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Language model based on deep learning network for biomedical named entity recognition Methods (IF 4.8) Pub Date : 2024-04-17 Guan Hou, Yuhao Jian, Qingqing Zhao, Xiongwen Quan, Han Zhang
Biomedical Named Entity Recognition (BioNER) is one of the most basic tasks in biomedical text mining, which aims to automatically identify and classify biomedical entities in text. Recently, deep learning-based methods have been applied to Biomedical Named Entity Recognition and have shown encouraging results. However, many biological entities are polysemous and ambiguous, which is one of the main
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m6Aexpress-enet: Predicting the regulatory expression m6A sites by an enet-regularization negative binomial regression model Methods (IF 4.8) Pub Date : 2024-04-16 Teng Zhang, Shang Gao, Shao-wu Zhang, Xiao-dong Cui
As the most abundant mRNA modification, mA controls and influences many aspects of mRNA metabolism including the mRNA stability and degradation. However, the role of specific mA sites in regulating gene expression still remains unclear. In additional, the multicollinearity problem caused by the correlation of methylation level of multiple mA sites in each gene could influence the prediction performance
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Chromatin image-driven modelling Methods (IF 4.8) Pub Date : 2024-04-16 Michał Kadlof, Krzysztof Banecki, Mateusz Chiliński, Dariusz Plewczynski
The challenge of modelling the spatial conformation of chromatin remains an open problem. While multiple data-driven approaches have been proposed, each has limitations. This work introduces two image-driven modelling methods based on the Molecular Dynamics Flexible Fitting (MDFF) approach: the force method and the correlational method. Both methods have already been used successfully in protein modelling
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DEEP-EP: Identification of epigenetic protein by ensemble residual convolutional neural network for drug discovery Methods (IF 4.8) Pub Date : 2024-04-14 Farman Ali, Abdullah Almuhaimeed, Majdi Khalid, Hanan Alshanbari, Atef Masmoudi, Raed Alsini
Epigenetic proteins (EP) play a role in the progression of a wide range of diseases, including autoimmune disorders, neurological disorders, and cancer. Recognizing their different functions has prompted researchers to investigate them as potential therapeutic targets and pharmacological targets. This paper proposes a novel deep learning-based model that accurately predicts EP. This study introduces
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DTKGIN: Predicting drug-target interactions based on knowledge graph and intent graph Methods (IF 4.8) Pub Date : 2024-04-10 Yi Luo, Guihua Duan, Qichang Zhao, Xuehua Bi, Jianxin Wang
Knowledge graph intent graph attention mechanism Predicting drug-target interactions (DTIs) plays a crucial role in drug discovery and drug development. Considering the high cost and risk of biological experiments, developing computational approaches to explore the interactions between drugs and targets can effectively reduce the time and cost of drug development. Recently, many methods have made significant
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The bioaccessibility and tolerability of marine-derived sources of magnesium and calcium Methods (IF 4.8) Pub Date : 2024-04-10 Alison Dowley, Caitriona M. Long-Smith, Olusoji Demehin, Yvonne Nolan, Shane O'Connell, Denise M. O'Gorman
It is generally accepted that mineral deficiencies, including magnesium and calcium, are widespread globally. Dietary supplementation may be an effective approach to combat such deficiencies. However, challenges associated with limited mineral solubility in the digestive system can impede effective dissolution and hinder absorption, leading to deficiency, and undesirable gastrointestinal disturbances
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Exploring GPCR conformational dynamics using single-molecule fluorescence Methods (IF 4.8) Pub Date : 2024-04-10 Eugene Agyemang, Alyssa N. Gonneville, Sriram Tiruvadi-Krishnan, Rajan Lamichhane
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Pipelined biomedical event extraction rivaling joint learning Methods (IF 4.8) Pub Date : 2024-04-09 Pengchao Wu, Xuefeng Li, Jinghang Gu, Longhua Qian, Guodong Zhou
Biomedical event extraction is an information extraction task to obtain events from biomedical text, whose targets include the type, the trigger, and the respective arguments involved in an event. Traditional biomedical event extraction usually adopts a pipelined approach, which contains trigger identification, argument role recognition, and finally event construction either using specific rules or
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A ratiometric fluorescence probe for bisulfite detection in live cells and meat samples Methods (IF 4.8) Pub Date : 2024-03-31 Dihua Tian, Xin Qi, Maral Seididamyeh, Huayue Zhang, Anh Phan, Zexi Zhang, Xuhui Geng, Yasmina Sultanbawa, Run Zhang
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Current methodologies in studying chromatin and gene expression Methods (IF 4.8) Pub Date : 2024-03-30 Sukesh R. Bhaumik
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Deep Learning-Based construction of a Drug-Like compound database and its application in virtual screening of HsDHODH inhibitors Methods (IF 4.8) Pub Date : 2024-03-20 Wei Xia, Jin Xiao, Hengwei Bian, Jiajun Zhang, John Z.H. Zhang, Haiping Zhang
The process of virtual screening relies heavily on the databases, but it is disadvantageous to conduct virtual screening based on commercial databases with patent-protected compounds, high compound toxicity and side effects. Therefore, this paper utilizes generative recurrent neural networks (RNN) containing long short-term memory (LSTM) cells to learn the properties of drug compounds in the DrugBank
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Steady-state and time-resolved fluorescent methodologies to characterize the conformational landscape of the selectivity filter of K+ channels Methods (IF 4.8) Pub Date : 2024-03-19 María Lourdes Renart, Ana Marcela Giudici, José M. González-Ros, José A. Poveda
A variety of equilibrium and non-equilibrium methods have been used in a multidisciplinary approach to study the conformational landscape associated with the binding of different cations to the pore of potassium channels. These binding processes, and the conformational changes resulting therefrom, modulate the functional properties of such integral membrane properties, revealing these permeant and
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A methodological framework for rigorous systematic reviews: Tailoring comprehensive analyses to clinicians and healthcare professionals Methods (IF 4.8) Pub Date : 2024-03-17 Stefano Mancin, Marco Sguanci, Giuliano Anastasi, Lea Godino, Alessio Lo Cascio, Emanuela Morenghi, Michela Piredda, Maria Grazia De Marinis
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Establishing finite element model credibility of a pedicle screw system under compression-bending: An end-to-end example of the ASME V&V 40 standard Methods (IF 4.8) Pub Date : 2024-03-16 Srinidhi Nagaraja, Galyna Loughran, Andrew P. Baumann, Kumar Kartikeya, Marc Horner
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Isothermal titration calorimetry and surface plasmon resonance methods to probe protein–protein interactions Methods (IF 4.8) Pub Date : 2024-03-15 Vaibhav Upadhyay, Alexandra Lucas, Casey Patrick, Krishna M.G. Mallela
Isothermal titration calorimetry (ITC) and surface plasmon resonance (SPR) are two commonly used methods to probe biomolecular interactions. ITC can provide information about the binding affinity, stoichiometry, changes in Gibbs free energy, enthalpy, entropy, and heat capacity upon binding. SPR can provide information about the association and dissociation kinetics, binding affinity, and stoichiometry
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Machine learning aided single cell image analysis improves understanding of morphometric heterogeneity of human mesenchymal stem cells Methods (IF 4.8) Pub Date : 2024-03-13 Risani Mukhopadhyay, Pulkit Chandel, Keerthana Prasad, Uttara Chakraborty
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Deepm6A-MT: A deep learning-based method for identifying RNA N6-methyladenosine sites in multiple tissues Methods (IF 4.8) Pub Date : 2024-03-12 Guohua Huang, Xiaohong Huang, Jinyun Jiang
N6-methyladenosine (m6A) is the most prevalent, abundant, and conserved internal modification in the eukaryotic messenger RNA (mRNAs) and plays a crucial role in the cellular process. Although more than ten methods were developed for m6A detection over the past decades, there were rooms left to improve the predictive accuracy and the efficiency. In this paper, we proposed an improved method for predicting
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Chromene-chromene Schiff base as a fluorescent chemosensor for Th4+ and its application in bioimaging of Caenorhabditis elegans Methods (IF 4.8) Pub Date : 2024-03-12 Aastha Dua, Pratiksha Saini, Shiwani Goyal, Pravinkumar Selvam, S.K. Ashok Kumar, Govindhan Thiruppathi, Palanisamy Sundararaj, Harish K. Sharma, Selva Kumar Ramasamy
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Using synthetic genome readers/regulators to interrogate chromatin processes: A brief review Methods (IF 4.8) Pub Date : 2024-03-11 Steven J. Philips, Adithi Danda, Aseem Z. Ansari
Aberrant gene expression underlies numerous human ailments. Hence, developing small molecules to target and remedy dysfunctional gene regulation has been a long-standing goal at the interface of chemistry and medicine. A major challenge for designing small molecule therapeutics aimed at targeting desired genomic loci is the minimization of widescale disruption of genomic functions. To address this
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Naphthalimide-based new architecture for fluorescence turn-on sensing of Cu2+ and colorimetric detection of F-/CN– Methods (IF 4.8) Pub Date : 2024-03-02 Sumit Ghosh, Subhasis Ghosh, Subrata Ranjan Dhara, Nabajyoti Baildya, Kumaresh Ghosh
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Steric trapping strategy for studying the folding of helical membrane proteins Methods (IF 4.8) Pub Date : 2024-02-29 Jiaqi Yao, Heedeok Hong
Elucidating the folding energy landscape of membrane proteins is essential to the understanding of the proteins’ stabilizing forces, folding mechanisms, biogenesis, and quality control. This is not a trivial task because the reversible control of folding is inherently difficult in a lipid bilayer environment. Recently, novel methods have been developed, each of which has a unique strength in investigating
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MFA-DTI: Drug-target interaction prediction based on multi-feature fusion adopted framework Methods (IF 4.8) Pub Date : 2024-02-29 Siqi Chen, Minghui Li, Ivan Semenov
The identification of drug-target interactions (DTI) is a valuable step in the drug discovery and repositioning process. However, traditional laboratory experiments are time-consuming and expensive. Computational methods have streamlined research to determine DTIs. The application of deep learning methods has significantly improved the prediction performance for DTIs. Modern deep learning methods can
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A domain-label-guided translation model for molecular optimization Methods (IF 4.8) Pub Date : 2024-02-21 Yajie Zhang, Yongqi Tong, Xin Xia, Qingwen Wu, Yansen Su
Molecular optimization, which aims to improve molecular properties by modifying complex molecular structures, is a crucial and challenging task in drug discovery. In recent years, translation models provide a promising way to transform low-property molecules to high-property molecules, which enables molecular optimization to achieve remarkable progress. However, most existing models require matched
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Generation of site-specifically labelled fluorescent human XPA to investigate DNA binding dynamics during nucleotide excision repair Methods (IF 4.8) Pub Date : 2024-02-20 Sahiti Kuppa, Elliot Corless, Colleen C. Caldwell, Maria Spies, Edwin Antony
Nucleotide excision repair (NER) promotes genomic integrity by removing bulky DNA adducts introduced by external factors such as ultraviolet light. Defects in NER enzymes are associated with pathological conditions such as Xeroderma Pigmentosum, trichothiodystrophy, and Cockayne syndrome. A critical step in NER is the binding of the Xeroderma Pigmentosum group A protein (XPA) to the ss/ds DNA junction
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Design, in silico evaluation, and in vitro verification of new bivalent Smac mimetics with pro-apoptotic activity Methods (IF 4.8) Pub Date : 2024-02-17 Qingsheng Huang, Yin Peng, Yuefeng Peng, Huijuan Lin, Shiqi Deng, Shengzhong Feng, Yanjie Wei
Bivalent Smac mimetics have been shown to possess binding affinity and pro-apoptotic activity similar to or more potent than that of native Smac, a protein dimer able to neutralize the anti-apoptotic activity of an inhibitor of caspase enzymes, XIAP, which endows cancer cells with resistance to anticancer drugs. We design five new bivalent Smac mimetics, which are formed by various linkers tethering
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CT radiomics analysis discriminates pulmonary lesions in patients with pulmonary MALT lymphoma and non-pulmonary MALT lymphoma Methods (IF 4.8) Pub Date : 2024-02-17 Yuyin Le, Haojie Zhu, Chenjing Ye, Jiexiang Lin, Nila Wang, Ting Yang
The aim of this study is to create and validate a radiomics model based on CT scans, enabling the distinction between pulmonary mucosa-associated lymphoid tissue (MALT) lymphoma and other pulmonary lesion causes. Patients diagnosed with primary pulmonary MALT lymphoma and lung infections at Fuzhou Pulmonary Hospital were randomly assigned to either a training group or a validation group. Meanwhile
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Point-of-care image-based quantitative urinalysis with commercial reagent strips: Design and clinical evaluation Methods (IF 4.8) Pub Date : 2024-02-15 Damian Tohl, Anh Tran Tam Pham, Jordan Li, Youhong Tang
Urinalysis is a useful test as an indicator of health or disease and as such, is a part of routine health screening. Urinalysis can be undertaken in many ways, one of which is reagent strips used in the general evaluation of health and to aid in the diagnosis and monitoring of kidney disease. To be effective, the test must be performed properly, and the results interpreted correctly. However, different
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GSL-DTI: Graph structure learning network for Drug-Target interaction prediction Methods (IF 4.8) Pub Date : 2024-02-14 Zixuan E, Guanyu Qiao, Guohua Wang, Yang Li
Drug-target interaction prediction is an important area of research to predict whether there is an interaction between a drug molecule and its target protein. It plays a critical role in drug discovery and development by facilitating the identification of potential drug candidates and expediting the overall process. Given the time-consuming, expensive, and high-risk nature of traditional drug discovery
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Special issue Methods: Nano-/bio-interface and biomedical applications Methods (IF 4.8) Pub Date : 2024-02-12 Li Li, Alain Wuethrich
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Nanoscopic lipid domains determined by microscopy and neutron scattering Methods (IF 4.8) Pub Date : 2024-02-06 Charles P. Collier, Dima Bolmatov, James G. Elkins, John Katsaras
Biological membranes are highly complex supramolecular assemblies, which play central roles in biology. However, their complexity makes them challenging to study their nanoscale structures. To overcome this challenge, model membranes assembled using reduced sets of membrane-associated biomolecules have been found to be both excellent and tractable proxies for biological membranes. Due to their relative
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Fluorescent human RPA to track assembly dynamics on DNA Methods (IF 4.8) Pub Date : 2024-01-30 Vikas Kaushik, Rahul Chadda, Sahiti Kuppa, Nilisha Pokhrel, Abhinav Vayyeti, Scott Grady, Chris Arnatt, Edwin Antony
DNA metabolic processes including replication, repair, recombination, and telomere maintenance occur on single-stranded DNA (ssDNA). In each of these complex processes, dozens of proteins function together on the ssDNA template. However, when double-stranded DNA is unwound, the transiently open ssDNA is protected and coated by the high affinity heterotrimeric ssDNA binding Replication Protein A (RPA)
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Metabolic insights from mass spectrometry imaging of biofilms: A perspective from model microorganisms Methods (IF 4.8) Pub Date : 2024-01-29 Dharmeshkumar Parmar, Joenisse M. Rosado-Rosa, Joshua D. Shrout, Jonathan V. Sweedler
Biofilms are dense aggregates of bacterial colonies embedded inside a self-produced polymeric matrix. Biofilms have received increasing attention in medical, industrial, and environmental settings due to their enhanced survival. Their characterization using microscopy techniques has revealed the presence of structural and cellular heterogeneity in many bacterial systems. However, these techniques provide
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Enhancing antigenic peptide discovery: Improved MHC-I binding prediction and methodology Methods (IF 4.8) Pub Date : 2024-01-29 Stanisław Giziński, Grzegorz Preibisch, Piotr Kucharski, Michał Tyrolski, Michał Rembalski, Piotr Grzegorczyk, Anna Gambin
The Major Histocompatibility Complex (MHC) is a critical element of the vertebrate cellular immune system, responsible for presenting peptides derived from intracellular proteins. MHC-I presentation is pivotal in the immune response and holds considerable potential in the realms of vaccine development and cancer immunotherapy. This study delves into the limitations of current methods and benchmarks
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NMR techniques for investigating antimicrobial peptides in model membranes and bacterial cells Methods (IF 4.8) Pub Date : 2024-01-29 Marc-Antoine Sani, Sunnia Rajput, David W. Keizer, Frances Separovic
AMPs are short, mainly cationic membrane-active peptides found in all living organism. They perform diverse roles including signaling and acting as a line of defense against bacterial infections. AMPs have been extensively investigated as templates to facilitate the development of novel antimicrobial therapeutics. Understanding the interplay between these membrane-active peptides and the lipid membranes
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LoopSage: An energy-based Monte Carlo approach for the loop extrusion modeling of chromatin Methods (IF 4.8) Pub Date : 2024-01-29 Sevastianos Korsak, Dariusz Plewczynski
The connection between the patterns observed in 3C-type experiments and the modeling of polymers remains unresolved. This paper presents a simulation pipeline that generates thermodynamic ensembles of 3D structures for topologically associated domain (TAD) regions by loop extrusion model (LEM). The simulations consist of two main components: a stochastic simulation phase, employing a Monte Carlo approach
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MFD–GDrug: multimodal feature fusion-based deep learning for GPCR–drug interaction prediction Methods (IF 4.8) Pub Date : 2024-01-28 Xingyue Gu, Junkai Liu, Yue Yu, Pengfeng Xiao, Yijie Ding
The accurate identification of drug–protein interactions (DPIs) is crucial in drug development, especially concerning G protein-coupled receptors (GPCRs), which are vital targets in drug discovery. However, experimental validation of GPCR–drug pairings is costly, prompting the need for accurate predictive methods. To address this, we propose MFD–GDrug, a multimodal deep learning model. Leveraging the
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Atomic force microscope kymograph analysis: A case study of two membrane proteins Methods (IF 4.8) Pub Date : 2024-01-28 Dylan R. Weaver, Katherine G. Schaefer, Gavin M. King
Kymograph analysis is employed across the biological atomic force microscopy (AFM) community to boost temporal resolution. The method is well suited for revealing protein dynamics at the single molecule level in near-native conditions. Yet, kymograph analysis comes with limitations that depend on several factors including protein geometry and instrumental drift. This work focuses on conformational
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miTDS: Uncovering miRNA-mRNA interactions with deep learning for functional target prediction Methods (IF 4.8) Pub Date : 2024-01-26 Jialin Zhang, Haoran Zhu, Yin Liu, Xiangtao Li
MicroRNAs (miRNAs) are vital in regulating gene expression through binding to specific target sites on messenger RNAs (mRNAs), a process closely tied to cancer pathogenesis. Identifying miRNA functional targets is essential but challenging, due to incomplete genome annotation and an emphasis on known miRNA-mRNA interactions, restricting predictions of unknown ones. To address those challenges, we have
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Synthesis of functionalized mesoporous silica nanoparticles for colorimetric and fluorescence sensing of selective metal (Fe3+) ions in aqueous solution Methods (IF 4.8) Pub Date : 2024-01-23 Madhappan Santhamoorthy, Anandhu Mohan, Kailasam Saravana Mani, Tamiloli Devendhiran, Govindasami Periyasami, Seong-Cheol Kim, Mei-Ching Lin, Keerthika Kumarasamy, Po-Jui Huang, Asif Ali
The fabrication of red fluorescent hybrid mesoporous silica-based nanosensor materials has promised the bioimaging and selective detection of toxic pollutants in aqueous solutions. In this study, we present a hybrid mesoporous silica nanosensor in which the propidium iodide (PI) was used to conveniently integrate into the mesopore walls using bis(trimethoxysilylpropyl silane) precursors. Various characterization
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An amphiphilic dansyl based multianalyte sensor for the detection of Hg2+, PPi, and TNP: A three-in-one chemical sensor Methods (IF 4.8) Pub Date : 2024-01-23 Srushti Gadiyaram, M. Aakshika Sree, Nancy Sharma, D. Amilan Jose
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Predicting Drug-drug Interaction with Graph Mutual Interaction Attention Mechanism Methods (IF 4.8) Pub Date : 2024-01-21 Xiaoying Yan, Chi Gu, Yuehua Feng, Jiaxin Han
Effective representation of molecules is a crucial step in AI-driven drug design and drug discovery, especially for drug-drug interaction (DDIs) prediction. Previous work usually models the drug information from the drug-related knowledge graph or the single drug molecules, but the interaction information between molecular substructures of drug pair is seldom considered, thus often ignoring the influence
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Emulation of Quantitative Systems Pharmacology models to accelerate virtual population inference in immuno-oncology Methods (IF 4.8) Pub Date : 2024-01-19 Tomasz Pawłowski, Grzegorz Bokota, Georgia Lazarou, Andrzej M. Kierzek, Jacek Sroka
Quantitative Systems Pharmacology (QSP) models are increasingly being applied for target discovery and dose selection in immuno-oncology (IO). Typical application involves virtual trial, a simulation of a virtual population of hundreds of model instances with model inputs reflecting individual variability. While the structure of the model and initial parameterisation are based on literature describing
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AGF-PPIS: A protein–protein interaction site predictor based on an attention mechanism and graph convolutional networks Methods (IF 4.8) Pub Date : 2024-01-17 Xiuhao Fu, Ye Yuan, Haoye Qiu, Haodong Suo, Yingying Song, Anqi Li, Yupeng Zhang, Cuilin Xiao, Yazi Li, Lijun Dou, Zilong Zhang, Feifei Cui
Protein–protein interactions play an important role in various biological processes. Interaction among proteins has a wide range of applications. Therefore, the correct identification of protein–protein interactions sites is crucial. In this paper, we propose a novel predictor for protein–protein interactions sites, AGF-PPIS, where we utilize a multi-head self-attention mechanism (introducing a graph
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Comprehensive evaluation of molecule property prediction with ChatGPT Methods (IF 4.8) Pub Date : 2024-01-17 Xibao Cai, Houtim Lai, Xing Wang, Longyue Wang, Wei Liu, Yijun Wang, Zixu Wang, Dongsheng Cao, Xiangxiang Zeng
The versatility of ChatGPT in performing a diverse range of tasks has elicited considerable interest on its potential applications within professional fields. Taking drug discovery as a testbed, this paper provides a comprehensive evaluation of ChatGPT's ability on molecule property prediction. The study focuses on three aspects: 1) Effects of different prompt settings, where we investigate the impact
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DBPboost:A method of classification of DNA-binding proteins based on improved differential evolution algorithm and feature extraction Methods (IF 4.8) Pub Date : 2024-01-17 Ailun Sun, Hongfei Li, Guanghui Dong, Yuming Zhao, Dandan Zhang
DNA-binding proteins are a class of proteins that can interact with DNA molecules through physical and chemical interactions. Their main functions include regulating gene expression, maintaining chromosome structure and stability, and more. DNA-binding proteins play a crucial role in cellular and molecular biology, as they are essential for maintaining normal cellular physiological functions and adapting
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Sensitivity-enhanced NMR 15N R1 and R1ρ relaxation experiments for the investigation of intrinsically disordered proteins at high magnetic fields Methods (IF 4.8) Pub Date : 2024-01-17 Tobias Stief, Katharina Vormann, Nils-Alexander Lakomek
NMR relaxation experiments provide residue-specific insights into the structural dynamics of proteins. Here, we present an optimized set of sensitivity-enhanced N R and R relaxation experiments applicable to fully protonated proteins. The NMR pulse sequences are conceptually similar to the set of TROSY-based sequences and their HSQC counterpart (Lakomek et al., J. Biomol. NMR 2012). Instead of the
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LC-MS/MS platform-based serum untargeted screening reveals the diagnostic biomarker panel and molecular mechanism of breast cancer Methods (IF 4.8) Pub Date : 2024-01-14 Sisi Gong, Qingshui Wang, Jiewei Huang, Rongfu Huang, Shanshan Chen, Xiaojuan Cheng, Lei Liu, Xiaofang Dai, Yameng Zhong, Chunmei Fan, Zhijun Liao
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Bodipy-based quinoline derivative as a highly Hg2+-selective fluorescent chemosensor and its potential applications Methods (IF 4.8) Pub Date : 2024-01-14 Keerthika Kumarasamy, Tamiloli Devendhiran, Wei-Jyun Chien, Mei-Ching Lin, Selva Kumar Ramasamy, Ji-Jhang Yang
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Application scenario-oriented molecule generation platform developed for drug discovery Methods (IF 4.8) Pub Date : 2024-01-11 Lianjun Zheng, Fangjun Shi, Chunwang Peng, Min Xu, Fangda Fan, Yuanpeng Li, Lin Zhang, Jiewen Du, Zonghu Wang, Zhixiong Lin, Yina Sun, Chenglong Deng, Xinli Duan, Lin Wei, Chuanfang Zhao, Lei Fang, Peiyu Zhang, Songling Ma, Lipeng Lai, Mingjun Yang
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Fluorescence-based ratiometric sensors as emerging tools for CN− detection: Chemical structures, sensing mechanisms and applications Methods (IF 4.8) Pub Date : 2024-01-06 Ashwani Kumar, Eunhye Jeong, Youngwoo Noh, Pil Seok Chae
Hazardous cyanide anions (CN) are increasingly threatening the environment and human health due to their widespread use in industry and many other fields. Over the past three decades, a large number of probes have been reported to sensitively and selectively detect this toxic anion, while a rather limited number of ratiometric fluorescent probes have been developed. The ratiometric probes have significant
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Interpreting the effect of mutations to protein binding sites from large-scale genomic screens Methods (IF 4.8) Pub Date : 2024-01-05 Sara Jamshidi Parvar, Benjamin A Hall, David Shorthouse
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Microbiome metabolite quantification methods enabling insights into human health and disease Methods (IF 4.8) Pub Date : 2024-01-05 Jarrod Roach, Rohit Mital, Jacob J. Haffner, Nathan Colwell, Randy Coats, Horvey M. Palacios, Zongyuan Liu, Joseane L.P. Godinho, Monica Ness, Thilini Peramuna, Laura-Isobel McCall
Many of the health-associated impacts of the microbiome are mediated by its chemical activity, producing and modifying small molecules (metabolites). Thus, microbiome metabolite quantification has a central role in efforts to elucidate and measure microbiome function. In this review, we cover general considerations when designing experiments to quantify microbiome metabolites, including sample preparation
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DMGL-MDA: A dual-modal graph learning method for microbe-drug association prediction Methods (IF 4.8) Pub Date : 2024-01-04 Bei Zhu, Hao-Yang Yu, Bing-Xue Du, Jian-Yu Shi
The interaction between human microbes and drugs can significantly impact human physiological functions. It is crucial to identify potential microbe-drug associations (MDAs) before drug administration. However, conventional biological experiments to predict MDAs are plagued by drawbacks such as time-consuming, high costs, and potential risks. On the contrary, computational approaches can speed up the
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CRCS: An automatic image processing pipeline for hormone level analysis of Cushing’s disease Methods (IF 4.8) Pub Date : 2023-12-29 Haiyue Li, Jing Xie, Jialin Song, Cheng Jin, Hongyi Xin, Xiaoyong Pan, Jing Ke, Ye Yuan, Hongbin Shen, Guang Ning
Due to the abnormal secretion of adreno-cortico-tropic-hormone (ACTH) by tumors, Cushing's disease leads to hypercortisonemia, a precursor to a series of metabolic disorders and serious complications. Cushing's disease has high recurrence rate, short recurrence time and undiscovered recurrence reason after surgical resection. Qualitative or quantitative automatic image analysis of histology images
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Improving anti-cancer drug response prediction using multi-task learning on graph convolutional networks Methods (IF 4.8) Pub Date : 2023-12-27 Hancheng Liu, Wei Peng, Wei Dai, Jiangzhen Lin, Xiaodong Fu, Li Liu, Lijun Liu, Ning Yu
Predicting the therapeutic effect of anti-cancer drugs on tumors based on the characteristics of tumors and patients is one of the important contents of precision oncology. Existing computational methods regard the drug response prediction problem as a classification or regression task. However, few of them consider leveraging the relationship between the two tasks. In this work, we propose a Multi-task
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A near-infrared fluorescent probe with a substantial Stokes shift designed for the detection and imaging of β-galactosidase within living cells and animals Methods (IF 4.8) Pub Date : 2023-12-26 Yuan-Pin Lo, Narayanasamy Nivetha, Sivan Velmathi, Shu-Pao Wu
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SGAE-MDA: Exploring the MiRNA-disease associations in herbal medicines based on semi-supervised graph autoencoder Methods (IF 4.8) Pub Date : 2023-12-18 Lei Xu, Xiangzheng Fu, Linlin Zhuo, Zhecheng Zhou, Xuefeng Liao, Sha Tian, Ruofei Kang, Yifan Chen
Research indicates that miRNAs present in herbal medicines are crucial for identifying disease markers, advancing gene therapy, facilitating drug delivery, and so on. These miRNAs maintain stability in the extracellular environment, making them viable tools for disease diagnosis. They can withstand the digestive processes in the gastrointestinal tract, positioning them as potential carriers for specific
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Automatic ICD-10-CM coding via Lambda-Scaled attention based deep learning model Methods (IF 4.8) Pub Date : 2023-12-21 Sajida Raz Bhutto, Min Zeng, Kunying Niu, Sirajuddin Khoso, Muhammad Umar, Gul Lalley, Min Li
The International Classification of Diseases (ICD) serves as a global healthcare administration standard, with one of its editions being ICD-10-CM, an enhanced diagnostic classification system featuring numerous new codes for specific anatomic sites, co-morbidities, and causes. These additions facilitate conveying the complexities of various diseases. Currently, ICD-10 coding is widely adopted worldwide