the performance of this method is superior to other comparison methods in both data sets, especially in the case of unbalanced data sets.

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“…, in medical applications. We can find different classification techniques in different fields, such as cardiology ( Ali et al, 2019 ), neurology ( Deng et al, 2021 ), or oncology ( Murthy & Bethala, 2021 ). Among other problems studied with the use of classification techniques, we can find anomaly detection in aerospace telemetry ( Wu et al, 2020 ) as well as some form of return to seminal Tryon’s paper—the sentiment analysis deduced from written texts ( Wazrah & Alhumoud, 2021 ) or the sophisticated topic of Fine-Grain Classification, here on the example of malware detection ( Fu et al, 2018 ).…”

Section: Introductionmentioning

confidence: 99%

Self-optimizing neural network in the classification of real valued data

Miniak-Górecka

Podlaski

Gwizdałła

2022

PeerJ Computer Science

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The classification of multi-dimensional patterns is one of the most popular and often most challenging problems of machine learning. That is why some new approaches are being tried, expected to improve existing ones. The article proposes a new technique based on the decision network called self-optimizing neural networks (SONN). The proposed approach works on discretized data. Using a special procedure, we assign a feature vector to each element of the real-valued dataset. Later the feature vectors are analyzed, and decision patterns are created using so-called discriminants. We focus on how these discriminants are used and influence the final classifier prediction. Moreover, we also discuss the influence of the neighborhood topology. In the article, we use three different datasets with different properties. All results obtained by derived methods are compared with those obtained with the well-known support vector machine (SVM) approach. The results prove that the proposed solutions give better results than SVM. We can see that the information obtained from a training set is better generalized, and the final accuracy of the classifier is higher.

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Review paper on research direction towards cancer prediction and prognosis using machine learning and deep learning models

Cited by 19 publications

References 78 publications

Machine learning for the micropeptide encoded by LINC02381 regulates ferroptosis through the glucose transporter SLC2A10 in glioblastoma

Machine learning for the micropeptide encoded by LINC02381 regulates ferroptosis through the glucose transporter SLC2A10 in glioblastoma

Improved AHP Model and Neural Network for Consumer Finance Credit Risk Assessment

Self-optimizing neural network in the classification of real valued data

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