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Multiple myeloma (MM) is a haematological cancer that evolves from the benign precursor stage termed monoclonal gammopathy of undetermined significance (MGUS). Understanding the pivotal biomarkers, genomic events, and gene interactions distinguishing MM from MGUS can significantly contribute to early detection and an improved understanding of MM pathogenesis. This study presents a curated, comprehensive, targeted sequencing panel focusing on 295 MM-relevant genes and employing clinically oriented NGS-targeted sequencing approaches. To identify these genes, an innovative AI-powered attention model, the Bio-Inspired Graph Network Learning-based Gene-Gene Interaction (BIO-DGI) model, was devised for identifying Disease-Initiating and Disease-Transformative genes using the genomic profiles of MM and MGUS samples. The BIO-DGI model leverages gene interactions from nine protein-protein interaction (PPI) networks and analyzes the genomic features from 1154 MM and 61 MGUS samples. The proposed model outperformed baseline machine learning (ML) and deep learning (DL) models on quantitative performance metrics. Additionally, the BIO-DGI model identified the highest number of MM-relevant genes in the post-hoc analysis, demonstrating its superior qualitative performance. Pathway analysis highlighted the significance of top-ranked genes, emphasizing their role in MM-related pathways. Encompassing 9417 coding regions with a length of 2.630 Mb, the 295-gene panel exhibited superior performance, surpassing previously published panels in detecting genomic disease-initiating and disease-transformative events. The panel also revealed highly influential genes and their interactions within MM gene communities. Clinical relevance was confirmed through a two-fold univariate survival analysis, affirming the significance of the proposed gene panel in understanding disease progression. The study findings offer crucial insights into essential gene biomarkers and interactions, shaping our understanding of MM pathophysiology.Competing Interest StatementThe authors have declared no competing interest.Footnotes* The revised manuscript incorporates several enhancements to strengthen its scientific contributions: 1. Gene Panel Refinement: Thirteen additional multiple myeloma (MM)-relevant genes have been incorporated into the proposed panel, enhancing its comprehensiveness and relevance. 2. Extended Genomic Analysis: The manuscript now includes a thorough analysis of Copy Number Variations (CNVs), Structural Variations (SVs), and Loss-of-Function (LOF) mutations related to the proposed gene panel. This expanded analysis provides a more comprehensive understanding of the panel's significance in MM. 3. Pathway Analysis Refinement: The pathway analysis has been revised, underscoring MM-related pathways as the top-ranked pathways associated with the proposed gene panel. This refinement ensures a more accurate depiction of the panel's biological relevance. 4. Inclusion of Drug-Gene Interaction: A new section on drug-gene interaction has been added to spotlight potential novel therapeutic interventions. This addition enhances the manuscript's translational impact by identifying actionable genes and potential drug candidates associated with the proposed gene panel. These revisions aim to further contribute to the field by providing a more robust gene panel with expanded genomic insights and a refined understanding of its implications in multiple myeloma.

This study examines the use of large language models (LLMs) by undergraduate and graduate students for programming assignments in advanced computing classes. Unlike existing research, which primarily focuses on introductory classes and lacks in-depth analysis of actual student-LLM interactions, our work fills this gap. We conducted a comprehensive analysis involving 411 students from a Distributed Systems class at an Indian university, where they completed three programming assignments and shared their experiences through Google Form surveys. Our findings reveal that students leveraged LLMs for a variety of tasks, including code generation, debugging, conceptual inquiries, and test case creation. They employed a spectrum of prompting strategies, ranging from basic contextual prompts to advanced techniques like chain-of-thought prompting and iterative refinement. While students generally viewed LLMs as beneficial for enhancing productivity and learning, we noted a concerning trend of over-reliance, with many students submitting entire assignment descriptions to obtain complete solutions. Given the increasing use of LLMs in the software industry, our study highlights the need to update undergraduate curricula to include training on effective prompting strategies and to raise awareness about the benefits and potential drawbacks of LLM usage in academic settings.