Decision algorithm for classification of samples based on the data obtained with the new multiplex qPCR. by Fernanda Saloum de Neves Manta (8871500)

Algorithms used in this study. by Daniel Sanchez-Gomez (19065975)

“…This allowed us to develop a machine learning-based framework for the prediction of bead-forming minerals by training and benchmarking 13 of the most widely used supervised algorithms. …”

Hi-C read-pair classifications. by Matthew Z. DeMaere (6404834)

RITA classification of plasma and DBS samples. by Brian D. Rice (5429684)

Performance of the case classification system. by Quirine ten Bosch (13250509)

Interpretation of the Asante Rapid Recency Assay results is based on the presence or absence of specific lines. by Ernest L. Yufenyuy (12494849)

Evaluation metrics used in this study. by Daniel Sanchez-Gomez (19065975)

“…This allowed us to develop a machine learning-based framework for the prediction of bead-forming minerals by training and benchmarking 13 of the most widely used supervised algorithms. …”

Model benchmark. by Daniel Sanchez-Gomez (19065975)

“…This allowed us to develop a machine learning-based framework for the prediction of bead-forming minerals by training and benchmarking 13 of the most widely used supervised algorithms. …”

Class system distribution. by Daniel Sanchez-Gomez (19065975)

“…This allowed us to develop a machine learning-based framework for the prediction of bead-forming minerals by training and benchmarking 13 of the most widely used supervised algorithms. …”

Model performance before and after optimisation. by Daniel Sanchez-Gomez (19065975)

“…This allowed us to develop a machine learning-based framework for the prediction of bead-forming minerals by training and benchmarking 13 of the most widely used supervised algorithms. …”

Proof-of-concept confusion matrix. by Daniel Sanchez-Gomez (19065975)

“…This allowed us to develop a machine learning-based framework for the prediction of bead-forming minerals by training and benchmarking 13 of the most widely used supervised algorithms. …”

Evaluation metrics for VS1 and VS2. by Daniel Sanchez-Gomez (19065975)

“…This allowed us to develop a machine learning-based framework for the prediction of bead-forming minerals by training and benchmarking 13 of the most widely used supervised algorithms. …”

Image_2_Machine learning models-based on integration of next-generation sequencing testing and tumor cell sizes improve subtype classification of mature B-cell neoplasms.tif by Yafei Mu (16750227)

“…</p>Methods<p>Samples from the Catalogue Of Somatic Mutations In Cancer (COSMIC) database were collected for ML model construction and cases from our laboratory were used for ML model validation. …”

Table_1_Machine learning models-based on integration of next-generation sequencing testing and tumor cell sizes improve subtype classification of mature B-cell neoplasms.xlsx by Yafei Mu (16750227)

“…</p>Methods<p>Samples from the Catalogue Of Somatic Mutations In Cancer (COSMIC) database were collected for ML model construction and cases from our laboratory were used for ML model validation. …”

Image_1_Machine learning models-based on integration of next-generation sequencing testing and tumor cell sizes improve subtype classification of mature B-cell neoplasms.tif by Yafei Mu (16750227)

“…</p>Methods<p>Samples from the Catalogue Of Somatic Mutations In Cancer (COSMIC) database were collected for ML model construction and cases from our laboratory were used for ML model validation. …”

Identification of <i>Bacillus</i> and <i>Yersinia</i> species and hoax agents by protein profiling using microfluidic capillary electrophoresis with peak detection algorithms by Sorelle Bowman (6912863)

“…Boolean logic paths were then employed to predict the electrophoretic pattern of samples. Parameters assessed included variation within and between Experion™ Pro260 chips and the ability to discriminate between samples over time intervals, between operators and between field and laboratory analyses.…”

Image_1_A predictive model based on random forest for shoulder-hand syndrome.JPEG by Suli Yu (14947807)

“…</p>Results<p>A binary classification model was trained based on 25 handpicked features. …”

Data_Sheet_1_Development and validation of prediction models for hypertension risks: A cross-sectional study based on 4,287,407 participants.docx by Weidong Ji (129916)

“…The Borderline synthetic minority over-sampling technique was used for data balance. Non-laboratory and semi-laboratory analyses were carried out in combination with the selected features. …”

Average accuracy by feature extraction layer. by Şafak Kılıç (22227019)

“…The framework incorporates multiple feature extraction layers (CBAM, GAP, GMP, pre-final) combined with 10 distinct feature selection methods including Principal Component Analysis (PCA), Chi-square test, Random Forest importance, variance thresholding, and their intersections. Classification is performed using Support Vector Machines with RBF/Linear kernels and k-Nearest Neighbors algorithms. …”

Performance analysis by feature extraction layer. by Şafak Kılıç (22227019)

“…The framework incorporates multiple feature extraction layers (CBAM, GAP, GMP, pre-final) combined with 10 distinct feature selection methods including Principal Component Analysis (PCA), Chi-square test, Random Forest importance, variance thresholding, and their intersections. Classification is performed using Support Vector Machines with RBF/Linear kernels and k-Nearest Neighbors algorithms. …”