Reproducing kernel-based semiparametric functional smoothed score estimation with binary responses by Meichen Liu (8168148)

S1 Code - by Gaosha Li (20570760)

<i>hi</i>PRS algorithm process flow. by Michela C. Massi (14599915)

“…<b>(C)</b> The whole training data is then scanned, searching for these sequences and deriving a re-encoded dataset where interaction terms are binary features (i.e., 1 if sequence <i>i</i> is observed in <i>j</i>-th patient genotype, 0 otherwise). …”

Schematic representation of semi-automated algorithm for organoids analyzing. by Anna Demchenko (18449439)

Data_Sheet_2_Computational Neural Modeling of Auditory Cortical Receptive Fields.pdf by Jordan D. Chambers (6732224)

Data_Sheet_1_Computational Neural Modeling of Auditory Cortical Receptive Fields.PDF by Jordan D. Chambers (6732224)

Data_Sheet_3_Computational Neural Modeling of Auditory Cortical Receptive Fields.PDF by Jordan D. Chambers (6732224)

Table_1_Screening of Long Non-coding RNAs Biomarkers for the Diagnosis of Tuberculosis and Preliminary Construction of a Clinical Diagnosis Model.docx by Juli Chen (12187358)

“…Background<p>Pathogenic testing for tuberculosis (TB) is not yet sufficient for early and differential clinical diagnosis; thus, we investigated the potential of screening long non-coding RNAs (lncRNAs) from human hosts and using machine learning (ML) algorithms combined with electronic health record (EHR) metrics to construct a diagnostic model.…”

Malware detection workflow in compressed archives by Paul A. Gagniuc (1818325)

Distributions of model parameters mirror differentially expressed ion channel genes. by Daniele Linaro (227790)

Supplementary Material for: Penalized Logistic Regression Analysis for Genetic Association Studies of Binary Phenotypes by Yu Y. (3096192)

“…Our estimate of m is the maximizer of a marginal likelihood obtained by integrating the latent log-ORs out of the joint distribution of the parameters and observed data. We consider two approximate approaches to maximizing the marginal likelihood: (i) a Monte Carlo EM algorithm (MCEM) and (ii) a Laplace approximation (LA) to each integral, followed by derivative-free optimization of the approximation. …”

Latent state estimations for experimental data with no stressors. by Samiul Alam (18563323)

Latent state estimations for experimental data with interruptions. by Samiul Alam (18563323)

Latent state estimations for experimental data with time limit. by Samiul Alam (18563323)

Latent state estimations for experimental data with interruptions for subject 18. by Samiul Alam (18563323)

Latent state estimations for experimental data with no stressors for subject 22. by Samiul Alam (18563323)

Latent state estimations for experimental data with no stressors for subject 17. by Samiul Alam (18563323)

Latent state estimations for experimental data with interruptions for subject 13. by Samiul Alam (18563323)

Latent state estimations for experimental data with no stressors for subject 3. by Samiul Alam (18563323)

Latent state estimations for experimental data with no stressors for subject 15. by Samiul Alam (18563323)