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1.
Org Biomol Chem ; 22(14): 2851-2862, 2024 04 03.
Artigo em Inglês | MEDLINE | ID: mdl-38516867

RESUMO

Hypochlorous acid (HOCl) released from activated leukocytes plays a significant role in the human immune system, but is also implicated in numerous diseases due to its inappropriate production. Chlorinated nucleobases induce genetic changes that potentially enable and stimulate carcinogenesis, and thus have attracted considerable attention. However, their multiple halogenation sites pose challenges to identify them. As a good complement to experiments, quantum chemical computation was used to uncover chlorination sites and chlorinated products in this study. The results indicate that anion salt forms of all purine compounds play significant roles in chlorination except for adenosine. The kinetic reactivity order of all reaction sites in terms of the estimated apparent rate constant kobs-est (in M-1 s-1) is heterocyclic NH/N (102-107) > exocyclic NH2 (10-2-10) > heterocyclic C8 (10-5-10-1), but the order is reversed for thermodynamics. Combining kinetics and thermodynamics, the numerical simulation results show that N9 is the most reactive site for purine bases to form the main initial chlorinated product, while for purine nucleosides N1 and exocyclic N2/N6 are the most reactive sites to produce the main products controlled by kinetics and thermodynamics, respectively, and C8 is a possible site to generate the minor product. The formation mechanisms of biomarker 8-Cl- and 8-oxo-purine derivatives were also investigated. Additionally, the structure-kinetic reactivity relationship study reveals a good correlation between lg kobs-est and APT charge in all purine compounds compared to FED2 (HOMO), which proves again that the electrostatic interaction plays a key role. The results are helpful to further understand the reactivity of various reaction sites in aromatic compounds during chlorination.


Assuntos
Nucleosídeos , Poluentes Químicos da Água , Humanos , Nucleosídeos/química , Halogenação , Domínio Catalítico , Nucleosídeos de Purina , Ácido Hipocloroso/química , Cinética , Cloro/química , Poluentes Químicos da Água/química
2.
Artigo em Inglês | MEDLINE | ID: mdl-38412702

RESUMO

This study compares the skin structures of Rana kukunoris with two different skin colors living in the same area of Haibei in the Northeastern Qinghai-Tibet Plateau. The skin thickness of the khaki R. kukunoris was significantly greater than that of the brown R. kukunoris (P < 0.01), and significantly more mucous and granular glands were present on the dorsal skin of the khaki frog (P < 0.05). Meanwhile, the melanocytes on the dorsal skin of the brown frog were significantly larger than those on the khaki one (P < 0.05). Morphological changes in the expansion and aggregation of melanocytes seemed to deepen the skin color of R. kukunoris. Moreover, transcriptome sequencing identified tyrosine metabolism, melanogenesis, and riboflavin metabolism as the main pathways involved in melanin formation and metabolism in brown R. kukunoris. TYR, MC1R was upregulated as the skin color of R. kukunoris was deepened and contributed to melanin production and metabolism. In contrast, the khaki frog had significantly more upregulated genes and metabolic pathways related to autoimmunity. The khaki frog appeared to defend against ultraviolet (UV) radiation-induced damage by secreting mucus and small molecular peptides, whereas the brown frog protected itself by distributing a large amount of melanin. Hence, the different skin colors of R. kukunoris might represent different adaptation strategies for survival in the intense UV radiation environment of the Qinghai-Tibet Plateau.

3.
Brief Bioinform ; 25(2)2024 Jan 22.
Artigo em Inglês | MEDLINE | ID: mdl-38388682

RESUMO

Proteins play an important role in life activities and are the basic units for performing functions. Accurately annotating functions to proteins is crucial for understanding the intricate mechanisms of life and developing effective treatments for complex diseases. Traditional biological experiments struggle to keep pace with the growing number of known proteins. With the development of high-throughput sequencing technology, a wide variety of biological data provides the possibility to accurately predict protein functions by computational methods. Consequently, many computational methods have been proposed. Due to the diversity of application scenarios, it is necessary to conduct a comprehensive evaluation of these computational methods to determine the suitability of each algorithm for specific cases. In this study, we present a comprehensive benchmark, BeProf, to process data and evaluate representative computational methods. We first collect the latest datasets and analyze the data characteristics. Then, we investigate and summarize 17 state-of-the-art computational methods. Finally, we propose a novel comprehensive evaluation metric, design eight application scenarios and evaluate the performance of existing methods on these scenarios. Based on the evaluation, we provide practical recommendations for different scenarios, enabling users to select the most suitable method for their specific needs. All of these servers can be obtained from https://csuligroup.com/BEPROF and https://github.com/CSUBioGroup/BEPROF.


Assuntos
Aprendizado Profundo , Benchmarking , Proteínas , Algoritmos , Sequenciamento de Nucleotídeos em Larga Escala
4.
Org Lett ; 26(8): 1623-1628, 2024 Mar 01.
Artigo em Inglês | MEDLINE | ID: mdl-38363721

RESUMO

An extremely concise, scalable, and stereoselective synthesis of a privileged chiral skeleton based on 2,2'-biindolyl and commercially available chiral building blocks has been developed. This novel skeleton allows for easy access to a range of bisphosphine ligands (decagram scale, up to 58% total yield, only three steps). The synthetic method is characterized by an efficient central-to-axial chirality transfer strategy. In particular, the superior performance of the ligands has been demonstrated in diverse reactions, including several asymmetric hydrogenations, asymmetric conjugate reductions, and cycloisomerization reactions, indicating a great potential for the application of the newly developed chiral backbones in further modifications and exploration of novel chiral ligands and catalysts.

5.
Sci Total Environ ; 918: 170654, 2024 Mar 25.
Artigo em Inglês | MEDLINE | ID: mdl-38331284

RESUMO

Microplastics (MPs) are now prevalent in aquatic ecosystems, prompting the use of constructed wetlands (CWs) for remediation. However, the interaction between MPs and CWs, including removal efficiency, mechanisms, and impacts, remains a subject requiring significant investigation. This review investigates the removal of MPs in CWs and assesses their impact on the removal of carbon, nitrogen, and phosphorus. The analysis identifies crucial factors influencing the removal of MPs, with substrate particle size and CWs structure playing key roles. The review highlights substrate retention as the primary mechanism for MP removal. MPs hinder plant nitrogen uptake, microbial growth, community composition, and nitrogen-related enzymes, reducing nitrogen removal in CWs. For phosphorus and carbon removal, adverse effects of MPs on phosphorus elimination are observed, while their impact on carbon removal is minimal. Further research is needed to understand their influence fully. In summary, CWs are a promising option for treating MPs-contaminated wastewater, but the intricate relationship between MPs and CWs necessitates ongoing research to comprehend their dynamics and potential consequences.


Assuntos
Nitrogênio , Eliminação de Resíduos Líquidos , Fósforo , Microplásticos , Plásticos , Áreas Alagadas , Ecossistema , Carbono , Nutrientes
6.
J Colloid Interface Sci ; 663: 157-166, 2024 Jun.
Artigo em Inglês | MEDLINE | ID: mdl-38401437

RESUMO

Perovskite nanoplatelets (NPLs), as a promising material to achieve pure blue emission, have attracted significant attention in high gamut displays. However, the high surface-to-volume ratio and the loosely connected ligands of NPLs make them susceptible to degradation from light, air and heat. As a result, NPLs often exhibit low photoluminescence (PL) intensity and instability. Here, an Mn-ligand passivation strategy is proposed, in which Mn-doped DMAPbBr3 is used as a precursor. During the perovskite transformation, Mn2+ ions migrate from the lattice of DMAPbBr3 to the surface of CsPbBr3 NPLs, which have strong binding forces with ligands. The final products Mn-CsPbBr3 (M-CPB) NPLs are then acquired by the ligand-induced ripening growth process, which not only exhibit pure blue emission with narrow full width at half maximum (FWHM), but also possess near-unity PL quantum yields (QYs). Besides, M-CPB NPLs show excellent stability due to the strong Mn-ligand passivation layer. Based on the new growth mechanism discovery, the reaction time can be shortened to several minutes by heating. The innovative growth model proposed in this work will provide a paradigm for designing and optimizing future synthesis schemes.

7.
J Hazard Mater ; 468: 133780, 2024 Apr 15.
Artigo em Inglês | MEDLINE | ID: mdl-38401213

RESUMO

Chemically durable and effective adsorbents for radiotoxic TeOx2- (TeIV and TeVI) anions remain in great demand for contamination remediation. Herein, a low-cost iron-based metal-organic framework (MIL-101(Fe)) was used as an adsorbent to capture TeOx2- anions from contaminated solution with ultrafast kinetics and record-high adsorption capacity of 645 mg g-1 for TeO32- and 337 mg g-1 for TeO42-, outperforming previously reported adsorbents. Extended X-ray absorption fine structure (EXAFS) and density functional theory (DFT) calculations confirmed that the capture of TeOx2- by MIL-101(Fe) was mediated by the unique C-O-Te and Fe-O-Te coordination bonds at corresponding optimal adsorption sites, which enabled the selective adsorption of TeOx2- from solution and further irreversible immobilization under the geological environment. Meanwhile, MIL-101(Fe) works steadily over a wide pH range of 4-10 and at high concentrations of competing ions, and it is stable under ß-irradiation even at high dose of 200 kGy. Moreover, the MIL-101(Fe) membrane was fabricated to efficiently remove TeO32- ions from seawater for practical use, overcoming the secondary contamination and recovery problems in powder adsorption. Finally, the good sustainability of MIL-101(Fe) was evaluated from three perspectives of technology, environment, and society. Our strategy provides an alternative to traditional removal methods that should be attractive for Te contamination remediation.

8.
Materials (Basel) ; 17(2)2024 Jan 20.
Artigo em Inglês | MEDLINE | ID: mdl-38276446

RESUMO

The corrosion of grounding grid materials in soil is a prominent factor in power and electrical equipment failure. This paper aims to delve into the corrosion characteristics of grounding grid materials and the corresponding methods of safeguarding against this phenomenon. Firstly, the influencing factors of the soil environment on the corrosion of the grounding grid are introduced, including soil physicochemical properties, microorganisms, and stray currents. Then, the corrosion behavior and durability of common grounding grid materials such as copper, carbon steel, and galvanized steel are discussed in detail and compared comprehensively. In addition, commonly used protective measures in China and outside China, including anti-corrosion coatings, electrochemical protection, and other technologies are introduced. Finally, it summarizes the current research progress and potential future directions of this field of study.

9.
Dalton Trans ; 53(5): 2153-2158, 2024 Jan 30.
Artigo em Inglês | MEDLINE | ID: mdl-38189118

RESUMO

Mn-based perovskites have become a new candidate material for backlight display applications. However, low efficiency and poor stability are the key problems limiting the application of Mn-based perovskites. In this work, Zn-doped and SiO2-encapsulated Cs3MnBr5, denoted as Cs3Mn0.93Zn0.07Br5@SiO2 (CMZBS), was successfully synthesized to improve the photoluminescence quantum yield (PLQY) and stability. After Zn doping, the PLQY increased from 51% to 72% due to the reduction in the energy transfer between [MnBr4]2-. The PLQY can be further improved to 80% after coating SiO2. Compared with Cs3MnBr5 (CMB), CMZBS showed better stability against thermal, air, light, and polar solvents (ethanol and isopropanol). In addition, a white LED (WLED) device with a CIE of (0.323, 0.325) was fabricated by integrating CMZBS and the red phosphor K2SiF6:Mn4+ on a 465 nm blue GaN chip, which exhibited a high luminous efficiency of 92 lm W-1 and excellent stability, demonstrating its great potential application in wide color gamut displays.

10.
Bioresour Technol ; 394: 130179, 2024 Feb.
Artigo em Inglês | MEDLINE | ID: mdl-38092075

RESUMO

The current study generated co-pyrolysis biochar by pyrolyzing rice straw and pig manure at 300 °C and subsequently applying it in a field. Co-pyrolysis biochar demonstrated superior efficiency in mitigating agricultural non-point source pollution compared to biochar derived from individual sources. Furthermore, it displayed notable capabilities in retaining and releasing nutrients, resulting in increased soil levels of total nitrogen, total phosphorus, and organic matter during the maturation stage of rice. Moreover, co-pyrolysis biochar influences soil microbial communities, potentially impacting nutrient cycling. During the rice maturation stage, the soil treated with co-pyrolysis biochar exhibited significant increases in available nutrients and rice yield compared to the control (p < 0.05). These findings emphasize the potential of co-pyrolysis biochar for in-situ nutrient retention and enhanced soil nutrient utilization. To summarize, the co-pyrolysis of agricultural waste materials presents a promising approach to waste management, contributing to controlling non-point source pollution, improving soil fertility, and promoting crop production.


Assuntos
Poluição Difusa , Oryza , Animais , Suínos , Solo , Temperatura , Nitrogênio , Fósforo , Pirólise , Carvão Vegetal , Nutrientes
11.
J Am Chem Soc ; 145(29): 15695-15701, 2023 Jul 26.
Artigo em Inglês | MEDLINE | ID: mdl-37435957

RESUMO

The highly enantioselective and complete hydrogenation of protected indoles and benzofurans has been developed, affording facile access to a range of chiral three-dimensional octahydroindoles and octahydrobenzofurans, which are prevalent in many bioactive molecules and organocatalysts. Remarkably, we are in control of the nature of the ruthenium N-heterocyclic carbene complex and employed the complex as both homogeneous and heterogeneous catalysts, providing new avenues for its potential applications in the asymmetric hydrogenation of more challenging aromatic compounds.

12.
J Mol Biol ; 435(14): 167945, 2023 07 15.
Artigo em Inglês | MEDLINE | ID: mdl-36621533

RESUMO

Current sequence-based predictors of protein-binding residues (PBRs) belong to two distinct categories: structure-trained vs. intrinsic disorder-trained. Since disordered PBRs differ from structured PBRs in several ways, including ability to bind multiple partners by folding into different conformations and enrichment in different amino acids, the structure-trained and disorder-trained predictors were shown to provide inaccurate results for the other annotation type. A simple consensus-based solution that combines structure- and disorder-trained methods provides limited levels of predictive performance and generates relatively many cross-predictions, where residues that interact with other ligand types are predicted as PBRs. We address this unsolved problem by designing a novel and fast deep-learner, DeepPRObind, that relies on carefully designed modular convolutional architecture and uses innovative aggregate input features. Comparative empirical tests on a low-similarity test dataset reveal that DeepPRObind generates accurate predictions of structured and disordered PBRs and low amounts of cross-predictions, outperforming a comprehensive collection of 12 predictors of PBRs. Given the relatively low runtime of DeepPRObind (40 seconds per protein), we further validate its results based on an analysis of putative PBRs in the yeast proteome, confirming that interactions in disordered regions are enriched among hub proteins. We release DeepPRObind as a convenient web server at https://www.csuligroup.com/DeepPRObind/.


Assuntos
Aminoácidos , Biologia Computacional , Aprendizado Profundo , Ligação Proteica , Biologia Computacional/métodos , Bases de Dados de Proteínas , Proteoma/química , Conformação Proteica
13.
Nucleic Acids Res ; 51(5): e25, 2023 03 21.
Artigo em Inglês | MEDLINE | ID: mdl-36629262

RESUMO

The sequence-based predictors of RNA-binding residues (RBRs) are trained on either structure-annotated or disorder-annotated binding regions. A recent study of predictors of protein-binding residues shows that they are plagued by high levels of cross-predictions (protein binding residues are predicted as nucleic acid binding) and that structure-trained predictors perform poorly for the disorder-annotated regions and vice versa. Consequently, we analyze a representative set of the structure and disorder trained predictors of RBRs to comprehensively assess quality of their predictions. Our empirical analysis that relies on a new and low-similarity benchmark dataset reveals that the structure-trained predictors of RBRs perform well for the structure-annotated proteins while the disorder-trained predictors provide accurate results for the disorder-annotated proteins. However, these methods work only modestly well on the opposite types of annotations, motivating the need for new solutions. Using an empirical approach, we design HybridRNAbind meta-model that generates accurate predictions and low amounts of cross-predictions when tested on data that combines structure and disorder-annotated RBRs. We release this meta-model as a convenient webserver which is available at https://www.csuligroup.com/hybridRNAbind/.


Assuntos
Proteínas , Proteínas de Ligação a RNA , RNA , Biologia Computacional/métodos , Bases de Dados de Proteínas , Ligação Proteica/genética , Proteínas/química , RNA/química , Proteínas de Ligação a RNA/química
14.
Artigo em Inglês | MEDLINE | ID: mdl-35476573

RESUMO

The understanding of protein functions is critical to many biological problems such as the development of new drugs and new crops. To reduce the huge gap between the increase of protein sequences and annotations of protein functions, many methods have been proposed to deal with this problem. These methods use Gene Ontology (GO) to classify the functions of proteins and consider one GO term as a class label. However, they ignore the co-occurrence of GO terms that is helpful for protein function prediction. We propose a new deep learning model, named DeepPFP-CO, which uses Graph Convolutional Network (GCN) to explore and capture the co-occurrence of GO terms to improve the protein function prediction performance. In this way, we can further deduce the protein functions by fusing the predicted propensity of the center function and its co-occurrence functions. We use Fmax and AUPR to evaluate the performance of DeepPFP-CO and compare DeepPFP-CO with state-of-the-art methods such as DeepGOPlus and DeepGOA. The computational results show that DeepPFP-CO outperforms DeepGOPlus and other methods. Moreover, we further analyze our model at the protein level. The results have demonstrated that DeepPFP-CO improves the performance of protein function prediction. DeepPFP-CO is available at https://csuligroup.com/DeepPFP/.


Assuntos
Aprendizado Profundo , Ontologia Genética , Proteínas/genética , Sequência de Aminoácidos
15.
Bioinformatics ; 39(1)2023 01 01.
Artigo em Inglês | MEDLINE | ID: mdl-36458923

RESUMO

MOTIVATION: Protein essentiality is usually accepted to be a conditional trait and strongly affected by cellular environments. However, existing computational methods often do not take such characteristics into account, preferring to incorporate all available data and train a general model for all cell lines. In addition, the lack of model interpretability limits further exploration and analysis of essential protein predictions. RESULTS: In this study, we proposed DeepCellEss, a sequence-based interpretable deep learning framework for cell line-specific essential protein predictions. DeepCellEss utilizes a convolutional neural network and bidirectional long short-term memory to learn short- and long-range latent information from protein sequences. Further, a multi-head self-attention mechanism is used to provide residue-level model interpretability. For model construction, we collected extremely large-scale benchmark datasets across 323 cell lines. Extensive computational experiments demonstrate that DeepCellEss yields effective prediction performance for different cell lines and outperforms existing sequence-based methods as well as network-based centrality measures. Finally, we conducted some case studies to illustrate the necessity of considering specific cell lines and the superiority of DeepCellEss. We believe that DeepCellEss can serve as a useful tool for predicting essential proteins across different cell lines. AVAILABILITY AND IMPLEMENTATION: The DeepCellEss web server is available at http://csuligroup.com:8000/DeepCellEss. The source code and data underlying this study can be obtained from https://github.com/CSUBioGroup/DeepCellEss. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.


Assuntos
Aprendizado Profundo , Proteínas/metabolismo , Sequência de Aminoácidos , Software , Linhagem Celular , Biologia Computacional/métodos
16.
Bioresour Technol ; 367: 128240, 2023 Jan.
Artigo em Inglês | MEDLINE | ID: mdl-36332867

RESUMO

Pyrrhotite is a promising electron donor for autotrophic denitrification. Using pyrrhotite as the substrate in constructed wetlands (CWs) can enhance the nitrogen removal performance in carbon-limited wastewater treatment. However, the role of plants in pyrrhotite-integrated CW is under debate as the oxygen released from plant roots may destroy the anoxic condition for autotrophic denitrification. This study compared pyrrhotite-integrated CWs with and without plants and identified the effects of plants' presence in nitrogen removal, pyrrhotite oxidized dissolution, and microbial community. The results show that plants enhanced the TN removal significantly (from 41.6 ± 3.9 % to 97.1 ± 2.6 %). Plants can accelerate the PAD in CW through the strengthening of pyrrhotite dissolution. Enriched functional (Thiobacillus and Acidiferrobacter) and a more complex bacterial co-occurrence network has been found in CW with plants. This study identified the role of plants in PAD acceleration, providing an in-depth understanding of pyrrhotite in CW systems.


Assuntos
Nitrogênio , Áreas Alagadas , Desnitrificação , Processos Autotróficos , Plantas , Águas Residuárias , Eliminação de Resíduos Líquidos
17.
Angew Chem Int Ed Engl ; 61(47): e202209494, 2022 11 21.
Artigo em Inglês | MEDLINE | ID: mdl-36200408

RESUMO

A sequential phosphine-catalyzed asymmetric [3+2] annulation of aldimines with allenoates and oxidative central-to-axial chirality transfer strategy has been developed. This approach is operationally simple, allowing for rapid access to a range of axially chiral CF3 -containing 2-arylpyrroles with high enantiomeric excess. Furthermore, an atroposelective synthesis of esaxerenone is presented, illustrating the practical potential of the reported method.


Assuntos
Fosfinas , Catálise , Estereoisomerismo , Estresse Oxidativo
18.
Angew Chem Int Ed Engl ; 61(24): e202203212, 2022 06 13.
Artigo em Inglês | MEDLINE | ID: mdl-35357071

RESUMO

A phosphine-catalyzed highly enantioselective and diastereoselective (up to 98 % ee and >20 : 1 dr) (3+2) annulation between vinylcyclopropanes and N-tosylaldimines has been developed, which allows facile access to a range of highly functionalized chiral pyrrolidines. Notably, this method makes use of vinylcyclopropanes as a synthon for phosphine-mediated asymmetric annulation reaction, which will offer new opportunities for potential applications of cyclopropanes substrates in phosphine-catalyzed organic transformations.


Assuntos
Iminas , Pirrolidinas , Catálise , Fosfinas , Estereoisomerismo
19.
Brief Bioinform ; 23(1)2022 01 17.
Artigo em Inglês | MEDLINE | ID: mdl-34905768

RESUMO

Proteins with intrinsically disordered regions (IDRs) are common among eukaryotes. Many IDRs interact with nucleic acids and proteins. Annotation of these interactions is supported by computational predictors, but to date, only one tool that predicts interactions with nucleic acids was released, and recent assessments demonstrate that current predictors offer modest levels of accuracy. We have developed DeepDISOBind, an innovative deep multi-task architecture that accurately predicts deoxyribonucleic acid (DNA)-, ribonucleic acid (RNA)- and protein-binding IDRs from protein sequences. DeepDISOBind relies on an information-rich sequence profile that is processed by an innovative multi-task deep neural network, where subsequent layers are gradually specialized to predict interactions with specific partner types. The common input layer links to a layer that differentiates protein- and nucleic acid-binding, which further links to layers that discriminate between DNA and RNA interactions. Empirical tests show that this multi-task design provides statistically significant gains in predictive quality across the three partner types when compared to a single-task design and a representative selection of the existing methods that cover both disorder- and structure-trained tools. Analysis of the predictions on the human proteome reveals that DeepDISOBind predictions can be encoded into protein-level propensities that accurately predict DNA- and RNA-binding proteins and protein hubs. DeepDISOBind is available at https://www.csuligroup.com/DeepDISOBind/.


Assuntos
Proteínas de Ligação a DNA/química , DNA/química , Aprendizado Profundo , Proteínas Intrinsicamente Desordenadas/química , Proteínas de Ligação a RNA/química , RNA/química , Biologia Computacional/métodos , DNA/metabolismo , Proteínas de Ligação a DNA/metabolismo , Humanos , Redes Neurais de Computação , Ácidos Nucleicos/metabolismo , Ligação Proteica , Proteoma/metabolismo , RNA/metabolismo , Proteínas de Ligação a RNA/metabolismo
20.
Brief Bioinform ; 23(1)2022 01 17.
Artigo em Inglês | MEDLINE | ID: mdl-34498677

RESUMO

Long non-coding RNAs (lncRNAs) are a class of RNA molecules with more than 200 nucleotides. A growing amount of evidence reveals that subcellular localization of lncRNAs can provide valuable insights into their biological functions. Existing computational methods for predicting lncRNA subcellular localization use k-mer features to encode lncRNA sequences. However, the sequence order information is lost by using only k-mer features. We proposed a deep learning framework, DeepLncLoc, to predict lncRNA subcellular localization. In DeepLncLoc, we introduced a new subsequence embedding method that keeps the order information of lncRNA sequences. The subsequence embedding method first divides a sequence into some consecutive subsequences and then extracts the patterns of each subsequence, last combines these patterns to obtain a complete representation of the lncRNA sequence. After that, a text convolutional neural network is employed to learn high-level features and perform the prediction task. Compared with traditional machine learning models, popular representation methods and existing predictors, DeepLncLoc achieved better performance, which shows that DeepLncLoc could effectively predict lncRNA subcellular localization. Our study not only presented a novel computational model for predicting lncRNA subcellular localization but also introduced a new subsequence embedding method which is expected to be applied in other sequence-based prediction tasks. The DeepLncLoc web server is freely accessible at http://bioinformatics.csu.edu.cn/DeepLncLoc/, and source code and datasets can be downloaded from https://github.com/CSUBioGroup/DeepLncLoc.


Assuntos
Aprendizado Profundo , RNA Longo não Codificante , Biologia Computacional/métodos , Redes Neurais de Computação , RNA Longo não Codificante/genética , Software
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