Peng Chen (陈鹏)Anhui University · Institute of Health Sciences
Peng Chen (陈鹏)
Dr. Chen
About
169
Publications
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Introduction
Skills and Expertise
Additional affiliations
June 2012 - February 2014
January 2011 - January 2013
Independent Researcher
Position
- Professor (Associate)
June 2009 - December 2011
Publications
Publications (169)
Hot spot residues of proteins are fundamental interface residues that help proteins perform their functions. Detecting hot spots by experimental methods is costly and time-consuming. Sequential and structural information has been widely used in the computational prediction of hot spots. However, structural information is not always available. In th...
Sequence-based understanding and identification of protein binding interfaces is a challenging research topic due to the complexity in protein systems and the imbalanced distribution between interface and noninterface residues. This paper presents an outlier detection idea to address the redundancy problem in protein interaction data. The cleaned t...
Protein-protein interactions play essential roles in protein function determination and drug design. Numerous methods have been proposed to recognize their interaction sites, however, only a small proportion of protein complexes have been successfully resolved due to the high cost. Therefore, it is important to improve the performance for predictin...
Protein domains are structural and fundamental functional units of proteins. The information of protein domain boundaries is helpful in understanding the evolution, structures and functions of proteins, and also plays an important role in protein classification. In this paper, we propose a support vector regression-based method to address the probl...
This paper proposes a novel method that can predict protein interaction sites in heterocomplexes using residue spatial sequence profile and evolution rate approaches. The former represents the information of multiple sequence alignments while the latter corresponds to a residue's evolutionary conservation score based on a phylogenetic tree. Three p...
Goal: Cervical cancer is one of the most common cancers in women worldwide, ranking among the top four. Unfortunately, it is also the fourth leading cause of cancer-related deaths among women, particularly in developing countries where incidence and mortality rates are higher compared to developed nations. Colposcopy can aid in the early detection...
Achieving the precise and real-time detection of wheat spikes play a crucial role in wheat growth monitoring for precision agriculture community. Machine-learning methods are commonly introduced to automatically detect and count the wheat spikes, which need carefully selected hand-crafted feature descriptors, leading to time-consuming and poor perf...
Real-time object detectors deployed on general-purpose graphics processing units (GPUs) or embedded devices allow their mass usage in industrial applications at an affordable cost. However, existing state-of-the-art object detectors are difficult to meet the requirements of high accuracy and low inference latency simultaneously in industrial applic...
Protein hotspot residues are key sites that mediate protein-protein interactions. Accurate identification of these residues is essential for understanding the mechanism from protein to function and for designing drug targets. Current research has mostly focused on using machine learning methods to predict hot spots from known interface residues, wh...
The quality of concrete is crucial for the safety of facilities. Specifically, the ex-posed surface defects of the bridge seriously affect its strength and aesthetics. However, due to the influence of weather and light, different types of defects on the concrete surface may potentially overlap, making it difficult for classification algorithms to i...
In recent times, there has been notable progress in the effectiveness of Generative Adversarial Networks (GANs) for synthesizing images. Consequently, numerous studies have started utilizing GANs for image editing purposes. To enable editing of real images, it is crucial to embed a real image into the latent space of GANs. This involves obtaining t...
Printed Circuit Board (PCB) is a significant component of the power system, and their surface defects may hinder electrical performance. Therefore, developing an efficient and precise PCB surface defect detection method is crucial for ensuring the state of the entire power system. In recent years, there has been growing interest in lightweight atte...
Corneal ulcer is a common disease located in the eye. If not detected and treated in a timely manner, it is highly likely to cause irreversible damage to the patient’s eyes, and even lead to blindness. Traditional detection methods have drawbacks such as complex steps and painful inspection processes. So there is an urgent need to develop a fast, c...
As a high mortality disease, cancer seriously affects people's life and well-being. Reliance on pathologists to assess disease progression from pathological images is inaccurate and burdensome. Computer aided diagnosis (CAD) system can effectively assist diagnosis and make more credible decisions. However, a large number of labeled medical images t...
Protein-ligand binding can play an important role in many fields. It is of great importance to accurately predict the binding affinity between molecules by computational methods. Most computational binding affinity methods require molecular structures. However, there are still a large number of protein molecules with known amino acid sequences whos...
Background
As the basic material of life, protein regulates physiological activities such as material in and out of cells, signal transduction, metabolism and so on. However, studies have shown that proteins cannot perform these functions alone in cells, and need to be combined with ligands to perform functions.
Background
As the basic material of...
Arrhythmia is an important group of cardiovascular diseases, which can suddenly attack and cause sudden death, or continue to affect the heart and cause its failure. Electrocardiogram (ECG) is an important tool for detecting arrhythmia, but its analysis is time-consuming and dependent on extensive expertise. Deep neural networks have become a popul...
Humans spend about one-third of their time in sleep. Sleep is closely related to human physical and mental health and is a very important life activity. Automatic sleep stage classification is an important tool for analyzing sleep quality. However, due to different difficulties such as poor signal quality, the minor difference between different sta...
Surface defect detection is an important part of the steel production process. Recently, attention mechanisms have been widely used in steel surface defect detection to ensure product quality. The existing attention modules cannot distinguish the difference between steel surface images and natural images. Therefore, we propose an adaptive graph cha...
As a daily staple food of more than one third of the world’s population, wheat is one of the main food crops in the world. The increase in wheat production will help meet the current global food security needs. In the process of wheat growth, diseases and insect pests have great influence on the yield, which leads to a significant decline. Wheat sp...
Recently, single-stage detection methods have made great progress, achieving comparable accuracy to two-stage detection methods. However, they have poor performance over small object detection. In this work, we improve the performance of the single-stage detector for detecting objects of small sizes. The proposed method makes two major novel contri...
The Coronavirus Disease 2019 (COVID-19) is the pandemic that has had the greatest impact on world economic development in recent years. Early detection is critical to identify patients with COVID-19, chest x-ray is used for early detection is a rapid, extensive and cost-effective method. The existing technology use deep learning methods, and have a...
Non-invasive blood pressure prediction is an important method to prevent diseases such as hypertension. This paper proposes a sub-network aggregation with large convolution kernel convolution to predict non-invasive blood pressure. First, the large convolution kernel module in the backbone network is used to extract PPG data features. Then, the mul...
Medical images can be accurately segmented to provide reliable basis for clinical diagnosis and pathology research, and assist doctors to make more accurate diagnosis, as well as deep learning technology can accelerate this process. Convolutional Neural Networks (CNNs) and Transformer have become two mainstream architectures of deep learning in med...
Functional near-infrared spectroscopy (fNIRS), a non-invasive optical technique, is widely used to monitor brain activities for disease diagnosis and brain-computer interfaces (BCIs). Deep learning-based fNIRS classification faces three major barriers: limited datasets, confusing evaluation criteria, and domain barriers. We apply more appropriate e...
Surface defect classification plays an important role in the assessment of production status and analyzing possible defect causes of hot rolled strip steel. It is extremely challenging owing to the rare occurrence and various appearances of defects. In this work, an improved deep learning model is proposed to solve the problem of poor classificatio...
An accurate and robust pest detection and recognition scheme is an important step to enable the high quality and yield of agricultural products according to integrated pest management (IPM). Due to pose-variant, serious overlap, dense distribution, and interclass similarity of agricultural pests, the precise detection of multi-classes pest faces gr...
The accurate and robust crop pest detection system is an important step to enable the reliable forecasting of agricultural pest in the community of precision agriculture, attracting great attention in many countries. For achieving the automatic recognition and detection of agricultural pest, previous methods adopt image processing-based methods, le...
Putative identification of metabolites is comparing the observed mass spectrum of the sample to a reference library. However, the existing libraries cannot contain all the mass spectra due to the huge number of compounds. The identification process will fail if the target mass spectrum does not exist in the library. One solution is augmenting the l...
Protein hot spot residues are functional sites in protein–protein interactions. Biological experimental methods are traditionally used to identify hot spot residues, which is laborious and time-consuming. Thus a variety of computational methods were widely used in recent years. Despite the success of computational methods in hot spot identification...
Functional near-infrared spectroscopy (fNIRS) is a promising neuroimaging technology. The fNIRS classification problem has always been the focus of the brain-computer interface (BCI). Inspired by the success of Transformer based on self-attention mechanism in the fields of natural language processing and computer vision, we propose an fNIRS classif...
Video object and human action detection are applied in many fields, such as video surveillance, face recognition, etc. Video object detection includes object classification and object location within the frame. Human action recognition is the detection of human actions. Usually, video detection is more challenging than image detection, since video...
Surface defect inspection is a key step to ensure the quality of the hot rolled steel surface. However, current advanced detection methods have high precision but low detection speed, which hinders the application of the detector in actual production. In this work, a real-time detection network (RDN) focusing on both speed and accuracy is proposed...
BACKGROUND
Pests cause significant damage to agricultural crops and reduce crop yields. Use of manual methods of pest forecasting for integrated pest management is labor‐intensive and time‐consuming. Here, we present an automatic system for monitoring pests in large fields, with the aim of replacing manual forecasting. The system comprises an autom...
The computational methods of protein-protein interaction sites prediction can effectively avoid the shortcomings of high cost and time in traditional experimental approaches. However, the serious class imbalance between interface and non-interface residues on the protein sequences limits the prediction performance of these methods. This work theref...
The change of core temperature of blast furnace reflects the working status of hearth. However, the temperature of core dead stock column can not be measured by sensors directly. Therefore, a prediction model of Core Dead Stock Column Temperature is proposed in this work based on primary component analysis (PCA) and ridge regression algorithms, whe...
Effective pest management and control are the key factors in the agricultural food safety field. Therefore, the automatic monitoring and precise recognition of crop pests have a high practical value in the process of agricultural planting. Over the years, pest recognition and detection results have been rapidly improved with the development of deep...
Backgroud: The prediction of drug-target interactions (DTIs) is of great significance in drug development. It is time-consuming and expensive in traditional experimental methods. Machine learning can reduce the cost of prediction and is limited by the characteristics of imbalanced datasets and problems of essential feature selection.
Methods:
The...
Surface defect classification of hot-rolled strip based on machine vision is a challenge task caused by the diversity of defect morphology, high inter-class similarity, and the real-time requirements in actual production. In this work, VGG16-ADB, an improved VGG16 convolution neural network, is proposed to address the problem of defect identificati...
Accurate prediction of binding affinity between protein and ligand is a very important step in the field of drug discovery. Although there are many methods based on different assumptions and rules do exist, prediction performance of protein–ligand binding affinity is not satisfactory so far. This paper proposes a new cascade graph-based convolution...
Background
Verifying interactions between drugs and targets is key to discover new drugs. Many computational methods have been developed to predict drug-target interactions and performed successfully, but challenges still exist in the field.
Objective
We try to develop a machine learning method to predict drug-target affinity, which can determine...
Compound identification in electron-ionization mass spectrometry (EI-MS) is usually achieved by matching the query mass spectrum to the well-collected reference spectral library. Although various similarity methods have been developed in recent years, it is still difficult to distinguish some similar mass spectra, especially for isomers. In this wo...
The wheat mite always causes major damage in wheat plants and results in significant yield losses. Therefore, detecting wheat mites can provide important information, such as pest population dynamics and integrated pest management by monitoring wheat mite populations. However, the automatic classification and counting of wheat mites from images tak...
Papers published in top conferences or journals is an important measure of the innovation ability of institutions, and ranking paper acceptance rate can be helpful for evaluating affiliation potential in academic research. Most studies only focus on the paper quality itself, and apply simple statistical data to estimate the contribution of institut...
The occurrence of crop pests and diseases always affects the development of agriculture seriously, while pest meteorology showed that climate is important in affecting the occurrence. Recently, recurrent neural network (RNN) has been broadly applied in various fields, which was designed for modeling sequential data and has been testified to be quit...
The task of drug-target interaction (DTI) prediction plays important roles in drug development. The experimental methods in DTIs are time-consuming, expensive and challenging. To solve these problems, machine learning-based methods are introduced, which are restricted by effective feature extraction and negative sampling. In this work, features wit...
Scab, frogeye spot, and cedar rust are three common types of apple leaf diseases, and the rapid diagnosis and accurate identification of them play an important role in the development of apple production. In this work, an improved model based on VGG16 is proposed to identify apple leaf diseases, in which the global average poling layer is used to r...
Fog is the main weather phenomenon that causes low visibility, which makes traffic and outdoor work extremely dangerous. In this paper, we propose a novel LSTM framework for short-term fog forecasting. The proposed network framework consists of an LSTM network and fully connected layer. In order to make the proposed LSTM framework work, the meteoro...
Pathogenicity-related studies are of great importance in understanding the pathogenesis of complex diseases and improving the level of clinical medicine. This work proposed a bioinformatics scheme to analyze cancer-related gene mutations, and try to figure out potential genes associated with diseases from the protein domain-domain interaction netwo...
The study of protein-protein interaction is of great biological significance, and the prediction of protein-protein interaction sites can promote the understanding of cell biological activity and will be helpful for drug development. However, uneven distribution between interaction and non-interaction sites is common because only a small number of...
Research on quantitative structure-activity relationships (QSAR) provides an effective approach to accurately determine new hits and promising lead compounds during drug discovery. In the past decades, various works have gained good performance for QSAR with the development of machine learning. The rise of deep learning, along with massive accessib...
Background
Hotspots are those residues that contribute major free energy of binding in protein-protein interactions. Protein functions are frequently dependent on hotspot residues. At present, hotspot residues are always identified by Alanine scanning mutagenesis technology, which is costly, time-consuming and laborious.
Objective
Therefore, more...
Background:
The occurrence of cotton pests and diseases has always been an important factor affecting the total cotton production. Cotton has a great dependence on environmental factors during its growth, especially climate change. In recent years, machine learning and especially deep learning methods have been widely used in many fields and have...
Background:
The recognition of protein interaction sites is of great significance in many biological processes, signaling pathways and drug designs. However, most sites on protein sequences cannot be defined as interface or non-interface sites because only a small part of protein interactions had been identified, which will cause the lack of predi...
Background:
Accurate identification of potential interactions between drugs and protein targets is a critical step to accelerate drug discovery. Despite many relative experimental researches have been done in the past decades, detecting drug-target interactions (DTIs) remains to be extremely resource-intensive and time-consuming. Therefore, many c...
Biological targets are most commonly proteins such as enzymes, ion channels, and receptors. They are anything within a living organism to bind with some other entities (like an endogenous ligand or a drug), resulting in change in their behaviors or functions. Exploring potential drug-target interactions (DTIs) are crucial for drug discovery and eff...
Circadian rhythms exist in nearly all organisms. In mammals, transcriptional and translational feedback loops (TTFLs) are believed to underlie the mechanism of the circadian clock. Casein kinase 1δ/ε (CK1δ/ε) are key kinases that phosphorylate clock components such as PER proteins, determining the pace of the clock. Most previous studies of the bio...
Apple tree disease is a main threat factor to apple quality and yield. This paper proposed an improved convolutional neural network model to classify apple tree diseases. It took the advantages of neural network to extract the deep characteristics of disease parts, and used deep learning to classify target disease areas. In order to improve the cla...
Hearth activity is one of the most important factors which affect the smooth progress of production and even the life of blast furnace. However, the calculation of hearth activity depends on the empirical model entirely, and the model parameter acquisition is difficult. To overcome this deficiency, this paper presents a novel method based on an imp...
Accurate identification of apple leaf diseases is key to the prevention and control of insect pests and diseases in apple trees. This paper proposed an improved convolutional neural network combining batch normalization and center loss function based on VGG16 model. Batch normalization is used to normalize the input data of the convolutional layer,...
Particle urinary sediment analysis in microscopic images can help doctors assess patients with kidney and urinary tract disease. Manual urine sediment inspection is labor intensive, subjective and time consuming, and traditional automated algorithms often extract handcrafted identification features. In this paper, instead of using manual extraction...
Regarding the fierce competition between research institutions, institutional rankings are widely carried out. At present, t