Biology subjects

Lu, I.-H.

Publications and source records attributed to Lu, I.-H..

3 recordsLinked to original sources

bioRxiv2022

Peptide-based drug predictions for cancer therapy using deep learning

BackgroundTherapeutic drugs used in cancer treatment have ineffectiveness and resistance to drug action problems. Anticancer peptides (ACPs) are selective and toxic to cancer cells and quickly produced. Thus, ACPs can be a satisfactory substitute for therapeutic drugs. We developed AI4ACP, a user-friendly web-server ACP predictor that can predict the anticancer property of query peptides, thus promoting the discovery of peptides with anticancer activity. ResultOur results revealed that the performance of our ACP predictor trained using the new ACP collection was superior to that of the available high-performance ACP predictors. ConclusionsAI4ACP is a user-friendly web-server ACP predictor that can be used to determine whether a query sequence is an ACP. This tool can be beneficial for drug development for cancer treatment. AI4ACP is freely accessible at https://axp.iis.sinica.edu.tw/AI4ACP/

cancer biology↗

bioRxiv2021

Developing an Antiviral Peptides Predictor with Generative Adversarial Network Data Augmentation

MotivationNew antiviral drugs are urgently needed because of emerging viral pathogens increasing severity and drug resistance. Antiviral peptides (AVPs) have multiple antiviral properties and are appealing candidates for antiviral drug development. We developed a sequence-based binary classifier to identify whether an unknown short peptide has AVP activity. We collected AVP sequence data from six existing databases. We used a generative adversarial network to augment the number of AVPs in the positive training dataset and allow our deep convolutional neural network model to train on more data. ResultsOur classifier achieved outstanding performance on the testing dataset compared with other state-of-the-art classifiers. We deployed our trained classifier on a user-friendly web server. Availability and implementationAI4AVP is freely accessible at http://axp.iis.sinica.edu.tw/AI4AVP/ Contactcylin@iis.sinica.edu.tw Supplementary informationSupplementary data is also available.

microbiology↗

bioRxiv2020

AI4AMP: Sequence-based antimicrobial peptides predictor using physicochemical properties-based encoding method and deep learning

MotivationAntimicrobial peptides (AMPs) are innate immune components that have aroused a great deal of interest among drug developers recently, as they may become a substitution for antibiotics. However, AMPs discovery through traditional wet-lab research is expensive and inefficient. Thus, we developed AI4AMP, a user-friendly web-server that provides an accurate prediction of the antimicrobial activity of a given protein sequence, to accelerate the process of AMP discovery. ResultsOur results show that our prediction model is superior to the existing AMP predictors. AvailabilityAI4AMP is freely accessible at http://symbiosis.iis.sinica.edu.tw/PC_6/ Contactcylin@iis.sinica.edu.tw

microbiology↗