Results 61 to 70 of about 201,162 (313)

Classification Accuracy of Linear Discriminant Function using Principal Components with Multiple Correlated Variables: A Simulation based Exploration

Medical Journal of Dr. D.Y. Patil Vidyapeeth
Background: Linear Discriminant Analysis (LDA) is a powerful and widely used technique for classification with correlated variables. Principal Components (PCs) group these variables into linear combinations and produce independent variables.
Akash Mishra, Sachit Ganapathy, Sreekumaran Nair, Durgesh Shukla, Ashish Kumar Yadav, Rajaat Vohra, Kuldeep Soni   +6 more
doaj   +1 more source

ROCker Models for Reliable Detection and Typing of Short-Read Sequences Carrying β-Lactamase Genes

mSystems, 2022
Identification of genes encoding β-lactamases (BLs) from short-read sequences remains challenging due to the high frequency of shared amino acid functional domains and motifs in proteins encoded by BL genes and related non-BL gene sequences. Divergent BL
Si-Yu Zhang, Brittany Suttner, Luis M. Rodriguez-R, Luis H. Orellana, Roth E. Conrad, Fang Liu, Jessica L. Rowell, Hattie E. Webb, Amanda J. Williams-Newkirk, Andrew Huang, Konstantinos T. Konstantinidis   +10 more
doaj   +1 more source

Deep learning algorithms (VADER Baseline F1-Score, Kappa Score, and Accuracy).

, 2023
Deep learning algorithms (VADER Baseline F1-Score, Kappa Score, and Accuracy).
Abdul Ghafoor (849371), Ali Shariq Imran (8100131), Rakhi Batra (16907773), Zenun Kastrati (8100125), Sarang Shaikh (16907770), Sher Muhammad Daudpota (10340933)   +5 more
core   +1 more source

Interpreting the effects of DNA polymerase variants at the structural level

Molecular Oncology, EarlyView.
Using MAVISp and molecular dynamics simulations, we analyzed over 60 000 missense variants in POLE and POLD1 from ClinVar, COSMIC, cBioPortal, and saturation mutagenesis. Identified mechanistic indicators, including stability, binding, and long‐range, enable structural interpretation, providing ACMG‐like evidence for possible reclassification of VUS ...
Matteo Arnaudi, Karolina Krzesińska, Ludovica Beltrame, Pablo Sánchez‐Izquierdo Besora, Matteo Tiberti, Mef Nilbert, Anna Rohlin, Elena Papaleo   +7 more
wiley   +1 more source

Calibrating F1 Scores for Fair Performance Comparison of Binary Classification Models With Application to Student Dropout Prediction

IEEE Access
The F1 score has been widely used to measure the performance of machine learning models. However, it is variant to the ratio of the positive class in the training data, $\pi $ .
Hyeon Gyu Kim, Yoohyun Park
doaj   +1 more source

Deep algorithms (Weakly supervised Baseline F1-Score, Kappa Score, and Accuracy).

, 2023
Deep algorithms (Weakly supervised Baseline F1-Score, Kappa Score, and Accuracy).
Abdul Ghafoor (849371), Ali Shariq Imran (8100131), Rakhi Batra (16907773), Zenun Kastrati (8100125), Sarang Shaikh (16907770), Sher Muhammad Daudpota (10340933)   +5 more
core   +1 more source

Applicability of mitotic figure counting by deep learning: a development and pan‐cancer validation study

FEBS Open Bio, EarlyView.
In this study, we developed a deep learning method for mitotic figure counting in H&E‐stained whole‐slide images and evaluated its prognostic impact in 13 external validation cohorts from seven different cancer types. Patients with more mitotic figures per mm2 had significantly worse patient outcome in all the studied cancer types except colorectal ...
Joakim Kalsnes, Maria X. Isaksen, Frida Julbø, Manohar Pradhan, Andreas Kleppe, Sepp De Raedt, Ole‐Johan Skrede, Turid Torheim, John Arne Nesheim, Hans Martin Mohn, Hanne A. Askautrud, Karolina Cyll, Wanja Kildal, Emma Rewcastle, Melinda Lillesand, Vebjørn Kvikstad, Emiel Janssen, Robert Jones, Odd Terje Brustugun, Bjørn Brennhovd, Erik Skaaheim Haug, Lill‐Tove Rasmussen Busund, Elin Richardsen, Sigve Andersen, Tom Dønnem, Kristina Lindemann, Gunnar Kristensen, Neil A. Shepherd, Marco Novelli, Knut Liestøl, David Kerr, Håvard E. Danielsen, Tarjei S. Hveem   +32 more
wiley   +1 more source

Comparative Studies of Intelligent Algorithms for Enhancing Machine Learning Training in Diabetes Prediction

Algerian Journal of Signals and Systems
This The objective of this study is to compare the effectiveness of various intelligent algorithms in enhancing machine learning training for predicting diabetes from patient data.
Lakhdari Lahcen
doaj   +1 more source

Deep learning algorithms (TextBlob Baseline F1-Score, Kappa Score, and Accuracy).

, 2023
Deep learning algorithms (TextBlob Baseline F1-Score, Kappa Score, and Accuracy).
Abdul Ghafoor (849371), Ali Shariq Imran (8100131), Rakhi Batra (16907773), Zenun Kastrati (8100125), Sarang Shaikh (16907770), Sher Muhammad Daudpota (10340933)   +5 more
core   +1 more source

Developmental and Epileptic Encephalopathy due to Biallelic Pathogenic Variants in PIGM

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objective PIGM encodes a critical enzyme in the glycosylphosphatidylinositol (GPI)‐anchor biosynthesis pathway. While promoter‐region mutations in PIGM have been associated with a relatively mild phenotype characterized by portal vein thrombosis and absence seizures, recent evidence suggests that coding‐region mutations result in a more severe
Júlia Sala‐Coromina, Anna Marcé‐Grau, Barbara Masotto, Marta Codina, Lamia BenJemaa, Yasmina Elaribi, Mónica Martinez‐Gallo, Roger Colobran, Angel Sanchez‐Montañez, Irene Valenzuela, Yoshiko Murakami, Alfons Macaya   +11 more
wiley   +1 more source