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)

Search-based testing (Genetic Algorithm) - Chapter 11 of the book "Software Testing Automation" by Saeed Parsa (13893726)

“…</p> <p><br></p> <p>3. Algorithm</p> <p>Below is the main body of the test data generator program:</p> <p>  </p> <p>the main body of a Python program to generate test data for Python functions.…”

Suggestions to improve patient centered clinical best practices in snakebite management. by Anna Tupetz (9925857)

Subjects:

Emergent themes and codes. by Anna Tupetz (9925857)

Subjects:

Recommendations to improve scientific evidence base on snakebite management practices. by Anna Tupetz (9925857)

Subjects:

Participant characteristics. by Anna Tupetz (9925857)

Subjects:

Overview of available and utilized resources. by Anna Tupetz (9925857)

Subjects:

Who has ears, listen: Citizen Listening Program for disease prevention. by Ramiro García Pereira (2836310)

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

DataSheet_1_CL-PMI: A Precursor MicroRNA Identification Method Based on Convolutional and Long Short-Term Memory Networks.pdf by Huiqing Wang (1370457)

“…In order to overcome the limitations of existing methods, we propose a pre-miRNA identification algorithm based on a cascaded CNN-LSTM framework, called CL-PMI. …”

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)