Publications by authors named "Yanjie Wei"

In virtual drug screening, consensus docking is a standard in-silico approach consisting of a combined result from optimized docking experiments, a minimum of two results combination. Therefore, consensus docking is subjected to a lower success rate than the best docking method due to its mathematical nature, an unavoidable limitation. This study aims to overcome this drawback via random forest, an ensemble machine learning model.

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Various technologies and projects have been explored and developed for the synergetic control of environmental pollution and carbon emissions in aquatic ecosystems. Planting submerged vegetation in shallow waters was also expected to achieve this purpose. However, the magnitude and mechanism of carbon dioxide (CO) emission affected by submerged vegetation is not clear enough in complex aquatic ecosystems.

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Time-lapse microscopy imaging is a crucial technique in biomedical studies for observing cellular behavior over time, providing essential data on cell numbers, sizes, shapes, and interactions. Manual analysis of hundreds or thousands of cells is impractical, necessitating the development of automated cell segmentation approaches. Traditional image processing methods have made significant progress in this area, but the advent of deep learning methods, particularly those using U-Net-based networks, has further enhanced performance in medical and microscopy image segmentation.

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The algal-bacterial symbiosis system (ABSS) is considered as a sustainable wastewater treatment process. This review provides a comprehensive overview of the mechanisms of ABSS for the removal of common pollutant, heavy metals, and especially for emerging pollutants. For the macroscopical level, this review not only describes in detail the reactor types, influencing factors, and the development of the algal-bacterial process, but also innovatively proposes an emerging process that combines an ABSS with other processes, which enhances the efficiency of removing difficult-to-biodegrade pollutants.

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Extracellular microRNA (miRNA) expression data generated by different laboratories exhibit heterogeneity, which poses challenges for biologists without bioinformatics expertise. To address this, we introduce ExomiRHub (http://www.biomedical-web.

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Disulfide bonds, covalently formed by sulfur atoms in cysteine residues, play a crucial role in protein folding and structure stability. Considering their significance, artificial disulfide bonds are often introduced to enhance protein thermostability. Although an increasing number of tools can assist with this task, significant amounts of time and resources are often wasted owing to inadequate consideration.

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Accurate protein-ligand binding poses are the prerequisites of structure-based binding affinity prediction and provide the structural basis for in-depth lead optimization in small molecule drug design. However, it is challenging to provide reasonable predictions of binding poses for different molecules due to the complexity and diversity of the chemical space of small molecules. Similarity-based molecular alignment techniques can effectively narrow the search range, as structurally similar molecules are likely to have similar binding modes, with higher similarity usually correlated to higher success rates.

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Long pulse thermography (LPT) and shearography have been developed as primary methods for detecting debonding or delamination defects in composites due to their full-field imaging, non-contact operation, and high detection efficiency. Both methods utilize halogen lamps as the excitation source for thermal loading. However, the defects detected by the two techniques differ due to their distinct inspection mechanisms.

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Molecular dynamics simulation is a crucial research domain within the life sciences, focusing on comprehending the mechanisms of biomolecular interactions at atomic scales. Protein simulation, as a critical subfield, often utilizes MD for implementation, with trajectory data play a pivotal role in drug discovery. The advancement of high-performance computing and deep learning technology becomes popular and critical to predict protein properties from vast trajectory data, posing challenges regarding data features extraction from the complicated simulation data and dimensionality reduction.

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The amount of genetic data generated by Next Generation Sequencing (NGS) technologies grows faster than Moore's law. This necessitates the development of efficient NGS data processing and analysis algorithms. A filter before the computationally-costly analysis step can significantly reduce the run time of the NGS data analysis.

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Robust encapsulation and controllable release of biomolecules have wide biomedical applications ranging from biosensing, drug delivery to information storage. However, conventional biomolecule encapsulation strategies have limitations in complicated operations, optical instability, and difficulty in decapsulation. Here, we report a simple, robust, and solvent-free biomolecule encapsulation strategy based on gallium liquid metal featuring low-temperature phase transition, self-healing, high hermetic sealing, and intrinsic resistance to optical damage.

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Submerged plants affect nitrogen cycling in aquatic ecosystems. However, whether and how submerged plants change nitrous oxide (NO) production mechanism and emissions flux remains controversial. Current research primarily focuses on the feedback from NO release to variation of substrate level and microbial communities.

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Protein-ligand interaction prediction presents a significant challenge in drug design. Numerous machine learning and deep learning (DL) models have been developed to accurately identify docking poses of ligands and active compounds against specific targets. However, current models often suffer from inadequate accuracy or lack practical physical significance in their scoring systems.

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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 two diazabicyclic cores being the IAP binding motifs. We built in silico models of the five mimetics by the TwistDock workflow and evaluated their conformational tendency, which suggests that compound 3, whose linker is n-hexylene, possess the highest binding potency among the five.

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The automatic detection of cells in microscopy image sequences is a significant task in biomedical research. However, routine microscopy images with cells, which are taken during the process whereby constant division and differentiation occur, are notoriously difficult to detect due to changes in their appearance and number. Recently, convolutional neural network (CNN)-based methods have made significant progress in cell detection and tracking.

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Recent studies have shed light on the potential of circular RNA (circRNA) as a biomarker for disease diagnosis and as a nucleic acid vaccine. The exploration of these functionalities requires correct circRNA full-length sequences; however, existing assembly tools can only correctly assemble some circRNAs, and their performance can be further improved. Here, we introduce a novel feature known as the junction contig (JC), which is an extension of the back-splice junction (BSJ).

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We introduce a deep learning-based ligand pose scoring model called zPoseScore for predicting protein-ligand complexes in the 15th Critical Assessment of Protein Structure Prediction (CASP15). Our contributions are threefold: first, we generate six training and evaluation data sets by employing advanced data augmentation and sampling methods. Second, we redesign the "zFormer" module, inspired by AlphaFold2's Evoformer, to efficiently describe protein-ligand interactions.

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Spiking neural network (SNN) simulators play an important role in neural system modeling and brain function research. They can help scientists reproduce and explore neuronal activities in brain regions, neuroscience, brain-like computing, and other fields and can also be applied to artificial intelligence, machine learning, and other fields. At present, many simulators using central processing unit (CPU) or graphics processing unit (GPU) have been developed.

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Assessing the quality of sequencing data plays a crucial role in downstream data analysis. However, existing tools often achieve sub-optimal efficiency, especially when dealing with compressed files or performing complicated quality control operations such as over-representation analysis and error correction. We present RabbitQCPlus, an ultra-efficient quality control tool for modern multi-core systems.

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We present RabbitTClust, a fast and memory-efficient genome clustering tool based on sketch-based distance estimation. Our approach enables efficient processing of large-scale datasets by combining dimensionality reduction techniques with streaming and parallelization on modern multi-core platforms. 113,674 complete bacterial genome sequences from RefSeq, 455 GB in FASTA format, can be clustered within less than 6 min and 1,009,738 GenBank assembled bacterial genomes, 4.

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Synopsis of recent research by authors named "Yanjie Wei"

  • - Yanjie Wei's recent research focuses on integrating environmental science and biomedical technology, particularly exploring the synergetic reduction of carbon emissions in aquatic ecosystems through submerged vegetation and developing automated methods for analyzing biological data in microscopy.
  • - Wei has contributed to advancements in protein-ligand docking with the OpenDock framework, promoting increased accessibility and efficiency in drug discovery while addressing computing speed challenges associated with existing molecular docking tools.
  • - Wei's work includes the development of innovative approaches for wastewater treatment utilizing algal-bacterial symbiosis systems and addressing the heterogeneity in extracellular microRNA data through the ExomiRHub database, thus supporting more effective biological research and applications.