A5067453693- Bacterial Identification and Susceptibility Testing
- Antimicrobial Peptides and Activities
- Antimicrobial Resistance in Staphylococcus
- Machine Learning in Bioinformatics
- Antibiotic Resistance in Bacteria
- Biochemical and Structural Characterization
- vaccines and immunoinformatics approaches
- Neural and Behavioral Psychology Studies
- RNA modifications and cancer
- Heart Rate Variability and Autonomic Control
- RNA and protein synthesis mechanisms
- Biosensors and Analytical Detection
- Genomics and Phylogenetic Studies
- Bioinformatics and Genomic Networks
- Streptococcal Infections and Treatments
- Biomedical Text Mining and Ontologies
- Antibiotic Use and Resistance
- Clostridium difficile and Clostridium perfringens research
- EEG and Brain-Computer Interfaces
- AI in cancer detection
- Machine Learning in Healthcare
- Cancer-related molecular mechanisms research
- Geochemistry and Geologic Mapping
- Metabolomics and Mass Spectrometry Studies
- Peptidase Inhibition and Analysis
National Central University
2019-2025
Chinese University of Hong Kong, Shenzhen
2022-2023
University of Science and Technology of China
2022-2023
The last 18 months, or more, have seen a profound shift in our global experience, with many of us navigating once-in-100-year pandemic. To date, COVID-19 remains life-threatening pandemic little to no targeted therapeutic recourse. discovery novel antiviral agents, such as vaccines and drugs, can provide solutions save human beings from severe infections; however, there is specifically effective treatment confirmed for now. Thus, great attention has been paid the use natural artificial...
In recent years, antimicrobial peptides (AMPs) have become an emerging area of focus when developing therapeutics hot spot residues proteins are dominant against infections. Importantly, AMPs produced by virtually all known living organisms and able to target a wide range pathogenic microorganisms, including viruses, parasites, bacteria fungi. Although several studies proposed different machine learning methods predict as being AMPs, most do not consider the diversity AMP activities. On this...
Abstract The emergence and spread of antibiotic‐resistant bacteria pose a significant public health threat, necessitating the exploration alternative antibacterial strategies. Antibacterial peptide (ABP) is kind antimicrobial (AMP) that has potential ability to fight against infection, offering promising avenue for developing novel therapeutic interventions. This study introduces AMPActiPred, three‐stage computational framework designed identify ABPs, characterize their activity diverse...
Abstract Antiviral peptides (AVPs) have shown potential in inhibiting viral attachment, preventing fusion with host cells and disrupting replication due to their unique action mechanisms. They now become a broad-spectrum, promising antiviral therapy. However, identifying effective AVPs is traditionally slow costly. This study proposed new two-stage computational framework for AVP identification. The first stage identifies from wide range of peptides, the second recognizes targeting specific...
Because of the rapid development multidrug resistance, conventional antibiotics cannot kill pathogenic bacteria efficiently. New antibiotic treatments such as antimicrobial peptides (AMPs) can provide a possible solution to antibiotic-resistance crisis. However, identification AMPs using experimental methods is expensive and time-consuming. Meanwhile, few studies use amino acid compositions (AACs) physicochemical properties with different sequence lengths against organisms predict AMPs....
Abstract Fungal infections have become a significant global health issue, affecting millions worldwide. Antifungal peptides (AFPs) emerged as promising alternative to conventional antifungal drugs due their low toxicity and propensity for inducing resistance. In this study, we developed deep learning‐based framework called DeepAFP efficiently identify AFPs. fully leverages mines composition information, evolutionary physicochemical properties of by employing combined kernels from multiple...
Cancer is one of the leading diseases threatening human life and health worldwide. Peptide-based therapies have attracted much attention in recent years. Therefore, precise prediction anticancer peptides (ACPs) crucial for discovering designing novel cancer treatments. In this study, we proposed a machine learning framework (GRDF) that incorporates deep graphical representation forest architecture identifying ACPs. Specifically, GRDF extracts features based on physicochemical properties...
Abstract Cancer is a severe illness that significantly threatens human life and health. Anticancer peptides (ACPs) represent promising therapeutic strategy for combating cancer. In silico methods enable rapid accurate identification of ACPs without extensive material resources. This study proposes two-stage computational framework called ACP-CapsPred, which can accurately identify characterize their functional activities across different cancer types. ACP-CapsPred integrates protein language...
Recent studies have demonstrated that the matrix-assisted laser desorption ionization-time of flight mass spectrometry (MALDI-TOF MS) could be used to detect superbugs, such as methicillin-resistant Staphylococcus aureus (MRSA). Due an increasingly clinical need classify between MRSA and methicillin-sensitive (MSSA) efficiently effectively, we were motivated develop a systematic pipeline based on large-scale dataset MS spectra. However, shifting problem peaks in spectra induced low...
Alzheimer's disease (AD) is a neurodegenerative disorder. Though it not yet curable or reversible, research has shown that clinical intervention intensive cognitive training at an early stage may effectively delay the progress of disease. As result, screening populations with mild impairment (MCI) AD via efficient, effective and low-cost assessments important. Currently, assessment relies mostly on tests, such as Mini-Mental State Examination (MMSE) Montreal Cognitive Assessment (MoCA),...
Methamphetamine abuse is getting worse amongst the younger population. While there methadone or buprenorphine harm-reduction treatment for heroin addicts, no drug addicts with methamphetamine use disorder (MUD). Recently, non-medication treatment, such as cue-elicited craving method integrated biofeedback, has been widely used. Further, virtual reality (VR) proposed to simulate an immersive environment in therapy. In this study, we developed a VR system equipped flavor simulation purpose of...
Matrix-assisted laser desorption ionization time-of-flight (MALDI-TOF) mass spectrometry (MS) has recently become a useful analytical approach for microbial identification. The presence and absence of specific peaks on MS spectra are commonly used to identify the bacterial species predict antibiotic-resistant strains. However, conventional using few single would result in insufficient prediction power without complete information whole spectra. In past years, machine learning algorithms have...
Drug abuse has always been a severe issue, but the proportion of drug and addiction is rising. According to research reports, youth are motivated access drugs mainly due curiosity peer influence. Additionally, especially lack proper knowledge education surrounding abuse. Analyzing whether potential addicts intend helpful in preventing addiction. We developed an Anti-drug Chatbot for young people on popular online social platform. can detect risks, obtain warnings from user-entered query...
Inflammation is a biological response to harmful stimuli, aiding in the maintenance of tissue homeostasis. However, excessive or persistent inflammation can precipitate myriad pathological conditions. Although current treatments such as NSAIDs, corticosteroids, and immunosuppressants are effective, they have side effects resistance issues. In this backdrop, anti-inflammatory peptides (AIPs) emerged promising therapeutic approach against inflammation. Leveraging machine learning methods, we...
One of the major challenges in cancer therapy lies limited targeting specificity exhibited by existing anti-cancer drugs. Tumor-homing peptides (THPs) have emerged as a promising solution to this issue, due their capability specifically bind and accumulate tumor tissues while minimally impacting healthy tissues. THPs are short oligopeptides that offer superior biological safety profile, with minimal antigenicity, faster incorporation rates into target cells/tissues. However, identifying...
Group B streptococcus (GBS) is an important pathogen that responsible for invasive infections, including sepsis and meningitis. GBS serotyping essential means the investigation of possible infection outbreaks can identify sources infection. Although it to determine serotypes by either immuno-serotyping or geno-serotyping, both traditional methods are time-consuming labor-intensive. In recent years, matrix-assisted laser desorption ionization-time flight mass spectrometry (MALDI-TOF MS) has...
Attention-deficit/Hyperactivity disorder(ADHD) is a common neurodevelopmental disorder among children. Traditional assessment methods generally rely on behavioral rating scales (BRS) performed by clinicians, and sometimes parents or teachers. However, BRS time consuming, the subjective ratings may lead to bias for evaluation. Therefore, major purpose of this study was develop Virtual Reality (VR) classroom associated with an intelligent model assist clinicians diagnosis ADHD. In study,...
Based on a large amount of matrix-assisted laser desorption ionization–time-of-flight mass spectrometry (MALDI-TOF MS) clinical data, comprising 37,918 Escherichia coli isolates, data-driven two-stage framework was established to evaluate the antimicrobial resistance E. . Five antibiotics, including amoxicillin (AMC), ceftazidime (CAZ), ciprofloxacin (CIP), ceftriaxone (CRO), and cefuroxime (CXM), were considered for model training, values area under receiver operating characteristic curve...
Cervical cancer, a major health concern among women worldwide, is closely linked to human papillomavirus (HPV) infection. This study explores the evolving landscape of HPV molecular epidemiology in Taiwan over decade (2010-2020), where prophylactic vaccination has been implemented since 2007. Analyzing data from 40,561 vaginal swab samples, with 42.0% testing positive for HPV, we reveal shifting trends genotype distribution and infection patterns. The 12 high-risk genotypes, order decreasing...
Tuberculosis (TB) is a severe disease caused by Mycobacterium tuberculosis that poses significant threat to human health. The emergence of drug-resistant strains has made the global fight against TB even more challenging. Antituberculosis peptides (ATPs) have shown promising results as potential treatment for TB. However, conventional wet lab-based approaches ATP discovery are time-consuming and costly often fail discover with desired properties. To address these challenges, we propose novel...
Enhancers are a class of noncoding DNA, serving as crucial regulatory elements in governing gene expression by binding to transcription factors. The identification enhancers holds paramount importance the field biology. However, traditional experimental methods for enhancer demand substantial human and material resources. Consequently, there is growing interest employing computational prediction. In this study, we propose two-stage framework based on deep learning, termed CapsEnhancer, their...
Staphylococcus haemolyticus is one of the most significant coagulase-negative staphylococci, and it often causes severe infections. Rapid strain typing pathogenic S. indispensable in modern public health infectious disease control, facilitating identification origin infections to prevent further outbreak. enables effective control infections, which tremendously beneficial critically ill patients. However, existing methods, such as multi-locus sequencing, are relatively high cost...
A modified binning method was incorporated to cluster MS shifting ions into a set of representative peaks based on large-scale data clinical VRE fm and VSE isolates, including 2,795 2,922 isolates. Predictions with the algorithm were significantly more accurate than empirical antibiotic use, accuracy which 0.50, local epidemiology.
Early diagnosis and treatment can reduce the symptoms of Attention Deficit/Hyperactivity Disorder (ADHD) in children, but medical is usually delayed. Hence, it important to increase efficiency early diagnosis. Previous studies used behavioral neuronal data during GO/NOGO task help detect ADHD accuracy differed considerably from 53% 92%, depending on employed methods number electroencephalogram (EEG) channels. It remains unclear whether a few EEG channels still lead good detecting ADHD. Here,...