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Classifying breast cancer and fibroadenoma tissue biopsies from paraffined stain-free slides by fractal biomarkers in Fourier Ptychographic Microscopy Comput. Struct. Biotechnol. J. (IF 6.0) Pub Date : 2024-03-24 Vittorio Bianco, Marika Valentino, Daniele Pirone, Lisa Miccio, Pasquale Memmolo, Valentina Brancato, Luigi Coppola, Giovanni Smaldone, Massimiliano D’Aiuto, Gennaro Mossetti, Marco Salvatore, Pietro Ferraro
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Databases of ligand-binding pockets and protein-ligand interactions Comput. Struct. Biotechnol. J. (IF 6.0) Pub Date : 2024-03-24 Kristy A. Carpenter, Russ B. Altman
Many research groups and institutions have created a variety of databases curating experimental and predicted data related to protein-ligand binding. The landscape of available databases is dynamic, with new databases emerging and established databases becoming defunct. Here, we review the current state of databases that contain binding pockets and protein-ligand binding interactions. We have compiled
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Benchmarking feature selection and feature extraction methods to improve the performances of machine-learning algorithms for patient classification using metabolomics biomedical data Comput. Struct. Biotechnol. J. (IF 6.0) Pub Date : 2024-03-19 Justine Labory, Evariste Njomgue-Fotso, Silvia Bottini
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LiViT-Net: A U-Net-like, lightweight Transformer network for retinal vessel segmentation Comput. Struct. Biotechnol. J. (IF 6.0) Pub Date : 2024-03-19 Le Tong, Tianjiu Li, Qian Zhang, Qin Zhang, Renchaoli Zhu, Wei Du, Pengwei Hu
The intricate task of precisely segmenting retinal vessels from images, which is critical for diagnosing various eye diseases, presents significant challenges for models due to factors such as scale variation, complex anatomical patterns, low contrast, and limitations in training data. Building on these challenges, we offer novel contributions spanning model architecture, loss function design, robustness
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Deep learning-based characterization of neutrophil activation phenotypes in ex vivo human Candida blood infections Comput. Struct. Biotechnol. J. (IF 6.0) Pub Date : 2024-03-18 Arjun Sarkar, Jan-Philipp Praetorius, Marc Thilo Figge
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Exploring key features of selectivity in somatostatin receptors through molecular dynamics simulations Comput. Struct. Biotechnol. J. (IF 6.0) Pub Date : 2024-03-18 C. Guccione, S. Gervasoni, I. Öztürk, A. Bosin, P. Ruggerone, G. Malloci
Somatostatin receptors (SSTRs) are widely distributed throughout the human body and play crucial roles in various physiological processes. They are recognized as key targets for both radiotherapy and radiodiagnosis due to their overexpression in several cancer types. However, the discovery and design of selective drugs for each of the five isoforms have been significantly hindered by the lack of complete
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Alternative polyadenylation regulates the translation of metabolic and inflammation-related proteins in adipose tissue of gestational diabetes mellitus Comput. Struct. Biotechnol. J. (IF 6.0) Pub Date : 2024-03-15 Bingnan Chen, Xuyang Chen, Ruohan Hu, Hongli Li, Min Wang, Linwei Zhou, Hao Chen, Jianqi Wang, Hanwen Zhang, Xiaobo Zhou, Hua Zhang
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Mobile robots for isolation-room hospital settings: A scenario-based preliminary study Comput. Struct. Biotechnol. J. (IF 6.0) Pub Date : 2024-03-15 Hye Jin Yoo, Eui Hyun Kim, Hyeongsuk Lee
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SpatialPPI: Three-dimensional space protein-protein interaction prediction with AlphaFold Multimer Comput. Struct. Biotechnol. J. (IF 6.0) Pub Date : 2024-03-15 Wenxing Hu, Masahito Ohue
Rapid advancements in protein sequencing technology have resulted in gaps between proteins with identified sequences and those with mapped structures. Although sequence-based predictions offer insights, they can be incomplete due to the absence of structural details. Conversely, structure-based methods face challenges with respect to newly sequenced proteins. The AlphaFold Multimer has remarkable accuracy
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"Active carbon" is more advantageous to the bacterial community in the rice rhizosphere than "stable carbon" Comput. Struct. Biotechnol. J. (IF 6.0) Pub Date : 2024-03-13 Zongkun Yang, Xin Cui, Xiaoge Fan, Yefeng Ruan, Zhennan Xiang, Lingfei Ji, Han Gao, Min Zhang, Shengdao Shan, Wenbo Liu
Carbon materials are commonly used for soil carbon sequestration and fertilization, which can also affect crop growth by manipulating the rhizosphere bacterial community. However, the comparison of the differences between active carbon (e.g., organic fertilizers) and stable carbon (e.g., biochar) on rhizosphere microdomains is still unclear. Hence, a trial was implemented to explore the influence of
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Smad4 regulates TGF-β1-mediated hedgehog activation to promote epithelial-to-mesenchymal transition in pancreatic cancer cells by suppressing Gli1 activity Comput. Struct. Biotechnol. J. (IF 6.0) Pub Date : 2024-03-13 Hangcheng Guo, Zujian Hu, Xuejia Yang, Ziwei Yuan, Mengsi Wang, Chaoyue Chen, Lili Xie, Yuanyuan Gao, Wangjian Li, Yongheng Bai, Chunjing Lin
Pancreatic cancer (PC) is an aggressive and metastatic gastrointestinal tumor with a poor prognosis. Persistent activation of the TGF-β/Smad signaling induces PC cell (PCC) invasion and infiltration via epithelial-to-mesenchymal transition (EMT). Hedgehog signaling is a crucial pathway for the development of PC via the transcription factors Gli1/2/3. This study aimed to investigate the underlying molecular
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ASCOT: A web tool for the digital construction of energy minimized Ag, CuO, TiO2 spherical nanoparticles and calculation of their atomistic descriptors Comput. Struct. Biotechnol. J. (IF 6.0) Pub Date : 2024-03-12 Panagiotis D. Kolokathis, Evangelos Voyiatzis, Nikolaos K. Sidiropoulos, Andreas Tsoumanis, Georgia Melagraki, Kaido Tämm, Iseult Lynch, Antreas Afantitis
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Decoding phenotypic screening: A comparative analysis of image representations Comput. Struct. Biotechnol. J. (IF 6.0) Pub Date : 2024-03-12 Adriana Borowa, Dawid Rymarczyk, Marek Żyła, Maciej Kańdula, Ana Sánchez-Fernández, Krzysztof Rataj, Łukasz Struski, Jacek Tabor, Bartosz Zieliński
Biomedical imaging techniques such as high content screening (HCS) are valuable for drug discovery, but high costs limit their use to pharmaceutical companies. To address this issue, The JUMP-CP consortium released a massive open image dataset of chemical and genetic perturbations, providing a valuable resource for deep learning research. In this work, we aim to utilize the JUMP-CP dataset to develop
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Machine learning framework to extract the biomarker potential of plasma IgG N-glycans towards disease risk stratification Comput. Struct. Biotechnol. J. (IF 6.0) Pub Date : 2024-03-11 Konstantinos Flevaris, Joseph Davies, Shoh Nakai, Frano Vučković, Gordan Lauc, Malcolm G. Dunlop, Cleo Kontoravdi
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Role of artificial intelligence in digital pathology for gynecological cancers Comput. Struct. Biotechnol. J. (IF 6.0) Pub Date : 2024-03-11 Ya-Li Wang, Song Gao, Qian Xiao, Chen Li, Marcin Grzegorzek, Ying-Ying Zhang, Xiao-Han Li, Ye Kang, Fang-Hua Liu, Dong-Hui Huang, Ting-Ting Gong, Qi-Jun Wu
The diagnosis of cancer is typically based on histopathological sections or biopsies on glass slides. Artificial intelligence (AI) approaches have greatly enhanced our ability to extract quantitative information from digital histopathology images as a rapid growth in oncology data. Gynecological cancers are major diseases affecting women's health worldwide. They are characterized by high mortality
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Statistical analysis of sequential motifs at biologically relevant protein-protein interfaces Comput. Struct. Biotechnol. J. (IF 6.0) Pub Date : 2024-03-07 Yair Frank, Ron Unger, Hanoch Senderowitz
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Multi-omics analysis reveals the unique landscape of DLD in the breast cancer tumor microenvironment and its implications for immune-related prognosis Comput. Struct. Biotechnol. J. (IF 6.0) Pub Date : 2024-03-06 Lijun Xu, Lei Yang, Dan Zhang, Yunxi Wu, Jiali Shan, Huixia Zhu, Zhengyi Lian, Guying He, Chongyu Wang, Qingqing Wang
Cuproptosis, i.e., copper-induced programmed cell death, has potential implications in cancer therapy. However, the impact of the cuproptosis-related gene (CRG) dihydrolipoyl dehydrogenase (DLD) on breast cancer (BC) prognosis remains underexplored. We employed real-time quantitative PCR and multiplexed immunostaining techniques to quantify DLD expression in both BC and the adjacent non-cancerous tissues
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Computational methods for alignment and integration of spatially resolved transcriptomics data Comput. Struct. Biotechnol. J. (IF 6.0) Pub Date : 2024-03-05 Yuyao Liu, Can Yang
Most of the complex biological regulatory activities occur in three dimensions (3D). To better analyze biological processes, it is essential not only to decipher the molecular information of numerous cells but also to understand how their spatial contexts influence their behavior. With the development of spatially resolved transcriptomics (SRT) technologies, SRT datasets are being generated to simultaneously
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Overcoming the cognition-reality gap in robot-to-human handovers with anisotropic variable force guidance Comput. Struct. Biotechnol. J. (IF 6.0) Pub Date : 2024-03-05 Chaolong Qin, Aiguo Song, Huijun Li, Lifeng Zhu, Xiaorui Zhang, Jianzhi Wang
Object handover is a fundamental task for collaborative robots, particularly service robots. In in-home assistance scenarios, individuals often face constraints due to their posture and declining physical functions, necessitating high demands on robots for flexible real-time control and intuitive interactions. During robot-to-human handovers, individuals are limited to making perceptual judgements
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Comprehensive analysis of m6A methylome alterations after azacytidine plus venetoclax treatment for acute myeloid leukemia by nanopore sequencing Comput. Struct. Biotechnol. J. (IF 6.0) Pub Date : 2024-03-02 Zaifeng Zhang, Lili Zhang, Jiangtao Li, Ru Feng, Chang Li, Ye Liu, Gaoyuan Sun, Fei Xiao, Chunli Zhang
N6 adenosine methylation (mA), one of the most prevalent internal modifications on mammalian RNAs, regulates RNA transcription, stabilization, and splicing. Growing evidence has focused on the functional role of mA regulators on acute myeloid leukemia (AML). However, the global mA levels after azacytidine (AZA) plus venetoclax (VEN) treatment in AML patients remain unclear. In our present study, bone
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Multi-omics analysis reveals promiscuous O-glycosyltransferases involved in the diversity of flavonoid glycosides in Periploca forrestii (Apocynaceae) Comput. Struct. Biotechnol. J. (IF 6.0) Pub Date : 2024-03-02 Xiaotong Wang, Lan Wu, Wanran Zhang, Shi Qiu, Zhichao Xu, Huihua Wan, Jiang He, Wenting Wang, Mengyue Wang, Qinggang Yin, Yuhua Shi, Ranran Gao, Li Xiang, Weijun Yang
Flavonoid glycosides are widespread in plants, and are of great interest owing to their diverse biological activities and effectiveness in preventing chronic diseases. , a renowned medicinal plant of the Apocynaceae family, contains diverse flavonoid glycosides and is clinically used to treat rheumatoid arthritis and traumatic injuries. However, the mechanisms underlying the biosynthesis of these flavonoid
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Stabilization of the retromer complex: Analysis of novel binding sites of bis-1,3-phenyl guanylhydrazone 2a to the VPS29/VPS35 interface Comput. Struct. Biotechnol. J. (IF 6.0) Pub Date : 2024-03-02 Elisa Fagnani, Francesco Bonì, Pierfausto Seneci, Davide Gornati, Luca Muzio, Eloise Mastrangelo, Mario Milani
The stabilization of the retromer protein complex can be effective in the treatment of different neurological disorders. Following the identification of bis-1,3-phenyl guanylhydrazone as an effective new compound for the treatment of amyotrophic lateral sclerosis, in this work we analyze the possible binding sites of this molecule to the VPS35/VPS29 dimer of the retromer complex. Our results show that
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Phosphopeptide binding to the N-SH2 domain of tyrosine phosphatase SHP2 correlates with the unzipping of its central β-sheet Comput. Struct. Biotechnol. J. (IF 6.0) Pub Date : 2024-03-02 Michelangelo Marasco, John Kirkpatrick, Teresa Carlomagno, Jochen S. Hub, Massimiliano Anselmi
SHP2 is a tyrosine phosphatase that plays a regulatory role in multiple intracellular signaling cascades and is known to be oncogenic in certain contexts. In the absence of effectors, SHP2 adopts an autoinhibited conformation with its N-SH2 domain blocking the active site. Given the key role of N-SH2 in regulating SHP2, this domain has been extensively studied, often by X-ray crystallography. Using
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MiVitals– xed Reality Interface for Monitoring: A HoloLens based prototype for healthcare practices Comput. Struct. Biotechnol. J. (IF 6.0) Pub Date : 2024-03-01 Syed K Tanbeer, Edward Roderick Sykes
In this paper, we introduce —a Mixed Reality (MR) system designed for healthcare professionals to monitor patients in wards or clinics. We detail the design, development, and evaluation of , which integrates real-time vital signs from a biosensor-equipped wearable, . The system generates holographic visualizations, allowing healthcare professionals to interact with medical charts and information panels
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drexml: A command line tool and Python package for drug repurposing Comput. Struct. Biotechnol. J. (IF 6.0) Pub Date : 2024-03-01 Marina Esteban-Medina, Víctor Manuel de la Oliva Roque, Sara Herráiz-Gil, María Peña-Chilet, Joaquín Dopazo, Carlos Loucera
We introduce drexml, a command line tool and Python package for rational data-driven drug repurposing. The package employs machine learning and mechanistic signal transduction modeling to identify drug targets capable of regulating a particular disease. In addition, it employs explainability tools to contextualize potential drug targets within the functional landscape of the disease. The methodology
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Multidimensional morphological analysis of live sperm based on multiple-target tracking Comput. Struct. Biotechnol. J. (IF 6.0) Pub Date : 2024-03-01 Hao Yang, Mengmeng Ma, Xiangfeng Chen, Guowu Chen, Yi Shen, Lijun Zhao, Jianfeng Wang, Feifei Yan, Difeng Huang, Huijie Gao, Hao Jiang, Yuqian Zheng, Yu Wang, Qian Xiao, Ying Chen, Jian Zhou, Jie Shi, Yi Guo, Bo Liang, Xiaoming Teng
Manual semen evaluation methods are subjective and time-consuming. In this study, a deep learning algorithmic framework was designed to enable non-invasive multidimensional morphological analysis of live sperm in motion, improve current clinical sperm morphology testing methods, and significantly contribute to the advancement of assisted reproductive technologies. We improved the FairMOT tracking algorithm
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Robust multi-read reconstruction from noisy clusters using deep neural network for DNA storage Comput. Struct. Biotechnol. J. (IF 6.0) Pub Date : 2024-03-01 Yun Qin, Fei Zhu, Bo Xi, Lifu Song
DNA holds immense potential as an emerging data storage medium. However, the recovery of information in DNA storage systems faces challenges posed by various errors, including IDS errors, strand breaks, and rearrangements, inevitably introduced during synthesis, amplification, sequencing, and storage processes. Sequence reconstruction, crucial for decoding, involves inferring the DNA reference from
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Differential gene expression analysis pipelines and bioinformatic tools for the identification of specific biomarkers: A review Comput. Struct. Biotechnol. J. (IF 6.0) Pub Date : 2024-03-01 Diletta Rosati, Maria Palmieri, Giulia Brunelli, Andrea Morrione, Francesco Iannelli, Elisa Frullanti, Antonio Giordano
In recent years, the role of bioinformatics and computational biology together with omics techniques and transcriptomics has gained tremendous importance in biomedicine and healthcare, particularly for the identification of biomarkers for precision medicine and drug discovery. Differential gene expression (DGE) analysis is one of the most used techniques for RNA-sequencing (RNA-seq) data analysis.
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miR-21 Responsive Nanocarrier Targeting Ovarian Cancer Cells Comput. Struct. Biotechnol. J. (IF 6.0) Pub Date : 2024-03-01 Liting Han, Tao Song, Xinyu Wang, Yan Luo, Chuanqi Gu, Xin Li, Jinda Wen, Zhibin Wen, Xiaolong Shi
In recent years, DNA origami-based nanocarriers have been extensively utilized for efficient cancer therapy. However, developing a nanocarrier capable of effectively protecting cargos such as RNA remains a challenge. In this study, we designed a compact and controllable DNA tubular origami (DTO) measuring 120 nm in length and 18 nm in width. The DTO exhibited appropriate structural characteristics
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Uncovering the Potential of APOD as a Biomarker in Gastric Cancer: A Retrospective and Multi-center Study Comput. Struct. Biotechnol. J. (IF 6.0) Pub Date : 2024-02-24 Zisong Wang, Hongshan Chen, Le Sun, Xuanyu Wang, Yihang Xu, Sufang Tian, Xiaoping Liu
Gastric cancer (GC) poses a significant health challenge worldwide, necessitating the identification of predictive biomarkers to improve prognosis. Dysregulated lipid metabolism is a well-recognized hallmark of tumorigenesis, prompting investigation into apolipoproteins (APOs). In this study, we focused on apolipoprotein D (APOD) following comprehensive analyses of APOs in pan-cancer. Utilizing data
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Identification and Validation of Protein Biomarkers for Predicting Gastrointestinal Stromal Tumor Recurrence Comput. Struct. Biotechnol. J. (IF 6.0) Pub Date : 2024-02-22 Juan Sun, Jie Li, Yixuan He, Weiming Kang, Xin Ye
We conducted a proteomic analysis using mass spectrometry to identify and validate protein biomarkers for accurately predicting recurrence risk in gastrointestinal stromal tumors (GIST) patients, focusing on differentially expressed proteins in metastatic versus primary GIST tissues. We selected five biomarkers—GPX4, RBM4, TPM3, PFKFB2, and PGAM5—and validated their expressions in primary tumors of
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Design rules applied to silver nanoparticles synthesis: a practical example of machine learning application. Comput. Struct. Biotechnol. J. (IF 6.0) Pub Date : 2024-02-17 Irini Furxhi, Lara Faccani, Ilaria Zanoni, Andrea Brigliadori, Maurizio Vespignani, Anna Costa
The synthesis of silver nanoparticles with controlled physicochemical properties is essential for governing their intended functionalities and safety profiles. However, synthesis process involves multiple parameters that could influence the resulting properties. This challenge could be addressed with the development of predictive models that forecast endpoints based on key synthesis parameters. In
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Privacy-preserving federated machine learning on FAIR health data: A real-world application Comput. Struct. Biotechnol. J. (IF 6.0) Pub Date : 2024-02-17 A. Anil Sinaci, Mert Gencturk, Celia Alvarez-Romero, Gokce Banu Laleci Erturkmen, Alicia Martinez-Garcia, María José Escalona-Cuaresma, Carlos Luis Parra-Calderon
This paper introduces a privacy-preserving federated machine learning (ML) architecture built upon Findable, Accessible, Interoperable, and Reusable (FAIR) health data. It aims to devise an architecture for executing classification algorithms in a federated manner, enabling collaborative model-building among health data owners without sharing their datasets. Utilizing an agent-based architecture, a
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How do medical professionals make sense (or not) of AI? A social-media-based computational grounded theory study and an online survey Comput. Struct. Biotechnol. J. (IF 6.0) Pub Date : 2024-02-17 Sebastian Weber, Marc Wyszynski, Marie Godefroid, Ralf Plattfaut, Bjoern Niehaves
To investigate opinions and attitudes of medical professionals towards adopting AI-enabled healthcare technologies in their daily business, we used a mixed-methods approach. Study 1 employed a qualitative computational grounded theory approach analyzing 181 Reddit threads in the several subreddits of r/medicine. By utilizing an unsupervised machine learning clustering method, we identified three key
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Network-based analysis of heterogeneous patient-matched brain and extracranial melanoma metastasis pairs reveals three homogeneous subgroups Comput. Struct. Biotechnol. J. (IF 6.0) Pub Date : 2024-02-17 Konrad Grützmann, Theresa Kraft, Matthias Meinhardt, Friedegund Meier, Dana Westphal, Michael Seifert
Melanoma, the deadliest form of skin cancer, can metastasize to different organs. Molecular differences between brain and extracranial melanoma metastases are poorly understood. Here, promoter methylation and gene expression of 11 heterogeneous patient-matched pairs of brain and extracranial metastases were analyzed using melanoma-specific gene regulatory networks learned from public transcriptome
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Cyclodextrins: Establishing building blocks for AI-driven drug design by determining affinity constants in silico Comput. Struct. Biotechnol. J. (IF 6.0) Pub Date : 2024-02-16 Amelia Anderson, Ángel Piñeiro, Rebeca García-Fandiño, Matthew S. O’Connor
Cyclodextrins (CDs) are cyclic carbohydrate polymers that hold significant promise for drug delivery and industrial applications. Their effectiveness depends on their ability to encapsulate target molecules with strong affinity and specificity, but quantifying affinities in these systems accurately is challenging for a variety of reasons. Computational methods represent an exceptional complement to
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Optimisation of surfactin yield in Bacillus using data-efficient active learning and high-throughput mass spectrometry Comput. Struct. Biotechnol. J. (IF 6.0) Pub Date : 2024-02-15 Ricardo Valencia Albornoz, Diego Oyarzún, Karl Burgess
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Curvature-enhanced graph convolutional network for biomolecular interaction prediction Comput. Struct. Biotechnol. J. (IF 6.0) Pub Date : 2024-02-15 Cong Shen, Pingjian Ding, Junjie Wee, Jialin Bi, Jiawei Luo, Kelin Xia
Geometric deep learning has demonstrated a great potential in non-Euclidean data analysis. The incorporation of geometric insights into learning architecture is vital to its success. Here we propose a curvature-enhanced graph convolutional network (CGCN) for biomolecular interaction prediction. Our CGCN employs Ollivier-Ricci curvature (ORC) to characterize network local geometric properties and enhance
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Computational design of α-amylase from Bacillus licheniformis to increase its activity and stability at high temperatures Comput. Struct. Biotechnol. J. (IF 6.0) Pub Date : 2024-02-13 Shuai Fan, Xudong Lü, Xiyu Wei, Ruijie Lü, Cuiyue Feng, Yuanyuan Jin, Maocai Yan, Zhaoyong Yang
The thermostable α-amylase derived from (BLA) has multiple advantages, including enhancing the mass transfer rate and by reducing microbial contamination in starch hydrolysis. Nonetheless, the application of BLA is constrained by the accessibility and stability of enzymes capable of achieving high conversion rates at elevated temperatures. Moreover, the thermotolerance of BLA requires further enhancement
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The limits of prediction: Why intrinsically disordered regions challenge our understanding of antimicrobial peptides Comput. Struct. Biotechnol. J. (IF 6.0) Pub Date : 2024-02-12 Roberto Bello-Madruga, Marc Torrent Burgas
Antimicrobial peptides (AMPs) are molecules found in most organisms, playing a vital role in innate immune defense against pathogens. Their mechanism of action involves the disruption of bacterial cell membranes, causing leakage of cellular contents and ultimately leading to cell death. While AMPs typically lack a defined structure in solution, they often assume a defined conformation when interacting
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Introducing a New Article Type “Innovation Reports”: Venue for Communication & Dissemination of Collaborative Projects Comput. Struct. Biotechnol. J. (IF 6.0) Pub Date : 2024-02-10 D.B.R.K. Gupta Udatha
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Automated machine learning in nanotoxicity assessment: A comparative study of predictive model performance Comput. Struct. Biotechnol. J. (IF 6.0) Pub Date : 2024-02-09 Xiao Xiao, Tung X. Trinh, Zayakhuu Gerelkhuu, Eunyong Ha, Tae Hyun Yoon
Computational modeling has earned significant interest as an alternative to animal testing of toxicity assessment. However, the process of selecting an appropriate algorithm and fine-tuning hyperparameters for the developing of optimized models takes considerable time, expertise, and an intensive search. The recent emergence of automated machine learning (autoML) approaches, available as user-friendly
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FluoroTensor: Identification and tracking of colocalised molecules and their stoichiometries in multi-colour single molecule imaging via deep learning Comput. Struct. Biotechnol. J. (IF 6.0) Pub Date : 2024-02-08 Max F.K. Wills, Carlos Bueno Alejo, Nikolas Hundt, Marina Santana-Vega, Andrea Taladriz-Sender, Alasdair W. Clark, Andrew J. Hudson, Ian C. Eperon
The identification of photobleaching steps in single molecule fluorescence imaging is a well-established procedure for analysing the stoichiometries of molecular complexes. Nonetheless, the method is challenging with protein fluorophores because of the high levels of noise, rapid bleaching and highly variable signal intensities, all of which complicate methods based on statistical analyses of intensities
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Computer-aided engineering of stabilized fibroblast growth factor 21 Comput. Struct. Biotechnol. J. (IF 6.0) Pub Date : 2024-02-07 Gabin de La Bourdonnaye, Tereza Ghazalova, Petr Fojtik, Katerina Kutalkova, David Bednar, Jiri Damborsky, Vladimir Rotrekl, Veronika Stepankova, Radka Chaloupkova
FGF21 is an endocrine signaling protein belonging to the family of fibroblast growth factors (FGFs). It has emerged as a molecule of interest for treating various metabolic diseases due to its role in regulating glucogenesis and ketogenesis in the liver. However, FGF21 is prone to heat, proteolytic, and acid-mediated degradation, and its low molecular weight makes it susceptible to kidney clearance
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Comprehensive integration of single-cell RNA and transcriptome RNA sequencing to establish a pyroptosis-related signature for improving prognostic prediction of gastric cancer Comput. Struct. Biotechnol. J. (IF 6.0) Pub Date : 2024-02-07 Jie Li, Tian Yu, Juan Sun, Mingwei Ma, Zicheng Zheng, Weiming Kang, Xin Ye
Cell pyroptosis, a Gasdermin-dependent programmed cell death characterized by inflammasome, plays a complex and dynamic role in Gastric cancer (GC), a serious threat to human health. Therefore, the value of pyroptosis-related genes (PRGs) as prognostic biomarkers and therapeutic indicators for patients needs to be exploited in GC. This study integrates single-cell RNA sequencing (scRNA-seq) dataset
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Enhancing Mass spectrometry-based tumor immunopeptide identification: machine learning filter leveraging HLA binding affinity, aliphatic index and retention time deviation Comput. Struct. Biotechnol. J. (IF 6.0) Pub Date : 2024-02-03 Feifei Wei, Taku Kouro, Yuko Nakamura, Hiroki Ueda, Susumu Iiizumi, Kyoko Hasegawa, Yuki Asahina, Takeshi Kishida, Soichiro Morinaga, Hidetomo Himuro, Shun Horaguchi, Kayoko Tsuji, Yasunobu Mano, Norihiro Nakamura, Takeshi Kawamura, Tetsuro Sasada
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A review on trends in development and translation of omics signatures in cancer Comput. Struct. Biotechnol. J. (IF 6.0) Pub Date : 2024-02-03 Wei Ma, Wenshu Tang, Jamie S.L. Kwok, Amy H.Y. Tong, Cario W.S. Lo, Annie T.W. Chu, Brian H.Y. Chung
The field of cancer genomics and transcriptomics has evolved from targeted profiling to swift sequencing of individual tumor genome and transcriptome. The steady growth in genome, epigenome, and transcriptome datasets on a genome-wide scale has significantly increased our capability in capturing signatures that represent both the intrinsic and extrinsic biological features of tumors. These biological
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Genetic variant classification by predicted protein structure: A case study on IRF6 Comput. Struct. Biotechnol. J. (IF 6.0) Pub Date : 2024-02-03 Hemma Murali, Peng Wang, Eric C. Liao, Kai Wang
Next-generation genome sequencing has revolutionized genetic testing, identifying numerous rare disease-associated gene variants. However, to impute pathogenicity, computational approaches remain inadequate and functional testing of gene variant is required to provide the highest level of evidence. The emergence of AlphaFold2 has transformed the field of protein structure determination, and here we
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Recent advances in spatially variable gene detection in spatial transcriptomics Comput. Struct. Biotechnol. J. (IF 6.0) Pub Date : 2024-02-02 Sikta Das Adhikari, Jiaxin Yang, Jianrong Wang, Yuehua Cui
With the emergence of advanced spatial transcriptomic technologies, there has been a surge in research papers dedicated to analyzing spatial transcriptomics data, resulting in significant contributions to our understanding of biology. The initial stage of downstream analysis of spatial transcriptomic data has centered on identifying spatially variable genes (SVGs) or genes expressed with specific spatial
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Genome-wide analysis for root and leaf architecture traits associated with drought tolerance at the seedling stage in a highly ecologically diverse wheat population Comput. Struct. Biotechnol. J. (IF 6.0) Pub Date : 2024-02-02 Ahmed Sallam, Rawan A. Awadalla, Maha M. Elshamy, Andreas Börner, Yasmin M. Heikal
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Comparative analyses of Pleurotus pulmonarius mitochondrial genomes reveal two major lineages of mini oyster mushroom cultivars Comput. Struct. Biotechnol. J. (IF 6.0) Pub Date : 2024-02-02 Yang Yu, Tianhai Liu, Yong Wang, Lixu Liu, Xiaolan He, Jianwei Li, Francis M. Martin, Weihong Peng, Hao Tan
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Large language models assisted multi-effect variants mining on cerebral cavernous malformation familial whole genome sequencing Comput. Struct. Biotechnol. J. (IF 6.0) Pub Date : 2024-02-01 Yiqi Wang, Jinmei Zuo, Chao Duan, Hao Peng, Jia Huang, Liang Zhao, Li Zhang, Zhiqiang Dong
Cerebral cavernous malformation (CCM) is a polygenic disease with intricate genetic interactions contributing to quantitative pathogenesis across multiple factors. The principal pathogenic genes of CCM, specifically KRIT1, CCM2, and PDCD10, have been reported, accompanied by a growing wealth of genetic data related to mutations. Furthermore, numerous other molecules associated with CCM have been unearthed
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Prognostic iron-metabolism signature robustly stratifies single-cell characteristics of hepatocellular carcinoma Comput. Struct. Biotechnol. J. (IF 6.0) Pub Date : 2024-02-01 Zhipeng Zhu, Huang Cao, Hongyu Yan, Hanzhi Liu, Zaifa Hong, Anran Sun, Tong Liu, Fengbiao Mao
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A detailed sensitivity analysis identifies the key factors influencing the enzymatic saccharification of lignocellulosic biomass Comput. Struct. Biotechnol. J. (IF 6.0) Pub Date : 2024-01-26 Partho Sakha De, Jasmin Theilmann, Adélaïde Raguin
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1Microbial functional pathways based on metatranscriptomic profiling enable effective saliva-based health assessments for precision wellness Comput. Struct. Biotechnol. J. (IF 6.0) Pub Date : 2024-01-29 Eric Patridge, Anmol Gorakshakar, Matthew M. Molusky, Oyetunji Ogundijo, Angel Janevski, Cristina Julian, Lan Hu, Momchilo Vuyisich, Guruduth Banavar
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Robotic Mirror Therapy for Stroke Rehabilitation through Virtual Activities of Daily Living Comput. Struct. Biotechnol. J. (IF 6.0) Pub Date : 2024-01-26 Harris Nisar, Srikar Annamraju, Shankar A. Deka, Anne Horowitz, Dušan M. Stipanović
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Predicting Zeta Potential of Liposomes from Their Structure: A Nano-QSPR Model for DOPE, DC-Chol, DOTAP, and EPC Formulations Comput. Struct. Biotechnol. J. (IF 6.0) Pub Date : 2024-01-24 Tomasz Puzyn, Krzesimir Ciura, Kamila Jarzynska, Agnieszka Gajewicz-Skretna
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T4SEpp: a pipeline integrating protein language models to predict bacterial type IV secreted effectors Comput. Struct. Biotechnol. J. (IF 6.0) Pub Date : 2024-01-23 Yueming Hu, Yejun Wang, Xiaotian Hu, Haoyu Chao, Sida Li, Qinyang Ni, Yanyan Zhu, Yixue Hu, Ziyi Zhao, Ming Chen
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Databases and Computational Methods for the Identification of piRNA-related Molecules: A Survey Comput. Struct. Biotechnol. J. (IF 6.0) Pub Date : 2024-01-22 Chang Guo, Xiaoli Wang, Han Ren
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