With increasing growth of DNA sequence data, it has become an urgent demand to develop new methods to accurately predict the genes. The performance of gene detection methods mainly depend on the efficiency of splice site prediction methods. In this paper, a novel method for detecting splice sites is proposed by using a new effective DNA encoding method and AdaBoost.M1 classifier. Our proposed DNA encoding method is based on multi-scale component (MSC) and first order Markov model (MM1). It has been applied to the HS3D dataset with repeated 10 fold cross validation. The experimental results indicate that the new method has increased the classification accuracy and outperformed some current methods such as MM1-SVM, Reduced MM1-SVM, SVM-B, LVMM, DM-SVM, DM2-AdaBoost and MS C+Pos(+APR)-SVM.
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http://dx.doi.org/10.1109/EMBC.2016.7591379 | DOI Listing |
Curr Obes Rep
January 2025
Metabolism and Body Composition, Pennington Biomedical Research Center, Baton Rouge, LA, 70808, USA.
Background: Recent technological advances have introduced novel methods for measuring body composition, each with unique benefits and limitations. The choice of method often depends on the trade-offs between accuracy, cost, participant burden, and the ability to measure specific body composition compartments.
Objective: To review the considerations of cost, accuracy, portability, and participant burden in reference and emerging body composition assessment methods, and to evaluate their clinical applicability.
Eur Radiol
January 2025
Department of Information Technology, Uppsala University, 75237, Uppsala, Sweden.
Objectives: The aim is to assess the feasibility and accuracy of a novel quantitative ultrasound (US) method based on global speed-of-sound (g-SoS) measurement using conventional US machines, for breast density assessment in comparison to mammographic ACR (m-ACR) categories.
Materials And Methods: In a prospective study, g-SoS was assessed in the upper-outer breast quadrant of 100 women, with 92 of them also having m-ACR assessed by two radiologists across the entire breast. For g-SoS, ultrasonic waves were transmitted from varying transducer locations and the image misalignments between these were then related analytically to breast SoS.
Drug Dev Ind Pharm
January 2025
Institute of Pharmaceutics, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China.
Objective: Boron Neutron Capture Therapy (BNCT) is a novel precision radiotherapy. The key to BNCT application lies in the effective targeting and retention of the boron-10 (B) carrier. Among the various compounds studied in clinical settings, 4-boronophenylalanine (BPA) become the most prevalent one currently.
View Article and Find Full Text PDFJACC Cardiovasc Imaging
January 2025
Department of Nuclear Medicine, Beijing Key Laboratory of Molecular Targeted Diagnosis and Therapy in Nuclear Medicine, State Key Laboratory of Complex Severe and Rare Diseases, Center for Rare Diseases Research, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Background: Cardiac involvement in amyloid light chain (AL) amyloidosis significantly influences prognosis, necessitating timely diagnosis and meticulous risk stratification.
Objectives: This prospective study aimed to delineate the molecular phenotypes of AL cardiac amyloidosis (AL-CA) by characterizing fibro-amyloid deposition using F-florbetapir and gallium-68-labeled fibroblast activation protein inhibitor-04 (Ga-FAPI-04) positron emission tomography (PET)/computed tomography (CT) imaging. The authors also proposed a novel molecular stratification methodology for prognosis.
Mayo Clin Proc
January 2025
Division of Pediatric Cardiology, Department of Pediatric and Adolescent Medicine, Mayo Clinic, Rochester, MN; Department of Molecular Pharmacology and Experimental Therapeutics, Windland Smith Rice Sudden Death Genomics Laboratory, Mayo Clinic, Rochester, MN; Division of Heart Rhythm Services, Department of Cardiovascular Medicine, Windland Smith Rice Genetic Heart Rhythm Clinic, Mayo Clinic, Rochester, MN. Electronic address:
Objective: To test whether an artificial intelligence (AI) deep neural network (DNN)-derived analysis of the 12-lead electrocardiogram (ECG) can distinguish patients with long QT syndrome (LQTS) from those with acquired QT prolongation.
Methods: The study cohort included all patients with genetically confirmed LQTS evaluated in the Windland Smith Rice Genetic Heart Rhythm Clinic and controls from Mayo Clinic's ECG data vault comprising more than 2.5 million patients.
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