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arXiv 2025年

作者： Wang, Qing Chen, Wen-Jie Li, Bo Su, Jing Wang, Guangyu Song, Qianqian Department of Health Outcomes and Biomedical Informatics College of Medicine University of Florida FL32611 United States School of Biological and Behavioural Sciences Queen Mary University of London LondonE1 4NS United Kingdom Department of Computer and Information Science University of Macau Macau SAR Taipa999078 China Department of Biostatistics and Health Data Science Indiana University School of Medicine IN46202 United States Center for Bioinformatics and Computational Biology Houston Methodist Research Institute TX77030 United States Department of Cardiothoracic Surgery Weill Cornell Medicine Cornell University NY United States

Histopathology, particularly hematoxylin and eosin (H&E) staining, plays a critical role in diagnosing and characterizing pathological conditions by highlighting tissue morphology. However, H&E-stained images inherently lack molecular information, requiring costly and resource-intensive methods like spatial transcriptomics to map gene expression with spatial resolution. To address these challenges, we introduce HECLIP (Histology-Enhanced Contrastive Learning for Imputation of Profiles), an innovative deep learning framework that bridges the gap between histological imaging and molecular profiling. HECLIP is specifically designed to infer gene expression profiles directly from H&E-stained images, eliminating the need for expensive spatial transcriptomics assays. HECLIP leverages an advanced image-centric contrastive loss function to optimize image representation learning, ensuring that critical morphological patterns in histology images are effectively captured and translated into accurate gene expression profiles. This design enhances the predictive power of the image modality while minimizing reliance on gene expression data. Through extensive benchmarking on publicly available datasets, HECLIP demonstrates superior performance compared to existing approaches, delivering robust and biologically meaningful predictions. Detailed ablation studies further underscore its effectiveness in extracting molecular insights from histology images. Additionally, HECLIP’s scalable and cost-efficient approach positions it as a transformative tool for both research and clinical applications, driving advancements in precision medicine. The source code for HECLIP is openly available at https://***/QSong-github/HECLIP. © 2025, CC BY-NC-ND.

关键词： Molecular imaging

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KeyEE:Enhancing Low-Resource Generative Event Extraction with Auxiliary Keyword Sub-Prompt

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Big data Mining and Analytics 2024年第2期7卷 547-560页

作者： Junwen Duan Xincheng Liao Ying An Jianxin Wang Hunan Key Laboratory of Bioinformatics School of Computer Science and EngineeringCentral South UniversityChangsha 410083China Big Data Institute Central South UniversityChangsha 410083China

Event Extraction(EE)is a key task in information extraction,which requires high-quality annotated data that are often costly to *** classification-based methods suffer from low-resource scenarios due to the lack of label semantics and fine-grained *** recent approaches have endeavored to address EE through a more data-efficient generative process,they often overlook event keywords,which are vital for *** tackle these challenges,we introduce KeyEE,a multi-prompt learning strategy that improves low-resource event extraction by Event Keywords Extraction(EKE).We suggest employing an auxiliary EKE sub-prompt and concurrently training both EE and EKE with a shared pre-trained language *** the auxiliary sub-prompt,KeyEE learns event keywords knowledge implicitly,thereby reducing the dependence on annotated ***,we investigate and analyze various EKE sub-prompt strategies to encourage further research in this *** experiments on benchmark datasets ACE2005 and ERE show that KeyEE achieves significant improvement in low-resource settings and sets new state-of-the-art results.

关键词： natural language processing Event Extraction(EE) Multi-Prompt Learning(MPL) low-resource

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Cholesterol allosteric modulation of the oxytocin receptor

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Biophysical Journal 2025年

作者： Marlow, Brennica Vogel, Alexander Kuenze, Georg Pankonin, Maik Reinhardt, Franziska Stadler, Peter F. Hildebrand, Peter W. Meiler, Jens Center for Structural Biology Vanderbilt University Nashville TN United States Institute for Drug Discovery Medical Faculty Leipzig University Leipzig Germany Center for Scalable Data Analytics and Artificial Intelligence ScaDS.AI Leipzig University Leipzig Germany Interdisciplinary Center for Bioinformatics Leipzig University Leipzig Germany Chemical and Physical Biology Program Vanderbilt University Nashville TN United States Department of Chemistry Vanderbilt University Nashville TN United States Institute of Medical Physics and Biophysics Medical Faculty Leipzig University Leipzig Germany Bioinformatics Group Institute of Computer Science Interdisciplinary Center of Bioinformatics Leipzig University Leipzig Germany Max-Planck-Institute for Mathematics in the Sciences Leipzig Germany Institute of Theoretical Chemistry University of Vienna Wien Austria Facultad de Ciencias Universidad National de Colombia Sede Bogota Colombia Santa Fe Institute New Mexico Santa Fe Mexico

G-protein coupled receptors are critical components in cellular signaling, mediating various physiological responses to external stimuli. Here, we investigate the intricate relationship between cholesterol and the oxytocin receptor (OXTR), focusing on the binding mechanisms and the allosteric cross talk of bound cholesterol to the orthosteric ligand binding pocket. Utilizing molecular docking and molecular dynamics simulations, we identify cholesterol binding sites both on the agonist-bound and antagonist-bound state, which show differing distributions and residence times of the cholesterol molecules. Importantly, both methods converge on several key sites, demonstrating strong predictive overlap. Notably, one such site, and several sites detected by our molecular dynamics approach, also coincide with electron density observed in an experimental cryo-EM map, providing orthogonal validation for computational predictions. Allosteric network analysis uncovers the distinct pathways through which cholesterol may affect ligand-mediated receptor signaling, highlighting the significance of one site on the extracellular leaflet between TM4 and TM5, and two sites on the intracellular leaflet between TM2, TM3, and TM4 and between TM4 and TM5 in transmitting allosteric signals to the orthosteric pocket. These findings provide insights into the impact of cholesterol on OXTR function, emphasizing specific binding sites and signaling paths for further experimental exploration. © 2025 Biophysical Society

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Incorporating the Knowledge Distillation to Improve the EfficientNet Transfer Learning Capability

Incorporating the Knowledge Distillation to Improve the Effi...

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2020 International Conference on data science and Its Applications, ICoDSA 2020

作者： Cenggoro, Tjeng Wawan Computer Science Department School of Computer Science Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia

ISBN: (纸本)9781728182353

Due to the Deep Learning requirement for a large training dataset, Transfer Learning has become a central method in the field of Computer Vision, which heavily used Deep Learning. Since the adoption of transfer learning in the field, the performance of the models in computer vision is significantly upgraded. The common transfer learning practice in computer vision research is to use state-of-the-art architectures in the ImageNet dataset as the backbone network. The best performing architecture in ImageNet is currently held by EfficientNet, which is a set of architectures that were progressively grown from a baseline architecture. By observing the EfficientNet development approach, it is natural to hypothesize that the smaller EfficientNet architecture is capable to contain the knowledge of the larger architecture. This hypothesis comes from the fact that the larger EfficientNet architecture was grown from the smaller architecture. Therefore, in this study, we experimented if it is beneficial to transfer the knowledge from the larger architecture to the smaller architecture of the EfficientNet. To achieve this goal, we proposed a transfer learning method that uses Knowledge Distillation as the knowledge transfer mechanism from the larger to smaller architecture. We found that the proposed method is able to upgrade the performance of each of the EfficientNet architecture. Several smaller architectures are even able to outperform the larger architecture that was trained using the standard transfer learning method. © 2020 IEEE.

关键词： Distillation

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The role of MLH1, MSH2 and MSH6 in the development of colorectal cancer in Uganda

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BMC Cancer 2025年第1期25卷 1-13页

作者： Wismayer, Richard Matthews, Rosie Whalley, Celina Kiwanuka, Julius Kakembo, Fredrick Elishama Thorn, Steve Wabinga, Henry Odida, Michael Tomlinson, Ian Department of Surgery Masaka Regional Referral Hospital Masaka Uganda Department of Surgery Faculty of Health Sciences Equator University of Science and Technology Masaka Uganda Department of Surgery Faculty of Health Sciences Habib Medical School IUIU University Kampala Uganda Department of Pathology School of Biomedical Sciences College of Health Sciences Makerere University Kampala Uganda Institute of Genetics and Cancer College of Medicine and Veterinary Medicine University of Edinburgh Edinburgh United Kingdom Institute of Cancer and Genomic Sciences College of Medical and Dental Sciences University of Birmingham Birmingham United Kingdom Department of Epidemiology and Biostatistics College of Health Sciences Makerere University Kampala Uganda Department of Immunology and Molecular Biology School of Biomedical Sciences College of Health Sciences Makerere University Kampala Uganda Department of Pathology Faculty of Medicine Gulu University Gulu Uganda Department of Oncology University of Oxford Oxford United Kingdom African Centre of Excellence in Bioinformatics and Data Intensive Sciences Infectious Diseases Institute Makerere University Kampala Uganda

Introduction: In Uganda, colorectal cancer (CRC) is steadily increasing according to the Kampala Cancer Registry. In the West, microsatellite instability is detected in 90% of hereditary nonpolyposis colon cancers (HNPCC) which account for 1–2% of all CRC, and 15% of sporadic CRC. Germline mutations in MLH1 and MSH2 account for 90% of HNPCC in the West, whilst the remainder of cases are due to mutations in MSH6 and PMS2. The aim of this study was to determine the microsatellite instability (MSI) status and determine the proportions of MLH1, MSH2, and MSH6 pathological mutations in Ugandan CRC patients. Methodology: This was a cross-sectional study carried out between 1st January 2008 to 15th September 2021. Patients were recruited prospectively from 16th September 2019 to 16th September 2021, from Masaka Regional Referral Hospital, Mulago National Referral Hospital, Uganda Martyrs’ Hospital Lubaga and Mengo Hospital. From 1st January 2008 to 15th September 2019, CRC FFPE tissue blocks were obtained from the archives of the Department of Pathology, Makerere University. data was abstracted from the medical case files for demographics, topography and stage. The histopathological subtype and grade of CRC were obtained by two consultant pathologists from the H&E slides. DNA was extracted from CRC formalin-fixed paraffin-embedded (FFPE) tissue blocks. Library preparation was completed using the Qiagen custom design panel. The custom panel represented 56 genes. The MLH-1, MSH2, MSH6, BRAF and KRAS genes were sequenced using the above library preparation and NGS sequencing. The MSI status was obtained if one of the MSI genes, MLH1, MSH2 or MSH6 was pathologically mutated. If none of the genes was pathologically mutated it was considered MSI negative, microsatellite stable (MSS). Immunohistochemistry was carried out to determine whether MLH1 and PMS2 was MMR proficient or deficient. Categorical data was summarized using frequencies and proportions corresponding to each of th

关键词： Colorectal cancer Immunohistochemistry Microsatellite instability (MSI) Microsatellite stable (MSS) Uganda

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Mobile applications for skin cancer detection are vulnerable to physical camera-based adversarial attacks

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Scientific reports 2025年第1期15卷 18119页

作者： Junsei Oda Kazuhiro Takemoto Department of Bioscience and Bioinformatics Kyushu Institute of Technology Iizuka Fukuoka Japan. Department of Bioscience and Bioinformatics Kyushu Institute of Technology Iizuka Fukuoka Japan. takemoto@bio.kyutech.ac.jp. Data Science and AI Research Center Kyushu Institute of Technology Iizuka Fukuoka Japan. takemoto@bio.kyutech.ac.jp.

Skin cancer is one of the most prevalent malignant tumors, and early detection is crucial for patient prognosis, leading to the development of mobile applications as screening tools. Recent advances in deep neural networks (DNNs) have accelerated the deployment of DNN-based applications for automated skin cancer detection. While DNNs have demonstrated remarkable capabilities, they are known to be vulnerable to adversarial attacks, where carefully crafted perturbations can manipulate model predictions. The vulnerability of deployed medical mobile applications to such attacks remains largely unexplored under real-world conditions. Here, we investigate the susceptibility of three DNN-based medical mobile applications to physical adversarial attacks using transparent camera stickers under black-box conditions where internal model architectures are inaccessible. Through digital experiments with various DNN architectures trained on a publicly available skin lesion dataset, we first demonstrate that camera-based adversarial patterns can achieve high transferability across different models. Using these findings, we implement physical attacks by attaching optimized transparent stickers to mobile device cameras. Our results show that these attacks successfully manipulate application predictions, particularly for melanoma images, with attack success rates reaching 50-80% across all applications while maintaining visual imperceptibility. Notably, melanoma images showed consistently higher vulnerability compared to nevus images across all tested applications. To the best of our knowledge, this is the first demonstration of real-world adversarial vulnerabilities in deployed medical mobile applications, revealing significant security concerns where prediction manipulation could affect diagnostic processes. Our study demonstrates the importance of security evaluation in deploying such applications in clinical settings.

关键词： Adversarial attacks Deep neural networks Medical imaging Security and privacy

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Characterizing Emergency Department Care for Patients With Histories of Incarceration

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JACEP Open 2025年第1期6卷 100022-100022页

作者： Huang, Thomas Socrates, Vimig Ovchinnikova, Polina Faustino, Isaac Kumar, Anusha Safranek, Conrad Chi, Ling Wang, Emily A. Puglisi, Lisa Wong, Ambrose H. Wang, Karen H. Taylor, R. Andrew Department of Emergency Medicine Yale School of Medicine New Haven CT United States Department for Biomedical Informatics and Data Science Yale University School of Medicine New Haven CT United States Program of Computational Biology and Bioinformatics Yale University New Haven CT United States SEICHE Center for Health and Justice Yale School of Medicine New Haven CT United States Equity Research and Innovation Center Yale School of Medicine Yale University New Haven CT United States

Objectives: Patients with a history of incarceration experience bias from health care team members, barriers to privacy, and a multitude of health care disparities. We aimed to assess care processes delivered in emergency departments (EDs) for people with histories of incarceration. Methods: We utilized a fine-tuned large language model to identify patient incarceration status from 480,374 notes from the ED setting. We compared socio-demographic characteristics, comorbidities, and care processes, including disposition, restraint use, and sedation, between individuals with and without a history of incarceration. We then conducted multivariable logistic regression to assess the independent correlation of incarceration history and management in the ED while adjusting for demographic characteristics, health behaviors, presentation, and past medical history. Results: We found 1734 unique patient encounters with a history of incarceration from a total of 177,987 encounters. Patients with history of incarceration were more likely to be male, Black, Hispanic, or other race/ethnicity, currently unemployed or disabled, and had smoking and substance use histories, compared with those without. This cohort demonstrated higher odds of elopement (OR: 3.59 [95% CI: 2.41–5.12]), leaving against medical advice (OR: 2.39 [95% CI: 1.46–3.67]), and being subjected to sedation (OR: 3.89 [95% CI: 3.19–4.70]) and restraint use (OR: 3.76 [95% CI: 3.06–4.57]). After adjusting for covariates, the association between incarceration and elopement remained significant (adjusted odds ratio: 1.65 [95% CI: 1.08–2.43]), while associations with other dispositions, restraint use, and sedation did not persist. Conclusion: This study identified differences in patient characteristics and care processes in the ED for patients with histories of incarceration and demonstrated the potential of using natural language processing in measuring care processes in populations that are largely hidden, but highly prev

关键词： artificial intelligence emergency medicine incarceration large language models quality of health care

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Transcriptome analysis reveals a de novo DNA element that may interact with chromatin-associated proteins in Plasmodium berghei during erythrocytic development

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Scientific Reports 2025年第1期15卷 1-18页

作者： Adaobi Okafor Benedikt Brors Ezekiel Adebiyi Yagoub Adam Applied Bioinformatics German Cancer Research Center (DKFZ) Im Neuenheimer Feld 280 69120 Heidelberg Germany Faculty of Biosciences Heidelberg University Im Neuenheimer Feld 234 69120 Heidelberg Germany Covenant University Bioinformatics Research (CUBRe) Covenant University Ota 112233 Ogun State Nigeria African Center of Excellence in Bioinformatics & Data Intensive Science Makerere University 10218 Kampala Uganda Institute of Infectious Diseases (IDI) Makerere University 10218 Kampala Uganda

The life cycle ofPlasmodiumparasites involves intricate, multistage processes that are tightly regulated by stage-specific transcription factors. These factors bind to regulatory regions within gene promoters, enabling the precise expression of genes required for each developmental stage. Despite the importance of these transcriptional mechanisms, our understanding remains limited, particularly in the rodent model organismP. *** address this, we conducted a genome-wide analysis of RNA-Seq data from different developmental stages ofP. bergheiby initially integrating data from human malaria parasitesP. falciparumandP. vivax. We identified unique transcriptional signatures acrossPlasmodiumspecies. Our analysis ofP. bergheirevealed stage-specific gene sets clustered by expression profiles and predicted regulatory motifs involved in their control. We interpreted these motifs using known binding sites for eukaryotic transcription factors including ApiAP2 proteins. Additionally, we expanded the annotation of the AGGTAA motif which resembles a de novo motif linked to erythrocytic development inP. falciparum, and identified its potential interacting proteins including members of the PfMORC and GCN5 complexes. This study enhances our understanding of gene regulation in P. berghei and provides new insights into the transcriptional dynamics underlyingPlasmodiumdevelopment.

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Streptophyte evolution: Auxin response born out of sensing tryptophan

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Current biology : CB 2025年第9期35卷 R333-R336页

作者： Erika Endriulaitytė Cäcilia F Kunz Jaccoline M S Zegers Jan de Vries Institute for Microbiology and Genetics Department of Applied Bioinformatics University of Göttingen 37077 Göttingen Germany. Institute for Microbiology and Genetics Department of Applied Bioinformatics University of Göttingen 37077 Göttingen Germany Göttingen Center for Molecular Biosciences (GZMB) Department of Applied Bioinformatics University of Göttingen 37077 Göttingen Germany Campus Institute Data Science (CIDAS) Department of Applied Bioinformatics University of Göttingen 37077 Göttingen Germany. Electronic address: devries.jan@uni-goettingen.de.

Auxin is the major phytohormone in land plants. A new study finds that auxin triggers proliferation and gene regulation in the streptophyte alga Penium margaritaceum, and importantly, tryptophan elicits a similar response. These findings open the possibility that auxin-like responses evolved before the emergence of auxin-specific regulatory networks.

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Molecular docking and molecular dynamics simulation studies of inhibitor candidates against Anopheles gambiae 3-hydroxykynurenine transaminase and implications on vector control

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Heliyon 2025年第1期11卷 e41633页

作者： Adedeji, Eunice O. Ogunlana, Olubanke O. Oduselu, Gbolahan O. Koenig, Rainer Adebiyi, Ezekiel Soremekun, Opeyemi S. Fatumo, Segun Covenant University Bioinformatics Research (CUBRe) Covenant University Ogun State Ota Nigeria Department of Biochemistry Covenant University Ogun State Ota Nigeria Department of Biology University of York York United Kingdom Covenant Applied Informatics and Communication Africa Centre of Excellence Covenant University Ota Nigeria Institute for Infectious Diseases and Infection Control (IIMK RG Systems Biology) Jena University Hospital Am Klinikum 1 Jena 07747 Germany African Center of Excellence in Bioinformatics & Data Intensive Science Makerere University Kampala 10218 Uganda Division of Applied Bioinformatics German Cancer Research Center (DKFZ) G200 Im Neuenheimer Feld 280 Heidelberg 69120 Germany The African Computational Genomics (TACG) Research Group MRC/UVRI and LSHTM Entebbe Uganda Precision Healthcare University Research Institute Queen Mary University of London United Kingdom Department of Non-Communicable Disease Epidemiology London School of Hygiene & Tropical Medicine United Kingdom

Isoxazole and oxadiazole derivatives inhibiting 3-hydroxykynurenine transaminase (3HKT) are potential larvicidal candidates. This study aims to identify more suited potential inhibitors of Anopheles gambiae 3HKT (Ag3HKT) through molecular docking and molecular dynamics simulation. A total of 958 compounds were docked against Anopheles gambiae 3HKT (PDB ID: 2CH2) using Autodock vina and Autodock4. The top three identified hits were subjected to 300 ns molecular dynamics simulation using AMBER 18 and ADMET analysis using SWISSADME predictor and ADMETSAR. Replacement of alkyl attachment on C5 of isoxazole or oxadiazole derivative with a cycloalkyl group yielded compounds with lower binding energy than their straight chain counterparts. The top three compounds were brominated compounds, 2‐[3‐(4‐bromophenyl)‐1,2‐oxazol‐5‐yl]cyclopentane‐1‐carboxylic acid, 2-[3-(4-bromophenyl)-1,2,4-oxadiazol-5-yl]cyclopentane-1-carboxylic acid, 3-[3-(4-bromo-2-methylphenyl)-1,2,4-oxadiazol-5-yl]cyclopentane-1-carboxylic acid, and they had binding energies of −8.58, −8.25, and −8.18 kcal/mol in virtual screening against 2CH2 protein target, respectively. These compounds were predicted to be less toxic than temephos, a standard larvicide and more biodegradable than previously reported inhibitors. The three compounds exhibited a greater stabilizing effect on 2CH2 protein target than 4-[3-(4-bromophenyl)-1,2,4-oxadiazol-5-yl]butanoic acid, a previously reported inhibitor candidate with good larvicidal activity on Aedes aegypti. Further thermodynamic calculations revealed that the top three compounds possessed total binding energies (ΔG_bind) of −26.64 kcal/mol, −24.26 kcal/mol and −14.11 kcal/mol, respectively, as compared to −12.02 kcal/mol for 4-[3-(4-bromophenyl)-1,2,4-oxadiazol-5-yl]butanoic acid. These compounds could be better larvicides than previously reported isoxazole or oxadiazole derivatives and safer than temephos. © 2025 The Authors

关键词： 1,2,4-Oxadiazole 3HKT Isoxazoles Larvicide Mosquito Vector control

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