Toxicity testing in the 21st century: progress in the past decade and future perspectives
D Krewski, ME Andersen, MG Tyshenko… - Archives of …, 2020 - Springer
Advances in the biological sciences have led to an ongoing paradigm shift in toxicity testing
based on expanded application of high-throughput in vitro screening and in silico methods …
based on expanded application of high-throughput in vitro screening and in silico methods …
The pockets guide to HLA class I molecules
Human leukocyte antigens (HLA) are cell-surface proteins that present peptides to T cells.
These peptides are bound within the peptide binding cleft of HLA, and together as a …
These peptides are bound within the peptide binding cleft of HLA, and together as a …
Predicting HLA class II antigen presentation through integrated deep learning
Accurate prediction of antigen presentation by human leukocyte antigen (HLA) class II
molecules would be valuable for vaccine development and cancer immunotherapies …
molecules would be valuable for vaccine development and cancer immunotherapies …
AnOxPePred: using deep learning for the prediction of antioxidative properties of peptides
Dietary antioxidants are an important preservative in food and have been suggested to help
in disease prevention. With consumer demands for less synthetic and safer additives in food …
in disease prevention. With consumer demands for less synthetic and safer additives in food …
Review of machine learning and deep learning models for toxicity prediction
The ever-increasing number of chemicals has raised public concerns due to their adverse
effects on human health and the environment. To protect public health and the environment …
effects on human health and the environment. To protect public health and the environment …
Machine learning models for predicting cytotoxicity of nanomaterials
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 …
concerns about their potential adverse effects on human health. Thus, more and more …
Deep learning models for predicting gas adsorption capacity of nanomaterials
Metal–organic frameworks (MOFs), a class of porous nanomaterials, have been widely used
in gas adsorption-based applications due to their high porosities and chemical tunability. To …
in gas adsorption-based applications due to their high porosities and chemical tunability. To …
Machine learning in quantitative protein–peptide affinity prediction: implications for therapeutic peptide design
Background: Protein–peptide recognition plays an essential role in the orchestration and
regulation of cell signaling networks, which is estimated to be responsible for up to 40% of …
regulation of cell signaling networks, which is estimated to be responsible for up to 40% of …
Machine learning and deep learning for brain tumor MRI image segmentation
Brain tumors are often fatal. Therefore, accurate brain tumor image segmentation is critical
for the diagnosis, treatment, and monitoring of patients with these tumors. Magnetic …
for the diagnosis, treatment, and monitoring of patients with these tumors. Magnetic …
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 …
crop pest. It damages host plants through both phloem feeding and vectoring plant …