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Mesoscopic Simulation on Self-assembly of Diphenylalanine-based Analogue with Ethylenediamine Linker

Chinese Journal of Polymer Science 2025年第4期43卷 666-676页

作者： Xin-Yi Zhao Si-Qi Sun Ning Zhou Xiao-Jun Xu Yan Wang Ting-Ting Sun Department of Physics Zhejiang University of Science and TechnologyHangzhou310008China Institute of Bioinformatics and Medical Engineering Jiangsu University of TechnologyChangzhou213001China

Diphenylalanine and its analogs cause many concerns owing to their perfect self-assembly properties in the fields of biology,medicine,and *** research has shown that diphenylalanine-based analogs with ethylenediamine linkers(PA,P=phenylalanine,and A=analog)can self-assemble into spherical assemblies,which can serve as novel anticancer drug *** this work,to understand the assembly pathways,drug loading behavior,and formation mechanism of PA aggregates at the molecular level,we carried out dissipative particle dynamics(DPD)simulations of PA molecule *** simulation results demonstrate that PA molecules spontaneously assemble into nanospheres and can self-assemble into drug-loaded nanospheres upon addition of the cancer chemotherapeutic agent doxorubicin(DOX).We also found that the hydrophobic side chain beads of PA molecules exhibited a unique onion-like distribution inside the nanospheres,which was not observed in the *** onion-like nanospheres were verified by calculating the radial distribution function(RDF)of the DPD ***,based on the analysis of the percentages of different interaction components in the total nonbonded energies,main chain-side chain interactions between PA molecules may be important in the formation of onion-like nanospheres,and the synergistic effects of main chain-side chain,main chain-drug,side chain-drug,and main chain-solvent interactions are significant in the formation of drug-loaded *** findings provide new insights into the structure and self-assembly pathway of PA assemblies,which may be helpful for the design of efficient and effective drug delivery systems.

关键词： Diphenylalanine-based analogue Self-assembly Drug delivery

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Identifying candidate biomarkers for detecting bronchogenic carcinoma stages using metaheuristic algorithms based on information fusion theory

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Discover Oncology 2025年第1期16卷 1-27页

作者： Khalvati, Bagher Kavousi, Kaveh Keyhanipour, Amir Hosein Arabfard, Masoud Department of Bioinformatics Kish International Campus University of Tehran Kish Iran Laboratory of Complex Biological Systems and Bioinformatics (CBB) Department of Bioinformatics Institute of Biochemistry and Biophysics (IBB) University of Tehran Tehran Iran Computer Engineering Department Faculty of Engineering College of Farabi University of Tehran Tehran Iran Chemical Injuries Research Center Systems Biology and Poisonings Institute Baqiyatallah University of Medical Sciences Tehran Iran

Objective: Invasive lung cancer staging poses significant challenges, often requiring painful and costly biopsy procedures. This study aims to identify non-invasive biomarkers for detecting bronchogenic carcinoma and its various stages by analyzing gene expression data using bioinformatics and machine learning techniques. By leveraging these advanced computational methods, we seek to eliminate the need for surgical intervention in the diagnostic process. Methods: We utilized the TCGA-LUAD dataset, including gene expression data from healthy and cancerous samples. To identify robust biomarkers, we applied eight metaheuristic algorithms for feature selection, combined with four classification methods and two data fusion techniques to optimize performance. Results: Our approach achieved 100% accuracy in distinguishing healthy samples from cancerous ones, outperforming existing methods that reported 97% accuracy. Notably, while prior methods have struggled to separate bronchogenic carcinoma stages effectively, our research achieved an approximate accuracy of 77% in stage classification. Furthermore, using gene enrichment methods, we identified 5, 7, and 16 diagnostic biomarker candidates for stages I, II, III, and IV, respectively. Conclusion: This study demonstrates that integrating bioinformatics, gene set enrichment, and biological pathway analysis can enable non-invasive diagnostics for bronchogenic carcinoma stages. These findings hold promise for developing alternatives to traditional, invasive staging systems, potentially improving patient outcomes and reducing healthcare costs. © The Author(s) 2025.

关键词： Biomarker Bronchogenic carcinoma Feature selection algorithms Information fusion Machine learning

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Graph based recurrent network for context specific synthetic lethality prediction

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Science China(Life Sciences) 2025年第2期68卷 527-540页

作者： Yuyang Jiang Jing Wang Yixin Zhang ZhiWei Cao Qinglong Zhang Jinsong Su Song He Xiaochen Bo Academy of Medical Engineering and Translational Medicine Tianjin UniversityTianjin 300072China Department of Bioinformatics Institute of Health Service and Transfusion MedicineBeijing 100850China School of Medicine Tsinghua UniversityBeijing 100084China School of Informatics Xiamen UniversityXiamen 361005China

The concept of synthetic lethality(SL)has been successfully used for targeted *** further explore SL for cancer therapy,identifying more SL interactions with therapeutic potential are ***,graph neural network-based deep learning methods have been proposed for SL prediction,which reduce the SL search space of wet-lab based ***,these methods ignore that most SL interactions depend strongly on genetic context,which limits the application of the predicted *** this study,we proposed a graph recurrent network-based model for specific context-dependent SL prediction(SLGRN).In particular,we introduced a Graph Recurrent Network-based encoder to acquire a context-specific,low-dimensional feature representation for each node,facilitating the prediction of novel *** leveraged gate recurrent unit(GRU)and it incorporated a context-dependent-level state to effectively integrate information from all *** a result,SLGRN outperforms the state-of-the-arts models for SL *** subsequently validate novel SL interactions under different contexts based on combination therapy or patient survival *** in vitro experiments and retrospective clinical analysis,we emphasize the potential clinical significance of this context-specific SL prediction model.

关键词： synthetic lethality graph recurrent network context-specific graph combination therapy

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SpaGRA:Graph augmentation facilitates domain identification for spatially resolved transcriptomics

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Journal of Genetics and Genomics 2025年第1期52卷 93-104页

作者： Xue Sun Wei Zhang Wenrui Li Na Yu Daoliang Zhang Qi Zou Qiongye Dong Xianglin Zhang Zhiping Liu Zhiyuan Yuan Rui Gao Center of Intelligent Medicine School of Control Science and EngineeringShandong UniversityJinanShandong 250061China MOE Key Lab of Bioinformatics and Bioinformatics Division of BNRIST Department of AutomationTsinghua UniversityBeijing 100084China Institute of Precision Medicine Peking University Shenzhen HospitalShenzhenGuangdong 518036China Department of Clinical Laboratory The Second HospitalCheeloo College of MedicineShandong UniversityJinanShandong 250033China Institute of Science and Technology for Brain-Inspired Intelligence Center for Medical Research and InnovationShanghai Pudong HospitalFudan University Pudong Medical CenterFudan UniversityShanghai 200433China

Recent advances in spatially resolved transcriptomics(SRT)have provided new opportunities for characterizing spatial structures of various ***-based geometric deep learning has gained widespread adoption for spatial domain identification ***,most methods define adjacency relation between cells or spots by their spatial distance in SRT data,which overlooks key biological interactions like gene expression similarities,and leads to inaccuracies in spatial domain *** tackle this challenge,we propose a novel method,SpaGRA(https://***/sunxue-yy/SpaGRA),for automatic multi-relationship construction based on graph *** uses spatial distance as prior knowledge and dynamically adjusts edge weights with multi-head graph attention networks(GATs).This helps SpaGRA to uncover diverse node relationships and enhance message passing in geometric contrastive ***,SpaGRA uses these multi-view relationships to construct negative samples,addressing sampling bias posed by random *** results show that SpaGRA presents superior domain identification performance on multiple datasets generated from different *** SpaGRA,we analyze the functional regions in the mouse hypothalamus,identify key genes related to heart development in mouse embryos,and observe cancer-associated fibroblasts enveloping cancer cells in the latest Visium HD ***,SpaGRA can effectively characterize spatial structures across diverse SRT datasets.

关键词： Spatial domain identification Spatially resolved transcriptomics Multi-head graph attention networks Graph augmentation Geometric contrastive learning

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Single-cell transcriptomes reveal cell-type-specific and sample-specific gene function in human cancer

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Heliyon 2025年第3期11卷 e42218页

作者： Yuan, Huating Liang, Xin Zhang, Xinxin Cao, Yu College of Biology and Engineering Guizhou Medical University Guiyang China College of Bioinformatics Science and Technology Harbin Medical University Harbin China Institute of Big Health Guizhou Medical University Guiyang China

Accurate annotation of gene function in individual samples and even in each cell type is essential for understanding the pathogenesis of cancers. Single-cell RNA-sequencing (scRNA-seq) provides unprecedented resolution to decipher gene function. In order to explore how scRNA-seq contributes to the understanding of gene function in cancers, we constructed an assessment framework based on co-expression network and neighbor-voting method using 116,814 cells. Compared with bulk transcriptome, scRNA-seq recalled more experimentally verified gene functions. Surprisingly, scRNA-seq revealed cell-type-specific functions, especially in immune cells, whose expression profile recalled immune-related functions that were not discovered in cancer cells. Furthermore, scRNA-seq discovered sample-specific functions, highlighting that it provided sample-specific information. We also explored factors affecting the performance of gene function prediction. We found that 500 or more cells should be considered in the prediction with scRNA-seq, and that scRNA-seq datasets generated from 10x Genomics platform had a better performance than those from Smart-seq2. Collectively, we compared the prediction performance of bulk data and scRNA-seq data from multiple perspectives, revealing the irreplaceable role of single-cell sequencing in decoding the biological progresses in which the gene involved. © 2025

关键词： Cancer Cell-type specificity Functional heterogeneity Gene function Sample specificity Single-cell RNA-Sequencing

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An Analytical Paradigm for Exploration of Diabetes Using Machine Learning 19

An Analytical Paradigm for Exploration of Diabetes Using Mac...

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19th International Conference on Ubiquitous Information Management and Communication, IMCOM 2025

作者： Chauhan, Ritu Mishra, Aarushi Mani, Ruchi Jakhmola Yafi, Eiad Zuhairi, Megat F. Artificial Intelligence and IoT Lab Center for Computational Biology and Bioinformatics Amity University UP India Proteomics and Translational Research Lab Center for Medical Biotechnology Amity University UP India Faculty of Engineering and Information Technology University of Technology Sydney Sydney Australia Malaysian Institute of Information Technology University of Kuala Lumpur Kuala Lumpur Malaysia

ISBN: (纸本)9798331507817

Machine Learning is an approach in AI which has swiftly emerged as a transformative and powerful tool in the healthcare industry, changing the methods of diagnosing and predicting diseases. In the present situation, the application of different machine learning methods, including deep learning, ensemble techniques, and natural language processing, across various healthcare activities, such as disease prediction, medical imaging analysis, drug discovery, and tailored treatment suggestions, has offered encouraging prospects for improved diagnostics, treatment, and patient results. By utilizing a diverse range of data sources, including electronic health records, medical imaging, and wearable devices, machine learning algorithms have facilitated early detection, precise prognostication, and customized interventions. The application of machine learning algorithms into health databases is the ongoing area of research concerning future modelling predictions. The current study revolves around the utilization of descriptive analytic methods and machine learning algorithms to predict if an individual is diabetic. The effectiveness of four distinct models of machine learning, specifically Random Forest (RF), Decision Tree (DT), Support Vector Machine (SVM) and K Nearest Neighbor (KNN), is assessed and contrasted. The respective accuracies of these classifiers are noted as 89.83%, 77.16%, 84.84%, 81.16%. © 2025 IEEE.

关键词： Electronic health record

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Multi-dimensional characterization of cellular states reveals clinically relevant immunological subtypes and therapeutic vulnerabilities in ovarian cancer

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Journal of Translational Medicine 2025年第1期23卷 1-18页

作者： Zhang, Can Li, Si Guo, Jiyu Pan, Tao Zhang, Ya Gao, Yueying Pan, Jiwei Liu, Meng Yang, Qingyi Yu, Jinyang Xu, Juan Li, Yongsheng Li, Xia College of Biomedical Information and Engineering Hainan Medical University Haikou 571199 China School of Interdisciplinary Medicine and Engineering Harbin Medical University Harbin 150081 China College of Bioinformatics Science and Technology Harbin Medical University Heilongjiang Province Harbin 150081 China Department of Radiation Oncology Harbin Medical University Cancer Hospital 150 Haping Road Nangang District Heilongjiang Harbin 150040 China Department of Anesthesiology The First Affiliated Hospital of Harbin Medical University Heilongjiang Harbin China

Background: Diverse cell types and cellular states in the tumor microenvironment (TME) are drivers of biological and therapeutic heterogeneity in ovarian cancer (OV). Characterization of the diverse malignant and immunology cellular states that make up the TME and their associations with clinical outcomes are critical for cancer therapy. However, we are still lack of knowledge about the cellular states and their clinical relevance in OV. Methods: We manually collected the comprehensive transcriptomes of OV samples and characterized the cellular states and ecotypes based on a machine-learning framework. The robustness of the cellular states was validated in independent cohorts and single-cell transcriptomes. The functions and regulators of cellular states were investigated. Meanwhile, we thoroughly examined the associations between cellular states and various clinical factors, including clinical prognosis and drug responses. Results: We depicted and characterized an immunophenotypic landscape of 3,099 OV samples and 80,044 cells based on a machine learning framework. We identified and validated 32 distinct transcriptionally defined cellular states from 12 cell types and three cellular communities or ecotypes, extending the current immunological subtypes in OV. Functional enrichment and upstream transcriptional regulator analyses revealed cancer hallmark-related pathways and potential immunological biomarkers. We further investigated the spatial patterns of identified cellular states by integrating the spatially resolved transcriptomes. Moreover, prognostic landscape and drug sensitivity analysis exhibited clinically relevant immunological subtypes and therapeutic vulnerabilities. Conclusion: Our comprehensive analysis of TME helps leveraging various immunological subtypes to highlight new directions and targets for the treatment of cancer. © The Author(s) 2025.

关键词： Cellular States Clinical prognosis Drug response Immunotherapy Tumor microenvironment

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Transient chemical-mediated epigenetic modulation confers unrestricted lineage potential on human primed pluripotent stem cells

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Science China(Life Sciences) 2025年第4期68卷 1084-1101页

作者： Shi Chen Yuanyuan He Lejun Lv Bei Liu Cheng Li Hongkui Deng Jun Xu Department of Cell Biology School of Basic Medical SciencesPeking University Stem Cell Research CenterPeking University Health Science CenterPeking UniversityBeijing100191China Academy of Advanced Interdisciplinary Studies Peking UniversityBeijing100871China BeiCell Therapeutics Beijing100094China School of Life Sciences Center for BioinformaticsCenter for Statistical SciencePeking UniversityBeijing100871China MOE Engineering Research Center of Regenerative Medicine School of Basic Medical SciencesState Key Laboratory of Natural and Biomimetic DrugsPeking University Health Science Center and the MOE Key Laboratory of Cell Proliferation and DifferentiationCollege of Life SciencesPeking-Tsinghua Center for Life SciencesPeking UniversityBeijing100871China

Human primed pluripotent stem cells are capable of generating all the embryonic ***,their extraembryonic trophectoderm potentials are *** remains unclear how to expand their developmental potential to trophectoderm *** we show that transient treatment with a cocktail of small molecule epigenetic modulators imparts trophectoderm lineage potentials to human primed pluripotent stem cells while preserving their embryonic *** chemically treated cells can generate trophectoderm-like cells and downstream trophoblast stem cells,diverging into syncytiotrophoblast and extravillous trophoblast *** and CUT&Tag analyses reveal that these induced cells share transcriptional profiles with in vivo trophectoderm and cytotrophoblast,and exhibit reduced H3K27me3 modification at gene loci specific to trophoblast lineages compared with primed pluripotent *** exploration highlighted the critical roles of epigenetic modulators HDAC2,EZH1/2,and KDM5s in the activation of trophoblast lineage *** findings demonstrate that transient epigenetic resetting activates unrestricted lineage potential in human primed pluripotent stem cells,and offer new mechanistic insights into human trophoblast lineage specification and in vitro models for studying placental development and related disorders.

关键词： trophectoderm lineages human primed pluripotent stem cells epigenetic regulation small molecules

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The Effect of Multi-Task Learning on the Prediction of Neoantigen–MHC Class II Binding

IEEE Transactions on Computational Biology and Bioinformatic...

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IEEE Transactions on Computational Biology and bioinformatics 2025年第2期22卷 570-580页

作者： Kazuhiro Ikkyu Itoshi Nikaido Systems and Information Engineering Graduate School of Science and Technology University of Tsukuba Tsukuba Japan Life Science Innovation (Bioinformatics) University of Tsukuba Tsukuba Japan Laboratory for Bioinformatics Research RIKEN Center for Biosystems Dynamics Research Saitama Japan Department of Functional Genome Informatics Division of Biological Data Science Medical Research Laboratory (MRL) Institute of Integrated Research (IIR) Institute of Science Tokyo Bunkyo Japan

Neoepitopes are significant therapeutic cancer vaccine candidates, given that tumor neoepitopes induce an immune response to eliminate cancer cells. This immune activation depends on the binding affinity between the antigen peptide and the major histocompatibility complex (MHC). The epitope–MHC binding assay is a technologically difficult, time-consuming, and expensive technique. Therefore, prediction methods for these binding affinities have been developed using computational prediction approaches. However, these predictive models are trained on datasets biased toward viral peptides and some MHC alleles and are limited in their prediction of neoepitopes. In particular, because of the wide variety of MHC class II binding formats, the performance of MHC class II prediction must be improved. Here, we propose a novel deep learning model that consists of multi-task bidirectional long short-term memory (Bi-LSTM) models. Our multi-task model can predict neoepitope–MHC class II bindings from limited training data by sharing MHC class I and II training parameters. We confirm that multi-task learning significantly enhances the prediction performances of cancer antigens. Our model achieves an area under the receiver operating characteristic curve (AUC-ROC) of 82.2%, outperforming existing state-of-the-art single allele neoantigen prediction models while maintaining its strong generalization performance.

关键词： Peptides Predictive models Multitasking Antigens Cancer Data models Training Computer architecture Training data Immune system

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Enhancing Arecanut Quality Grading: A Comparison of Custom CNNs and Transfer Learning Models

Research Square

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Research Square 2025年

作者： Ghate, Dhanush D. Hegde, Pramukh S. Saishma, H. Pallavi, K.N. Ghate, Sudeep D. Department of Computer Science and Engineering NMAM Institute of Technology NITTE deemed to be University India Department of Physiology KS Hegde Medical Academy NITTE deemed to be University India Center for Bioinformatics NITTE Deemed to be University India

Effective grading of arecanut is essential for ensuring product quality, maximizing market competitiveness, and satisfying consumer preferences. However, traditional methods of arecanut grading are challenging due to variations in arecanut size, shape, and appearance, resulting in subjective and inconsistent evaluations. Deep learning can enhance this process by automating grading and using sophisticated algorithms to assess both visual and non-visual attributes, thereby increasing efficiency, accuracy, and consistency. This study presents two standalone CNN-based methodologies for automated arecanut quality grading, leveraging DenseNet121 and InceptionV3 with custom layers tailored for arecanut classification. A dataset of 2,000 high-resolution images, manually curated from farms and augmented for diversity, was used for training and validation. Eight CNN architectures - DenseNet121, EfficientNetB4, InceptionResNetV2, InceptionV3, MobileNetV2, ResNet50, VGG16, and VGG19 were evaluated. Experimental findings showed DenseNet121 and InceptionV3 achieved the highest accuracy (95.67%) and strong precision/recall scores (96%), making them the most promising models. Meanwhile, MobileNetV2 was identified as the fastest model in terms of classification speed;however, its relatively low accuracy limits its practical application in grading tasks. DenseNet121 and InceptionV3, while marginally slower at 0.015 and 0.011 seconds per image, respectively, offered a good balance between computational cost and elevated accuracy. DenseNet121 excels in feature reuse through its dense connectivity, reducing redundancy and improving performance on smaller datasets, while InceptionV3 utilizes multi-scale feature extraction to capture intricate patterns effectively. Both models demonstrate robustness under varying conditions, ensuring reliability in practical deployment scenarios. This study highlights the potential of CNNs to provide a reliable, and scalable solution for arecanut grading,

关键词： Deep learning

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