Prediction of protein biophysical traits from limited data: a case study on nanobody thermostability through NanoMelt.

In-silico prediction of protein biophysical traits is often hindered by the limited availability of experimental data and their heterogeneity. Training on limited data can lead to overfitting and poor generalizability to sequences distant from those in the training set. Additionally, inadequate use of scarce and disparate data can introduce biases during evaluation, leading to unreliable model performances being reported.

Epidemiology, treatment and outcomes of gastroenteropancreatic neuroendocrine neoplasms.

To investigate incidence, treatment patterns and outcomes of gastroenteropancreatic neuroendocrine neoplasms (GEP-NEN) in the United States. The 2019 National Cancer Database was searched for adult GEP-NEN patients. Main outcomes included overall and site-specific incidence, treatment patterns, and overall survival (OS).

Impact of sex differences on patients with neuroendocrine neoplasms during hospital admission.

Background: Sex disparities are known modifiers of health and disease. In neuroendocrine neoplasms (NENs), sex-based differences have been observed in the epidemiology and treatment-related side effects.

Objectives: To examine sex differences in demographics, diagnoses present during hospital admission, comorbidities, and outcomes of hospital course among hospitalized patients with NENs.

Prediction of Perceived Exertion Ratings in National Level Soccer Players Using Wearable Sensor Data and Machine Learning Techniques.

This study aimed to identify relationships between external and internal load parameters with subjective ratings of perceived exertion (RPE). Consecutively, these relationships shall be used to evaluate different machine learning models and design a deep learning architecture to predict RPE in highly trained/national level soccer players. From a dataset comprising 5402 training sessions and 732 match observations, we gathered data on 174 distinct parameters, encompassing heart rate, GPS, accelerometer data and RPE (Borg's 0-10 scale) of 26 professional male professional soccer players.