Proteomics Versus Clinical Data and Stochastic Local Search Based Feature Selection for Acute Myeloid Leukemia Patients’ Classification

Chebouba, Lokmane; Boughaci, Dalila; Guziolowski, Carito

doi:10.1007/s10916-018-0972-z

Cited by 7 publications

(4 citation statements)

References 22 publications

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“…Ganggayah et al showed that the RF algorithm produced slightly better accuracy (82.7%), in comparison to other evaluated algorithms in predicting factors for survival of breast cancer patients [35]. Chebouba et al proposed to use a stochastic local search meta-heuristic as a feature selection method combined with a random forest classifier to classify AML patients' response to treatment [36]. They used BAC and the AUC scores as evaluation criteria.…”

Section: Discussionmentioning

confidence: 99%

Survival prognostic factors in patients with acute myeloid leukemia using machine learning techniques

et al. 2021

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This paper identifies prognosis factors for survival in patients with acute myeloid leukemia (AML) using machine learning techniques. We have integrated machine learning with feature selection methods and have compared their performances to identify the most suitable factors in assessing the survival of AML patients. Here, six data mining algorithms including Decision Tree, Random Forrest, Logistic Regression, Naive Bayes, W-Bayes Net, and Gradient Boosted Tree (GBT) are employed for the detection model and implemented using the common data mining tool RapidMiner and open-source R package. To improve the predictive ability of our model, a set of features were selected by employing multiple feature selection methods. The accuracy of classification was obtained using 10-fold cross-validation for the various combinations of the feature selection methods and machine learning algorithms. The performance of the models was assessed by various measurement indexes including accuracy, kappa, sensitivity, specificity, positive predictive value, negative predictive value, and area under the ROC curve (AUC). Our results showed that GBT with an accuracy of 85.17%, AUC of 0.930, and the feature selection via the Relief algorithm has the best performance in predicting the survival rate of AML patients.

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Section: Discussionmentioning

confidence: 99%

Survival prognostic factors in patients with acute myeloid leukemia using machine learning techniques

et al. 2021

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“…For this reason, many studies have employed techniques such as principal component analysis as a filter to identify any features that do not bring any important information to the classification process [ 115 ] to enhance detection accuracy. Similarly, Chebouba et al [ 32 ] used a meta-heuristic stochastic local search technique to select the most important genes and proteins to be used in the RF-based classification of patients with AML.…”

Section: Discussionmentioning

confidence: 99%

“…Values, n (%) Pathway stage [1,6,7,29,38,40,41,50,51,53,55,64,69,82,92,94,96,[99][100][101][102]105,117,118,136,143,144] 27 (20.6) Prediction [3,10,28,30,[32][33][34]36,[44][45][46]57,58,61,66,71,72,[76][77][78]80,83,85,89,91,…”

Section: Studiesmentioning

confidence: 99%

A Review of Artificial Intelligence Applications in Hematology Management: Current Practices and Future Prospects

Alaoui¹,

Elomri²,

Qaraqe³

et al. 2022

J Med Internet Res

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Background Machine learning (ML) and deep learning (DL) methods have recently garnered a great deal of attention in the field of cancer research by making a noticeable contribution to the growth of predictive medicine and modern oncological practices. Considerable focus has been particularly directed toward hematologic malignancies because of the complexity in detecting early symptoms. Many patients with blood cancer do not get properly diagnosed until their cancer has reached an advanced stage with limited treatment prospects. Hence, the state-of-the-art revolves around the latest artificial intelligence (AI) applications in hematology management. Objective This comprehensive review provides an in-depth analysis of the current AI practices in the field of hematology. Our objective is to explore the ML and DL applications in blood cancer research, with a special focus on the type of hematologic malignancies and the patient’s cancer stage to determine future research directions in blood cancer. Methods We searched a set of recognized databases (Scopus, Springer, and Web of Science) using a selected number of keywords. We included studies written in English and published between 2015 and 2021. For each study, we identified the ML and DL techniques used and highlighted the performance of each model. Results Using the aforementioned inclusion criteria, the search resulted in 567 papers, of which 144 were selected for review. Conclusions The current literature suggests that the application of AI in the field of hematology has generated impressive results in the screening, diagnosis, and treatment stages. Nevertheless, optimizing the patient’s pathway to treatment requires a prior prediction of the malignancy based on the patient’s symptoms or blood records, which is an area that has still not been properly investigated.

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“…In the absence of targetable mutations, these drug sensitivity screenings can provide treatment options rather than genetic abnormalities. However, HDS screening of anticancer drugs has not yet been generally applied in clinical practice for ALL patients, and it has great potential in personalized treatment, particularly in drug target problems for chemo-resistance groups (173). By performing HDS screening, we can analyze and determine optimal individual dosages combined with or without targeted drugs, which improves treatment efficacy while reducing or avoiding toxicity (174)(175)(176).…”

Section: High-throughput Drug Sensitivity Screeningmentioning

confidence: 99%

Novel Treatments for Pediatric Relapsed or Refractory Acute B-Cell Lineage Lymphoblastic Leukemia: Precision Medicine Era

2022

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With the markedly increased cure rate for children with newly diagnosed pediatric B-cell acute lymphoblastic leukemia (B-ALL), relapse and refractory B-ALL (R/R B-ALL) remain the primary cause of death worldwide due to the limitations of multidrug chemotherapy. As we now have a more profound understanding of R/R ALL, including the mechanism of recurrence and drug resistance, prognostic indicators, genotypic changes and so on, we can use newly emerging technologies to identify operational molecular targets and find sensitive drugs for individualized treatment. In addition, more promising and innovative immunotherapies and molecular targeted drugs that are expected to kill leukemic cells more effectively while maintaining low toxicity to achieve minimal residual disease (MRD) negativity and better bridge hematopoietic stem cell transplantation (HSCT) have also been widely developed. To date, the prognosis of pediatric patients with R/R B-ALL has been enhanced markedly thanks to the development of novel drugs. This article reviews the new advancements of several promising strategies for pediatric R/R B-ALL.

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Proteomics Versus Clinical Data and Stochastic Local Search Based Feature Selection for Acute Myeloid Leukemia Patients’ Classification

Cited by 7 publications

References 22 publications

Survival prognostic factors in patients with acute myeloid leukemia using machine learning techniques

Survival prognostic factors in patients with acute myeloid leukemia using machine learning techniques

A Review of Artificial Intelligence Applications in Hematology Management: Current Practices and Future Prospects

Novel Treatments for Pediatric Relapsed or Refractory Acute B-Cell Lineage Lymphoblastic Leukemia: Precision Medicine Era

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