Tumor homing peptides (THPs) play significant role in recognizing and specifically binding to tumor cells. Traditional experimental methods can accurately identify THPs but often suffer from high measurement costs and long experimental cycles. In-silico methods can rapidly screen THPs and accelerate the THPs identification process. Existing THPs prediction methods primarily rely on constructing peptide sequence features and machine learning approaches. These tools rely on manual feature extraction, and the combination of features is crucial for both model performance and robustness. In this study, we propose a method called PLMTHP based on protein language model and integrate multiple machine learning models. Compared to existing models, PLMTHP improves predictions by incorporating high-dimensional features encoded through a protein language model. Our method achieved impressive performance on an independent test set, with ACC of 0.915, MCC of 0.831, and AUC of 0.964. PLMTHP can be downloaded from the following website: https://github.com/Chenyb939/PLMTHP

Human activity recognition through the wearable sensor will enable a next-generation human-oriented ubiquitous computing. However, most of research on human activity recognition so far is based on small number of subjects, and non-public data. To overcome the situation, we have gathered 4897 accelerometer data with 116 subjects and compose them as HASC2011corpus. In the field of pattern recognition, it is very important to evaluate and to improve the recognition methods by using the same dataset as a common ground. We make the HASC2011corpus into public for the research community to use it as a common ground of the Human Activity Recognition. We also show several facts and results of obtained from the corpus.