[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-seo-333918-105":59,"doc-detail-333918-en":130},{"code":4,"msg":5,"data":6},0,"success",[7,13,18,23,28,33,38,43,48,51,55],{"id":8,"doc_module":4,"doc_module_name":9,"category_name":10,"show_sort_weight":11,"slug":12},1,"Document","Story & Novel",90,"story-novel",{"id":14,"doc_module":4,"doc_module_name":9,"category_name":15,"show_sort_weight":16,"slug":17},2,"Literature",80,"literature",{"id":19,"doc_module":4,"doc_module_name":9,"category_name":20,"show_sort_weight":21,"slug":22},4,"Exam",70,"exam",{"id":24,"doc_module":4,"doc_module_name":9,"category_name":25,"show_sort_weight":26,"slug":27},5,"Comic",60,"comic",{"id":29,"doc_module":4,"doc_module_name":9,"category_name":30,"show_sort_weight":31,"slug":32},6,"Technology",50,"technology",{"id":34,"doc_module":4,"doc_module_name":9,"category_name":35,"show_sort_weight":36,"slug":37},7,"Healthcare",40,"healthcare",{"id":39,"doc_module":4,"doc_module_name":9,"category_name":40,"show_sort_weight":41,"slug":42},8,"Research & Report",30,"research-report",{"id":44,"doc_module":4,"doc_module_name":9,"category_name":45,"show_sort_weight":46,"slug":47},9,"Religion & Spirituality",20,"religion-spirituality",{"id":46,"doc_module":4,"doc_module_name":9,"category_name":49,"show_sort_weight":46,"slug":50},"World Cup","world-cup",{"id":52,"doc_module":4,"doc_module_name":9,"category_name":53,"show_sort_weight":52,"slug":54},10,"Lifestyle","lifestyle",{"id":56,"doc_module":4,"doc_module_name":9,"category_name":57,"show_sort_weight":24,"slug":58},19,"General","general",{"code":4,"msg":60,"data":61},"ok",{"site_id":62,"language":63,"slug":64,"title":65,"keywords":66,"description":67,"schema_data":68,"social_meta":123,"head_meta":125,"extra_data":127,"updated_unix":129},105,"en","resisensenet-hybrid-neural-network-model-for-predicting-drug-sensitivity-and-repurposing-in-breast-cancer","ResisenseNet hybrid neural network model for predicting drug sensitivity and repurposing in breast Cancer","","Breast cancer is a leading cause of mortality among women worldwide, and drug resistance driven by transcription factor dysregulation and genomic mutations limits effective therapy. The paper presents ResisenseNet, a hybrid predictive framework combining 1D-CNN + LSTM with a DNN to learn long-range temporal patterns from amino-acid sequences and transcription-factor data. Model evaluation reports validation accuracy of 0.9794 and loss of 0.042, supported by comparisons to state-of-the-art methods and ablation studies. ResisenseNet is then used for drug repurposing across 14 cancer types, highlighting increased sensitivity of LGG and LUAD drugs for breast cancer, and prioritizing compounds without prior breast-cancer anticancer history.",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/resisensenet-hybrid-neural-network-model-for-predicting-drug-sensitivity-and-repurposing-in-breast-cancer/333918/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/resisensenet-hybrid-neural-network-model-for-predicting-drug-sensitivity-and-repurposing-in-breast-cancer/333918.png","ImageObject",300,407,{"name":92,"@type":93},"Riley","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-30","2026-09-21",true,{"@type":102,"interactionType":103,"userInteractionCount":29},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"What problem does ResisenseNet address in breast cancer treatment?","Question",{"text":112,"@type":113},"It addresses drug resistance by predicting drug sensitivity and enabling repurposing of anticancer compounds for breast cancer.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"What model architecture does ResisenseNet use?",{"text":117,"@type":113},"ResisenseNet integrates 1D-CNN + LSTM and a DNN, learning from transcription factor expression, genomic markers, drugs, and molecular descriptors.",{"name":119,"@type":110,"acceptedAnswer":120},"How was ResisenseNet validated and applied for drug repurposing?",{"text":121,"@type":113},"Validation compared performance with state-of-the-art models and included ablation studies, and the repurposing analysis screened existing anticancer drugs across 14 cancers with emphasis on breast cancer.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},333918,1790119337,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":29,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":139,"language":140,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":67,"update_tm":144,"read_time":36,"preview_page_count":4},1374391975076,"https://ap-avatar.wpscdn.com/avatar/14000253ca4ec9f6853?x-image-process=image/resize,m_fixed,w_180,h_180&k=1783305029341752051","[www. nature.com/scientificreports](www. nature.com/scientificreports)  \nOPEN  \nResisenseNet hybrid neural network model for predicting drug sensitivity and repurposing in breast Cancer  \nAnush Karampuri, Bharath Kumar Jakkula & Shyam Perugu *  \nBreast cancer remains a leading cause of mortality among women worldwide, with drug resistance driven by transcription factors and mutations posing significant challenges. To address this, we present ResisenseNet, a predictive model for drug sensitivity and resistance. ResisenseNet integrates transcription factor expression, genomic markers, drugs, and molecular descriptors, employing a hybrid architecture of 1D-CNN + LSTM and DNN to effectively learn long-range and temporal patterns from amino acid sequences and transcription factor data. The model demonstrated exceptional predictive accuracy, achieving a validation accuracy of 0.9794 and a loss value of 0.042. Comprehensive validation included comparisons with state-of-the-art models and ablation studies, confirming the robustness of the developed architecture. ResisenseNet has been applied to repurpose existing anticancer drugs across 14 different cancers, with a focus on breast cancer. Among the malignancies studied, drugs targeting Low-grade Glioma (LGG) and Lung Adenocarcinoma (LUAD) showed increased sensitivity to breast cancer as per ResisenseNet’s assessment. Further evaluation of the predicted sensitive drugs revealed that 14 had no prior history of anticancer activity against breast cancer. These drugs target key signaling pathways involved in breast cancer, presenting novel therapeutic opportunities. ResisenseNet addresses drug resistance by filtering ineffective compounds and enhancing chemotherapy for breast cancer. In vitro studies on sensitive drugs provide valuable insights into breast cancer prognosis, contributing to improved treatment strategies.  \nKeywords Drug resistance, Breast adenocarcinoma, Drug repurposing, DoRothEA, ResisenseNet  \nBreast cancer, characterized by its genetic variability, poses a significant threat to women, with drug resistance being a critical concern 1. Factors such as transcription factors and genomic mutations intricately impact treatment outcomes, emphasizing the need for targeted intervention strategies2. Transcription factors and genomic markers play a crucial role in malignancy drug resistance. Dysregulation of transcription factors, such as STAT3, leads to aberrant activities, including the maintenance of cancer stem cell phenotypes and immune response modulation, favouring drug resistance and metastasis. Activation of STAT3 via growth factor receptors like EGF, IGF, PDGF, and VEGF triggers pathways such as JAK2, G-alpha0, and G-alpha1, promoting resistance to chemotherapeutic agents and radiotherapy. Tumor microenvironments, characterized by increased CD133 + cancer stem cells, contribute to chemotherapeutic resistance via STAT activation 3. Mutations in the BCR-ABL1 Kinase Domain result in STAT3 hyperactivation and imatinib resistance in chronic myeloid leukaemia. CD44 + CD24-/ low cells activate STAT3, inducing antiapoptotic changes in breast cancer cells treated with tamoxifen, while fibronectin, IL-6, and EGF-mediated STAT3 activation lead to trastuzumab resistance 3, 4.  \nAdditionally, HIF-1 alpha expression, associated with poor prognosis, correlates with drug resistance, particularly in HER + tumours with PTEN negativity 56. HIF-1 alpha upregulates autophagy and maintains stemness, contributing to drug resistance against cytotoxic drugs such as doxorubicin, paclitaxel, gemcitabine, methotrexate, and docetaxel 5, 7. FOXO transcription factors mediate anticancer agent activity via PI3K inhibition and EGFR/HER2 inhibition8, 9, with low expression leading to resistance against paclitaxel and epirubicin 10. Mutations, such as loss of tumor suppressor gene function and alterations in critical genes, impacting NF-KB involvement in MDR1 gene expression, lead to doxorubicin resistance","cbCaikXrmJLNx3In","https://ap.wps.com/l/cbCaikXrmJLNx3In","pdf",2716936,16,"English","# Introduction\n## Drug resistance in breast cancer and transcriptional/genomic drivers\n## Need for alternative therapeutics and drug repurposing\n# Related work on drug repurposing models\n## Regression-based graph neural networks for drug-target interactions\n## Structure-based target prediction using 2D representations\n## Graph-based autoencoder and GAN approaches for interaction prediction","[{\"question\":\"What problem does ResisenseNet address in breast cancer treatment?\",\"answer\":\"It addresses drug resistance by predicting drug sensitivity and enabling repurposing of anticancer compounds for breast cancer.\"},{\"question\":\"What model architecture does ResisenseNet use?\",\"answer\":\"ResisenseNet integrates 1D-CNN + LSTM and a DNN, learning from transcription factor expression, genomic markers, drugs, and molecular descriptors.\"},{\"question\":\"How was ResisenseNet validated and applied for drug repurposing?\",\"answer\":\"Validation compared performance with state-of-the-art models and included ablation studies, and the repurposing analysis screened existing anticancer drugs across 14 cancers with emphasis on breast cancer.\"}]","ResisenseNet hybrid neural network model for predicting drug sensitivity and repurposing in breast Cancer | PDF",1790004642]