Corrigendum to "Leveraging large language models to predict antibody biological activity against influenza A hemagglutinin" [Comput Struct Biotechnol J 27 (2025) 1286-1295].

[This corrects the article DOI: 10.1016/j.csbj.

Preoperative kidney tumor risk estimation with AI: From logistic regression to transformer.

We consider the problem of renal mass risk classification to support doctors in adjuvant treatment decisions following nephrectomy. Recommendation of adjuvant therapy based on the mass appearance poses two major challenges: first, morphologic patterns may sometimes overlap across subtypes of varying risks. Second, interobserver variability is large.

Leveraging large language models to predict antibody biological activity against influenza A hemagglutinin.

Monoclonal antibodies (mAbs) represent one of the most prevalent FDA-approved treatments for autoimmune, infectious, and cancer diseases. However, their discovery and development remains a time-consuming and costly process. Recent advancements in machine learning (ML) and artificial intelligence (AI) have shown significant promise in revolutionizing antibody discovery field.

Cutoff due to pointwise degradations in color images.

Many studies analyze resolution limits in single-channel, pan-chromatic systems. However, color imaging is popular. Thus, there is a need for its modeling in terms of resolving capacity under noise.