Analysis of full-length Ste5 (1–917) coding sequence with bioinformatics algorithms. by Francis W. Farley (17199744)

The cost function Φ(<i>x</i>, <i>w</i>) versus the number of iterations for different Gaussian noise levels: (a) <i>σ</i> = 10⁻² and (b) <i>σ</i> = 10⁻³.... by Limin Ma (556873)

Highly Accurate Prediction of Core Spectra of Molecules at Density Functional Theory Cost: Attaining Sub-electronvolt Error from a Restricted Open-Shell Kohn–Sham Approach by Diptarka Hait (2835971)

“…ROKS/SGM with the modern SCAN/ωB97X-V functionals is found to predict the K-edge of C, N, O, and F to a root mean squared error of ∼0.3 eV. …”

Highly Accurate Prediction of Core Spectra of Molecules at Density Functional Theory Cost: Attaining Sub-electronvolt Error from a Restricted Open-Shell Kohn–Sham Approach by Diptarka Hait (2835971)

“…ROKS/SGM with the modern SCAN/ωB97X-V functionals is found to predict the K-edge of C, N, O, and F to a root mean squared error of ∼0.3 eV. …”

Supplementary Tables from SpatialDeX Is a Reference-Free Method for Cell-Type Deconvolution of Spatial Transcriptomics Data in Solid Tumors by Xinyi Liu (20493753)

Functions in nhppp. by Thomas A. Trikalinos (5785877)

“…The package’s functions are based on three algorithms that provably sample from a target NHPPP: the time-transformation of a homogeneous Poisson process (of intensity one) via the inverse of the integrated intensity function; the generation of a Poisson number of order statistics from a fixed density function; and the thinning of a majorizing NHPPP via an acceptance-rejection scheme. …”

Evaluation metrics graphs for different loss functions. by Huiying Zhang (200681)

Functional Connectivity for Experiment and Simulated Anesthesia and Reversal. by Bolaji P. Eniwaye (12915339)

Testing function diagram of QIMPN-PCA-DC. by Jun Zhao (59250)

Perturbations and their effects within networks. by Matthew Aguirre (9558032)

“…<b>(D)</b> KO effects as a function of network distance between two genes, and <b>(E)</b> within and across modules given by the generating algorithm. …”

Fit to Patient’s 2 data under a non-constant variance assumption. by Patricio Cumsille (6755882)

Fit to Patient’s 1 data under a non-constant variance assumption. by Patricio Cumsille (6755882)

The experimental geometric placement adopted to assess metal artifact reduction employing the new denoising convolutional neural network metal artifact reduction hybrid reconstruct... by Tsutomu Gomi (7382183)

Comparisons among the denoising convolutional neural network metal artifact reduction hybrid reconstruction (DnCNN-MARHR) algorithm and the traditional reconstruction algorithms wi... by Tsutomu Gomi (7382183)

Comparison of the top five upregulated and top five downregulated molecular function GO terms of Unipept with the molecular function GO terms from the other tools from the oral dys... by Ray Sajulga (5721518)

Exponentially attenuated sinusoidal function. by Hang Zhao (143592)

“…The Pareto optimal front was generated using MOCOA with the indicators of spectral kurtosis and KL divergence, by which the optimal intrinsic mode functions were obtained. A deep VMD-attention network based on MOCOA was developed for ECG signal classification. …”

Training loss function curve. by Mingtao Gao (21722035)

(Movie for Fig 2C) Similar to S1 Movie, but trained on a negative example, with synapse locations initiated such that synapses are clustered with same-sign input values. by Toviah Moldwin (10866708)

Machine learning analysis of 10 hub genes related to SCI was performed using eight algorithms applied to the dataset. by Tianbao Feng (21722233)

A fast algorithm for the accelerated failure time model with high-dimensional time-to-event data by Taehwa Choi (10880618)

“…This can be done by using a second-order approximation of the smoothed estimating function and coordinate decent algorithm. …”