Amino acid sequence analysis of pB169L. by Jiaqi Li (220961)

4sU-seq Signal at Putative Enhancers in Day 3 and 4 ADE Populations. by Jurriaan Jochem Hölzenspies (20642889)

Data generation process. by Nicola Dietler (12551766)

Analysis of PPI network and hub genes. by Yubin Xue (1537042)

Functional and Structural Characterization of Mechanosensitive Piezo1 Channel in Disease by Sophia Liu (722004)

“…However, most algorithms struggled to distinguish gain-of-function (GoF) from loss-of-function (LoF) variants, with GoF variants showing lower pathogenicity scores despite clinical relevance.…”

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. …”

Table 6_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 7_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 3_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 2_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 1_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 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. …”

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.…”

Low plDDT scores correlate with poor structural quality. by Samuel Eriksson Lidbrink (21616146)

MitoDEGs in DCM. by Wenlong Kuang (20485167)

Construction of weighted co-expression network related datasets and identification of related key modules. by Wenlong Kuang (20485167)

Fits of selected conformations to experimental SANS data. by Samuel Eriksson Lidbrink (21616146)

Fit of the change in SANS intensity. by Samuel Eriksson Lidbrink (21616146)

Clustering quality across the reruns of the pipeline. by Samuel Eriksson Lidbrink (21616146)