Table 5_Identifying network state-based Parkinson’s disease subtypes using clustering and support vector machine models.xlsx by Benedictor Alexander Nguchu (9984371)

“…</p>Methods<p>Here, we employ K-means and hierarchical clustering algorithms on data from the Parkinson’s Progression Markers Initiative (PPMI) to identify network-specific patterns that describe PD subtypes using the optimal number of brain features. …”

Table 7_Identifying network state-based Parkinson’s disease subtypes using clustering and support vector machine models.xlsx by Benedictor Alexander Nguchu (9984371)

“…</p>Methods<p>Here, we employ K-means and hierarchical clustering algorithms on data from the Parkinson’s Progression Markers Initiative (PPMI) to identify network-specific patterns that describe PD subtypes using the optimal number of brain features. …”

XCell Algorithm for Immune Cell Matrix. by Guolian Wu (21464168)

“…Immune cell infiltration analysis gusing the XCell algorithm showed increased levels of Monocytes, neutrophils, and other immune cells in KD, while B cells and T cells were decreased. …”

Area ratio and historical landslide numbers in different susceptibility categories for different models using the SMOTE sampling method. by Ming-zhou Lv (21402593)

Subjects:

Area ratio and historical landslide numbers in differentsusceptibility categories for different models using the SMOTE-Tomek sampling method. by Ming-zhou Lv (21402593)

Subjects:

WCST scores and protein expression levels. by Chen Chen (6544)

Infrared Spectroscopy Coupled with Machine Learning Algorithms to Investigate Vascular Dysfunction in Ovariectomy: An Animal Model Study by Tháfanys S. Travezani (20609398)

Proposing sequence genetic distances with a local greedy algorithm when genomic sampling data is available for a transmission pair. by Hannah Waddel (21520023)

Volume concentration (VC) vertical profiles (altitudes from 0 to 7.5 km above sea level) at the studied stations. by Celia Herrero del Barrio (20479635)

In this figure we show the output of the half-loop biclustering algorithm applied to the BDRN cohort in arm-1 (limited to those SNPs with maf ≥0.25). by Caroline C. McGrouther (5216153)

Subjects: “…corrected biclustering algorithm…”

Improving Social Communication Level for Mildly Autistic Children Using Video Call-based Mobile Application Game by Shahd Nooh (21344003)

“…This study improved social communication skills for preschool children aged 4-6 with mild autism by using a mobile application game that relies on video conferencing technology, personalized experience, positive reinforcement, and an AI text-to-speech algorithm. …”

The ROC curves to predict multi-classes of IA among university students in Bangladesh using ML models. by Nishat Tasnim Thity (21755858)

Subjects:

A classification scheme of bipartite randomization algorithms, based on whether the matrix row and columns sums are preserved exactly (Fixed, <i>F</i>), preserved on average (Propo... by Zachary P. Neal (9946331)

Association between GSRS and CD8⁺ T cell infiltration levels in the TCGA-KIRC cohort, and the infiltration proportion of CD8⁺ T cells was evaluated using multiple deconvolution algorithms, including TIMER (A), CIBERSORT (B), QUANTISEQ (C), MCPCOUNTER (D), and XCELL (E). by Yu Zhang (12946)

“…<p>Association between GSRS and CD8⁺ T cell infiltration levels in the TCGA-KIRC cohort, and the infiltration proportion of CD8⁺ T cells was evaluated using multiple deconvolution algorithms, including TIMER (A), CIBERSORT (B), QUANTISEQ (C), MCPCOUNTER (D), and XCELL (E).…”

Data Sheet 1_Identifying network state-based Parkinson’s disease subtypes using clustering and support vector machine models.pdf by Benedictor Alexander Nguchu (9984371)

“…</p>Methods<p>Here, we employ K-means and hierarchical clustering algorithms on data from the Parkinson’s Progression Markers Initiative (PPMI) to identify network-specific patterns that describe PD subtypes using the optimal number of brain features. …”

Classification table of provincial-level regions. by Mingming Zhai (602162)

“…<div><p>Objective</p><p>To quantitatively evaluate the drug regulatory capacity in China, aiming to optimize the drug regulatory system, precisely enhance local regulatory effectiveness, and reduce regional regulatory disparities.</p><p>Methods</p><p>Using the methods of literature research, expert interviews, investigation and analysis, the quantitative evaluation indicator system of supervision ability was established in all directions; the indicator data were collected and quantified; the indicator weight setting algorithm of the evaluation system was improved and the indicator weight was set by combining AHP and entropy method; the differences among eastern, central, and western provincial-level regions were analyzed by variance analysis; panel data were constructed for spatio-temporal evolution analysis; obstacle factor diagnosis model was used to analyze the obstacle factors.…”

Stylised representation of the SPORES algorithm, in its standard (a) and HITL (b) formulations. by Francesco Lombardi (4520995)

Precision and recall (in parentheses) of candidate methods under different heterogeneity levels (<i>h</i> = 10 or 20), different numbers of transferable source datasets (), and dif... by Lulu Pan (8977979)

This figure is similar to Fig 8, and uses the data from Figs 8, 9 and 10. by Caroline C. McGrouther (5216153)

Subjects: “…corrected biclustering algorithm…”

Parameters of stimuli used in the study. by Elnaz Nemati (21402730)