Machine learning models for predicting cytotoxicity of nanomaterials

Z Ji, W Guo, EL Wood, J Liu, S Sakkiah… - Chemical Research …, 2022 - ACS Publications
The wide application of nanomaterials in consumer and medical products has raised
concerns about their potential adverse effects on human health. Thus, more and more …

Molecular dynamics simulations and applications in computational toxicology and nanotoxicology

C Selvaraj, S Sakkiah, W Tong, H Hong - Food and Chemical Toxicology, 2018 - Elsevier
Nanotoxicology studies toxicity of nanomaterials and has been widely applied in biomedical
researches to explore toxicity of various biological systems. Investigating biological systems …

DeepHLApan: a deep learning approach for neoantigen prediction considering both HLA-peptide binding and immunogenicity

J Wu, W Wang, J Zhang, B Zhou, W Zhao, Z Su… - Frontiers in …, 2019 - frontiersin.org
Neoantigens play important roles in cancer immunotherapy. Current methods used for
neoantigen prediction focus on the binding between human leukocyte antigens (HLAs) and …

TSNAdb: a database for tumor-specific neoantigens from immunogenomics data analysis

J Wu, W Zhao, B Zhou, Z Su, X Gu… - Genomics …, 2018 - academic.oup.com
Tumor-specific neoantigens have attracted much attention since they can be used as
biomarkers to predict therapeutic effects of immune checkpoint blockade therapy and as …

Genome sequencing of the sweetpotato whitefly Bemisia tabaci MED/Q

W Xie, C Chen, Z Yang, L Guo, X Yang, D Wang… - …, 2017 - academic.oup.com
The sweetpotato whitefly Bemisia tabaci is a highly destructive agricultural and ornamental
crop pest. It damages host plants through both phloem feeding and vectoring plant …

HLA class I binding prediction via convolutional neural networks

YS Vang, X Xie - Bioinformatics, 2017 - academic.oup.com
Motivation Many biological processes are governed by protein–ligand interactions. One
such example is the recognition of self and non-self cells by the immune system. This …

PSSMHCpan: a novel PSSM-based software for predicting class I peptide-HLA binding affinity

G Liu, D Li, Z Li, S Qiu, W Li, C Chao, N Yang… - Giga …, 2017 - academic.oup.com
Predicting peptide binding affinity with human leukocyte antigen (HLA) is a crucial step in
developing powerful antitumor vaccine for cancer immunotherapy. Currently available …

DeepSeqPan, a novel deep convolutional neural network model for pan-specific class I HLA-peptide binding affinity prediction

Z Liu, Y Cui, Z Xiong, A Nasiri, A Zhang, J Hu - Scientific reports, 2019 - nature.com
Interactions between human leukocyte antigens (HLAs) and peptides play a critical role in
the human immune system. Accurate computational prediction of HLA-binding peptides can …

Network-based methods and their applications in drug discovery

Z Yu, Z Wu, Z Wang, Y Wang, M Zhou… - Journal of Chemical …, 2023 - ACS Publications
Drug discovery is time-consuming, expensive, and predominantly follows the “one drug→
one target→ one disease” paradigm. With the rapid development of systems biology and …

Experimental validation of immunogenic SARS-CoV-2 T cell epitopes identified by artificial intelligence

L Federico, B Malone, S Tennøe, V Chaban… - Frontiers in …, 2023 - frontiersin.org
During the COVID-19 pandemic we utilized an AI-driven T cell epitope prediction tool, the
NEC Immune Profiler (NIP) to scrutinize and predict regions of T cell immunogenicity …