Performance Comparison of 10 State-of-the-Art Machine Learning Algorithms for Outcome Prediction Modeling of Radiation-Induced Toxicity.

Purpose: To evaluate the efficacy of prominent machine learning algorithms in predicting normal tissue complication probability using clinical data obtained from 2 distinct disease sites and to create a software tool that facilitates the automatic determination of the optimal algorithm to model any given labeled data set.

Methods And Materials: We obtained 3 sets of radiation toxicity data (478 patients) from our clinic: gastrointestinal toxicity, radiation pneumonitis, and radiation esophagitis. These data comprised clinicopathological and dosimetric information for patients diagnosed with non-small cell lung cancer and anal squamous cell carcinoma.

Photosynthetic directed endosymbiosis to investigate the role of bioenergetics in chloroplast function and evolution.

Cyanobacterial photosynthesis (to produce ATP and NADPH) might have played a pivotal role in the endosymbiotic evolution to chloroplast. However, rather than meeting the ATP requirements of the host cell, the modern-day land plant chloroplasts are suggested to utilize photosynthesized ATP predominantly for carbon assimilation. This is further highlighted by the fact that the plastidic ADP/ATP carrier translocases from land plants preferentially import ATP.

A novel interpretable deep learning-based computational framework designed synthetic enhancers with broad cross-species activity.

Enhancers play a critical role in dynamically regulating spatial-temporal gene expression and establishing cell identity, underscoring the significance of designing them with specific properties for applications in biosynthetic engineering and gene therapy. Despite numerous high-throughput methods facilitating genome-wide enhancer identification, deciphering the sequence determinants of their activity remains challenging. Here, we present the DREAM (DNA cis-Regulatory Elements with controllable Activity design platforM) framework, a novel deep learning-based approach for synthetic enhancer design.