Comparative analysis of WordNet replacement impact on data distribution. by Enock Niyonkuru (21246879)

Graphs of the regularization terms. by Tomokaze Shiratori (9635271)

“…Our method is particularly advantageous for selecting true edges when cross-validation is used to determine the number of edges. Moreover, our DC algorithm converges within a practical time frame compared to the graphical lasso.…”

Computational Pipeline for Accelerating the Design of Glycomimetics by Yao Xiao (227567)

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Motifs identified within the IDRs of the C-terminal regions of MADS-domain proteins. by Erandi Ramírez-Aguirre (22110116)

“…Type I MADS-domain proteins do not contain molecular recognition features (MoRFs; purple rectangles) near or within the identified motifs. In contrast, in Type II MADS-domain proteins, MoRFs (purple rectangles) overlap with the predicted motifs, supporting their potential functional relevance. …”

Bangdiwala’s agreement charts for item 18 (A), 19 (B) and 22 (C) comparing item scores between the Paper-based support and the Tablet. by Dominique Vincent-Genod (20552594)

Schematic of <i>P. chabaudi</i> within-host infection dynamics and fitness optimization. by Avril Wang (22404300)

“…For local optimization, an arbitrary starting spline is picked (left panel) and an optimization algorithm is used to adjust the relative weights of the basis functions until a fitness maximum is achieved (going from left to right). …”

LBQANA python code + Merged Gene Expression Dataset from GSE10810, GSE17907, GSE20711, GSE42568, GSE45827, and GSE61304 for Breast Cancer Biomarker Discovery by M RN (9866504)

Dataset of networks used in assessing the Troika algorithm for clique partitioning and community detection by Samin Aref (4683934)

“…Each network is provided in .gml format or .pkl format which can be read into a networkX graph object using standard functions from the networkX library in Python. For accessing other networks used in the study, please refer to the article for references to the primary sources of those network data.…”

Synthetic Realness: Authenticity as Algorithm (Reality Drift Working Paper Series, 2025) by Reality Drift Working Papers Series 2020-2025 (22445446)

“…<p dir="ltr">This paper explores the concept of synthetic realness: how authenticity is increasingly engineered by algorithms until the line between real and fake becomes functionally irrelevant. …”

Clinical characteristics of the participants. by Yuki Takagi (9647692)

“…</p><p>Methods</p><p>We included 30 patients (48 eyes) who underwent HFA 24-2 Swedish Interactive Threshold Algorithm Standard and IMOvifa™ 24plus (1-2) Ambient Interactive Zippy Estimated tests on the same day and HFA 10-2 within six months. …”

Differential gene analysis and functional enrichment of the GSE775 dataset over time using the ClusterGVis package. by Wenlong Kuang (20485167)

Morpheus: A fragment-based algorithm to predict metamorphic behaviour in proteins across proteomes by Vijay Subramanian (20718933)

“…<p dir="ltr">Supplementary material for Morpheus: A fragment-based algorithm to predict metamorphic behaviour in proteins across proteomes.…”

The 3Mont summary statistics for the top 10 most significant pro-groups that are identified for BRCA molecular subtypes (HR+ and HR-) dataset. These statistics include Frequency of... by Miray Unlu Yazici (14792680)

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Distribution of pro-group sizes before and after selecting the top 10 features. by Miray Unlu Yazici (14792680)

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Internal cross-validation step of S component in 3Mont. by Miray Unlu Yazici (14792680)

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Workflow of 3Mont. by Miray Unlu Yazici (14792680)

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Performance evaluation metrics of 3Mont across different cancer types. The total sample size column lists the numbers of the tumor and control samples, respectively. A 1:1 sampling... by Miray Unlu Yazici (14792680)

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Generated network illustrating the pro-groups and their associated features that are identified by 3Mont for differentiating among the BRCA molecular subtypes (HR by Miray Unlu Yazici (14792680)

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Flow Φ of the controlled continuous time PDMP. by Benoîte de Saporta (20454662)

“…We propose modeling cancer evolution using a Piecewise Deterministic Markov Process, where patients alternate between remission and relapse phases, and control the model via long-term cost function optimization. This considers treatment side effects, visit burden, and quality of life, using noisy blood marker measurements for feedback. …”

Evidence for an AR(1) model for the Lake Michigan-Huron data. by Jesse Wheeler (18462457)