{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/mulmarker-a-gpt-assisted-comprehensive","title":"MulMarker: a comprehensive framework for identifying multi-gene prognostic signatures","arxiv_id":"2308.11349","date":"2023-08-22","proceeding":null,"authors":["Xu Zhang","Lei Chen"],"abstract":"Prognostic signatures play an important role in clinical research, offering insights into the potential health outcomes of patients and guiding therapeutic decisions. Although single-gene prognostic biomarkers are valuable, multi-gene prognostic signatures offer deeper insights into disease progression. In this paper, we propose MulMarker, a comprehensive framework for identifying multi-gene prognostic signatures across various diseases. MulMarker comprises three core modules: a chatbot for addressing user queries, a module for identifying multi-gene prognostic signatures, and a module for generating tailored reports. Employing MulMarker, we identified a cell cycle-related prognostic signature that consists of CCNA1/2, CCNB1/2/3, CCNC, CCND1/2/3, CCNE1/2, CCNF, CCNG1/2, and CCNH. Based on the prognostic signature, we successfully stratified patients into high-risk and low-risk groups. Notably, our analysis revealed that patients in the low-risk group demonstrated a significantly higher survival rate compared to those in the high-risk group. Overall, MulMarker represents an efficient approach for the identification of multi-gene prognostic signatures. We release the code of MulMarker at https://github.com/Tina9/MulMarker.","url_abs":"https://arxiv.org/abs/2308.11349v2","url_pdf":"https://arxiv.org/pdf/2308.11349v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"mulmarker-a-gpt-assisted-comprehensive","repo_url":"https://github.com/tina9/mulmarker","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"chatbot","task_name":"Chatbot"}],"methods":[{"method_slug":"adam","method_name":"Adam"},{"method_slug":"attention","method_name":"Attention"},{"method_slug":"attention-dropout","method_name":"Attention Dropout"},{"method_slug":"bpe","method_name":"BPE"},{"method_slug":"cosine-annealing","method_name":"Cosine Annealing"},{"method_slug":"dense-connections","method_name":"Dense Connections"},{"method_slug":"discriminative-fine-tuning","method_name":"Discriminative Fine-Tuning"},{"method_slug":"dropout","method_name":"Dropout"},{"method_slug":"gpt","method_name":"GPT"},{"method_slug":"layer-normalization","method_name":"Layer Normalization"},{"method_slug":"linear-layer","method_name":"Linear Layer"},{"method_slug":"linear-warmup-with-cosine-annealing","method_name":"Linear Warmup With Cosine Annealing"},{"method_slug":"multi-head-attention","method_name":"Multi-Head Attention"},{"method_slug":"residual-connection","method_name":"Residual Connection"},{"method_slug":"softmax","method_name":"Softmax"},{"method_slug":"weight-decay","method_name":"Weight Decay"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}