IUCrJ
Jul 1, 2023
This editorial acknowledges the transformative impact of new machine-learning methods, such as the use of AlphaFold, but also makes the case for the continuing need for experimental structural biology.
Computers in biology and medicine
Jun 30, 2023
Pathological examination is the optimal approach for diagnosing cancer, and with the advancement of digital imaging technologies, it has spurred the emergence of computational histopathology. The objective of computational histopathology is to assist...
ChemMedChem
Jun 30, 2023
Indoleamine 2,3-dioxygenase 1 (IDO1) is viewed as an extremely promising target for cancer immunotherapy. Here, we proposed a two-layer stacking ensemble model, IDO1Stack, that can efficiently predict IDO1 inhibitors. First, we constructed a series o...
IEEE transactions on pattern analysis and machine intelligence
Jun 30, 2023
Pairwise learning is receiving increasing attention since it covers many important machine learning tasks, e.g., metric learning, AUC maximization, and ranking. Investigating the generalization behavior of pairwise learning is thus of great significa...
IEEE transactions on pattern analysis and machine intelligence
Jun 30, 2023
The Area Under the ROC Curve (AUC) is a crucial metric for machine learning, which evaluates the average performance over all possible True Positive Rates (TPRs) and False Positive Rates (FPRs). Based on the knowledge that a skillful classifier shoul...
Journal of chemical information and modeling
Jun 30, 2023
One of the biggest obstacles to successful polymer property prediction is an effective representation that accurately captures the sequence of repeat units in a polymer. Motivated by the success of data augmentation in computer vision and natural lan...
Development (Cambridge, England)
Jun 30, 2023
Accurately counting and localising cellular events from movies is an important bottleneck of high-content tissue/embryo live imaging. Here, we propose a new methodology based on deep learning that allows automatic detection of cellular events and the...
IEEE transactions on medical imaging
Jun 30, 2023
The purpose of federated learning is to enable multiple clients to jointly train a machine learning model without sharing data. However, the existing methods for training an image segmentation model have been based on an unrealistic assumption that t...
IEEE transactions on medical imaging
Jun 30, 2023
Dataset auditing for machine learning (ML) models is a method to evaluate if a given dataset is used in training a model. In a Federated Learning setting where multiple institutions collaboratively train a model with their decentralized private datas...
IEEE transactions on medical imaging
Jun 30, 2023
Currently, data-driven based machine learning is considered one of the best choices in clinical pathology analysis, and its success is subject to the sufficiency of digitized slides, particularly those with deep annotations. Although centralized trai...