If datasets are small and/or noisy, linear-regression-based algorithms for identifying functional groups outperform more complex versions. by Yuanchen Zhao (12905580)

mAP@0.5 line charts for different models. by Meiling Shi (13148944)

The losses of the training process before and after the optimization of loss function. by Peihan Wen (6324315)

The Pareto-optimal fronts on the random network with 10 added edges and its corresponding solutions. by Ruochen Zhang (3434996)

The Pareto-optimal fronts on the regular network with 10 added edges and its corresponding solutions. by Ruochen Zhang (3434996)

Table1_Fine tuning a logical model of cancer cells to predict drug synergies: combining manual curation and automated parameterization.DOCX by Åsmund Flobak (789984)

Selecting an optimal machine learning classifier. by Ramu Anandakrishnan (294535)

Testing algorithm for modified 2-tiered approaches to Lyme disease diagnosis. by Richard Porwancher (11413931)

Illustration of the representation. by Ruochen Zhang (3434996)

The topologies of edging solution with <i>k</i> = 10 on Zachary’s Karate Club network. by Ruochen Zhang (3434996)

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

“…The sequences can include from a single SNP-allele pair up to a maximum number of pairs defined by the user (<i>l</i>_max). <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). …”

Heatmap of control parameters from CoCaBO. by Xianglong Tan (2818864)

Overview of the proposed improvements to the YOLO11 model. by Beigeng Zhao (21637197)

Distribution of Bound Conformations in Conformational Ensembles for X‑ray Ligands Predicted by the ANI-2X Machine Learning Potential by Fengyang Han (14613218)

“…In this study, we systematically studied the energy distribution of bioactive conformations of small molecular ligands in their conformational ensembles using ANI-2X, a machine learning potential, in conjunction with one of our recently developed geometry optimization algorithms, known as a conjugate gradient with backtracking line search (CG-BS). …”

Distribution of Bound Conformations in Conformational Ensembles for X‑ray Ligands Predicted by the ANI-2X Machine Learning Potential by Fengyang Han (14613218)

“…In this study, we systematically studied the energy distribution of bioactive conformations of small molecular ligands in their conformational ensembles using ANI-2X, a machine learning potential, in conjunction with one of our recently developed geometry optimization algorithms, known as a conjugate gradient with backtracking line search (CG-BS). …”

Case-dependent contact reduction. by Md Rafiul Islam (11719003)

Hip versus ankle joints torque for different methods. by Hedyeh Jafari (9667127)

Phase portraits comparison of the mentioned methods for the ankle joint. by Hedyeh Jafari (9667127)

Phase portraits comparison of the mentioned methods for the hip joint. by Hedyeh Jafari (9667127)

Rendered image volumes in the MIPAV GUI. by Andrew Lauziere (14192836)