Background: The aim of this study was to develop an original method to extract sets of relevant molecular biomarkers (gene sequences) that can be used for class prediction and can be included as prognostic and predictive tools.
Materials And Methods: The method is based on sequential patterns used as features for class prediction. We applied it to classify breast cancer tumors according to their histological grade.
Results: We obtained very good recall and precision for grades 1 and 3 tumors, but, like other authors, our results were less satisfactory for grade 2 tumors.
Conclusions: We demonstrated the interest of sequential patterns for class prediction of microarrays and we now have the material to use them for prognostic and predictive applications.
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http://dx.doi.org/10.1016/j.jbi.2011.03.002 | DOI Listing |
J Med Internet Res
January 2025
Univ Rennes, CHU Rennes, INSERM, LTSI - UMR 1099, F-35000 Rennes, France.
Background: To reduce the mortality related to bladder cancer, efforts need to be concentrated on early detection of the disease for more effective therapeutic intervention. Strong risk factors (eg, smoking status, age, professional exposure) have been identified, and some diagnostic tools (eg, by way of cystoscopy) have been proposed. However, to date, no fully satisfactory (noninvasive, inexpensive, high-performance) solution for widespread deployment has been proposed.
View Article and Find Full Text PDFJB JS Open Access
January 2025
School of Medicine, Rural Clinical School, University of Queensland, Toowoomba, Queensland, Australia.
Background: Although there is a known correlation between obesity and revision risk following total knee arthroplasty (TKA), there is an ongoing debate regarding the appropriateness of denying TKA solely based on the body mass index (BMI) of a patient. Our aim was to determine whether a patient's American Society of Anesthesiologists (ASA) class predicts their risks of early all-cause revision and revision for periprosthetic joint infection (PJI) following primary TKA, independent of their BMI.
Methods: Data from the Australian Orthopaedic Association National Joint Replacement Registry (AOANJRR) were obtained regarding all patients who underwent primary TKA for osteoarthritis in Australia from January 1, 2015, to December 31, 2022.
Front Artif Intell
January 2025
Department of Computer and Automatic Control, Faculty of Engineering, Tanta University, Tanta, Egypt.
Introduction: Diabetes prediction using clinical datasets is crucial for medical data analysis. However, class imbalances, where non-diabetic cases dominate, can significantly affect machine learning model performance, leading to biased predictions and reduced generalization.
Methods: A novel predictive framework employing cutting-edge machine learning algorithms and advanced imbalance handling techniques was developed.
Digit Health
January 2025
Department of Exercise Rehabilitation & Welfare, Gachon University, Incheon, Republic of Korea.
Objective: Sarcopenia, a condition characterized by the progressive loss of skeletal muscle mass and strength, poses significant challenges in research due to missing data. Incomplete datasets undermine the accuracy and reliability of studies, necessitating effective imputation techniques. This study conducts a comparative analysis of three advanced methods-multiple imputation by chained equations (MICE), support vector regression, and K-nearest neighbors (KNN)-to address data completeness issues in sarcopenia research.
View Article and Find Full Text PDFFront Oncol
January 2025
Department of Pediatrics, Children's Healthcare of Atlanta, Atlanta, GA, United States.
Background: Pediatric low-grade gliomas (pLGGs) have an overall survival of over 90%; however, patients harboring a BRAF alteration may have worse outcomes, particularly when treated with classic chemotherapy. Combined BRAF/MEK inhibition following incomplete resection demonstrated improved outcome in BRAF altered pLGG compared to combined carboplatin/vincristine chemotherapy and is now considered the standard FDA-approved treatment for this group of tumors. The aim herein was to investigate the efficacy and tolerability of single agent BRAF inhibitor treatment in BRAF altered pLGG.
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