Illustration of recording paradigm with PyNoetic’s Stimuli generation and recording module. by Gursimran Singh (575288)

Subjects:

A typical BCI system depicting the flow and processing of EEG data. by Gursimran Singh (575288)

Subjects:

Contrast enhancement of digital images using dragonfly algorithm by Soumyajit Saha (19726163)

“…Comparisons with state-of-art methods ensure the superiority of the proposed algorithm. The Python implementation of the proposed approach is available in this <a href="https://github.com/somnath796/DA_contrast_enhancement" target="_blank">Github repository</a>.…”

Example of a typical workflow to construct a network using NeKo. by Marco Ruscone (11747264)

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

Table 2_Innate immune cell barrier-related genes inform precision prognosis in pancreatic cancer.xlsx by Qiang Luo (387063)

“…Prognostic modeling of PC was developed using 14 machine learning algorithms, with performance validated through long-term survival metrics, functional enrichment, immune infiltration analysis, and drug sensitivity profiling. …”

Table 3_Innate immune cell barrier-related genes inform precision prognosis in pancreatic cancer.xlsx by Qiang Luo (387063)

“…Prognostic modeling of PC was developed using 14 machine learning algorithms, with performance validated through long-term survival metrics, functional enrichment, immune infiltration analysis, and drug sensitivity profiling. …”

Table 4_Innate immune cell barrier-related genes inform precision prognosis in pancreatic cancer.xlsx by Qiang Luo (387063)

“…Prognostic modeling of PC was developed using 14 machine learning algorithms, with performance validated through long-term survival metrics, functional enrichment, immune infiltration analysis, and drug sensitivity profiling. …”

Table 5_Innate immune cell barrier-related genes inform precision prognosis in pancreatic cancer.xlsx by Qiang Luo (387063)

“…Prognostic modeling of PC was developed using 14 machine learning algorithms, with performance validated through long-term survival metrics, functional enrichment, immune infiltration analysis, and drug sensitivity profiling. …”

Table 1_Innate immune cell barrier-related genes inform precision prognosis in pancreatic cancer.xlsx by Qiang Luo (387063)

“…Prognostic modeling of PC was developed using 14 machine learning algorithms, with performance validated through long-term survival metrics, functional enrichment, immune infiltration analysis, and drug sensitivity profiling. …”

Table 6_Innate immune cell barrier-related genes inform precision prognosis in pancreatic cancer.xlsx by Qiang Luo (387063)

“…Prognostic modeling of PC was developed using 14 machine learning algorithms, with performance validated through long-term survival metrics, functional enrichment, immune infiltration analysis, and drug sensitivity profiling. …”

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

List of meaningful comments reported by study subjects and therapists after item completion 18, 19 and 22. by Dominique Vincent-Genod (20552594)

Agreements and disagreements between scores of the therapist’s score on the tablet (T-score) and the automatic score (A-score) from the TabMe2 software. by Dominique Vincent-Genod (20552594)

Percentage agreement and weighted Kappa coefficient between evaluation using the paper-based support or the tablet for item 18, 19 and 22. by Dominique Vincent-Genod (20552594)

Agreements and disagreements between scores obtained during items 18, 19 and 22 completion using the Paper-based support (P-score) or the Tablet (T-score). by Dominique Vincent-Genod (20552594)

Original paper-based support for item 18 (A), 19 (B) and 22 (C) and screenshots of the digital interfaces of the item 18 (D), 19 (E) and 22 (F). by Dominique Vincent-Genod (20552594)

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)

Assessment of the level of difficulty of the task from subjects (A) and therapists (B) when using the tablet compared to the paper-based support during the item 18, 19 and 22 compl... by Dominique Vincent-Genod (20552594)

Subject characteristics. by Dominique Vincent-Genod (20552594)