Table 4_Predictive prioritization of genes significantly associated with biotic and abiotic stresses in maize using machine learning algorithms.xlsx by Anjan Kumar Pradhan (9386369)

“…However, only one gene Zm00001eb038720 encoding RNA-binding protein AU-1/Ribonuclease E/G, predicted by the PLSDA algorithm, was found commonly expressed under both biotic and abiotic stress. …”

Table 5_Predictive prioritization of genes significantly associated with biotic and abiotic stresses in maize using machine learning algorithms.xlsx by Anjan Kumar Pradhan (9386369)

“…However, only one gene Zm00001eb038720 encoding RNA-binding protein AU-1/Ribonuclease E/G, predicted by the PLSDA algorithm, was found commonly expressed under both biotic and abiotic stress. …”

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

“…In contrast, in Type II MADS-domain proteins, MoRFs (purple rectangles) overlap with the predicted motifs, supporting their potential functional relevance. …”

Flowchart of the entire pipeline. by Andreas Denger (12111159)

“…Finally, the iterative optimization algorithm described in Section Greedy algorithm for clustering of GO terms and in Appendix 4 is applied to the dataset, and an optimized set of functional annotations is returned.…”

Distribution of SNPs and their frequencies based on mutation classes. by Sadia Afrin Runa (22504971)

Table 1_Analysis and validation of characteristic genes in RNA sequencing datasets from heart failure patients based on multiple algorithms.docx by Yuxuan Li (1674307)

Image 1_Analysis and validation of characteristic genes in RNA sequencing datasets from heart failure patients based on multiple algorithms.tif by Yuxuan Li (1674307)

Order parameters versus temperature for amylase tasks: by Carlos A. Gomez-Uribe (21492788)

Consensus clustering base on expression of cuproptosis genes. by Hongmin Luo (2142307)

In vitro and in vivo DMS structure probing of ModT isoforms. by Tina Lenče (20428084)

Antibody pair dataset characteristics. by Katharina Waury (14203284)

Image 4_Multidisciplinary analysis of the prognosis and biological function of NUBPL in gastric cancer.tif by Luqian Liu (10814985)

“…Disulfidptosis-related proteins are essential for the preservation of protein stability and abnormal expression of disulfidptosis-related genes may be linked to cancer development and drug resistance.…”

Image 3_Multidisciplinary analysis of the prognosis and biological function of NUBPL in gastric cancer.tif by Luqian Liu (10814985)

“…Disulfidptosis-related proteins are essential for the preservation of protein stability and abnormal expression of disulfidptosis-related genes may be linked to cancer development and drug resistance.…”

Image 5_Multidisciplinary analysis of the prognosis and biological function of NUBPL in gastric cancer.tif by Luqian Liu (10814985)

“…Disulfidptosis-related proteins are essential for the preservation of protein stability and abnormal expression of disulfidptosis-related genes may be linked to cancer development and drug resistance.…”

Image 6_Multidisciplinary analysis of the prognosis and biological function of NUBPL in gastric cancer.tif by Luqian Liu (10814985)

“…Disulfidptosis-related proteins are essential for the preservation of protein stability and abnormal expression of disulfidptosis-related genes may be linked to cancer development and drug resistance.…”

Image 1_Multidisciplinary analysis of the prognosis and biological function of NUBPL in gastric cancer.tif by Luqian Liu (10814985)

“…Disulfidptosis-related proteins are essential for the preservation of protein stability and abnormal expression of disulfidptosis-related genes may be linked to cancer development and drug resistance.…”

Image 2_Multidisciplinary analysis of the prognosis and biological function of NUBPL in gastric cancer.tif by Luqian Liu (10814985)

“…Disulfidptosis-related proteins are essential for the preservation of protein stability and abnormal expression of disulfidptosis-related genes may be linked to cancer development and drug resistance.…”

PPI network and identification of hub genes. by Jun Li (6494)

Analysis of HK2-<i>SOX10</i> mRNA interaction. by Ana Luisa Dian (22258504)

The study flowchart. by Rui Guo (134395)