MOTIVATION: Accurate in silico identification of B-cell epitope residues is crucial for antibody design and structure-guided vaccine development. Although recent protein language models and structure-aware methods can capture spatial information of tertiary structure when generating residue embeddings, most existing epitope predictors use these embeddings to perform classification for individual residues one by one, without enforcing spatial continuity for reported epitope residues. Such methods often result in biologically implausible predictions because B-cell epitope residues always cluster together on the antigen surface. RESULTS: We present RoBep, a region-oriented B-cell epitope predictor that explicitly models the spatial clustering of epitope residues. RoBep introduces a novel region constraint mechanism and combines the advanced protein language model ESM-Cambrian with an equivariant graph neural network. Our method outperforms existing structure-based methods on the benchmark dataset, demonstrating improvements of 26%, 45%, 13%, and 43% in F1, Matthews correlation coefficient, area under the precision-recall curve, and AUROC0.1, respectively. In addition to residue-level predictions, RoBep can also provide antibody-antigen binding regions. Importantly, the predicted epitope residues are ensured to be spatially compact, enhancing biological plausibility and practical relevance for immunotherapeutic design. AVAILABILITY AND IMPLEMENTATION: A user-friendly website for using RoBep is provided at https://huggingface.co/spaces/NielTT/RoBep. All datasets, source code used in this work, and implementation instructions of the website are publicly available at https://github.com/YitaoXU/RoBep.

Structural variations (SVs) are commonly found in cancer genomes. They can cause gene amplification, deletion and fusion, among other functional consequences. With an average read length of hundreds of kilobases, nano-channel-based optical DNA mapping is powerful in detecting large SVs. However, existing SV calling methods are not tailored for cancer samples, which have special properties such as mixed cell types and sub-clones. Here we propose the Cancer Optical Mapping for detecting Structural Variations (COMSV) method that is specifically designed for cancer samples. It shows high sensitivity and specificity in benchmark comparisons. Applying to cancer cell lines and patient samples, COMSV identifies hundreds of novel SVs per sample.

In recommender systems, the cold start problem, especially the user cold start one, is quite challenging yet important as it mostly comes with the rapid growth of the product and business. A well-performed new or early user recommender system has the potential to not only offer great onboarding experience, but also largely convert these users into loyal ones with considerable long-term values, which is strategically important from a product perspective. From a machine learning perspective, it is not an easy task as modern recommender systems heavily rely on large amounts of user-item interaction data to train a high quality model, and the data sparsity problem that new and early users bring is a clear challenge in terms of model training quality. In this short survey, among many proposed ideas on this topic, we focused on two general approaches for user cold start recommender system problems: metadata and meta-learning. Metadata methods generally aim at calculating similarities and learning representations by well utilizing limited user data that is collected regardless of users’ ratings for recommended items. Meta-learning methods learn the model in a more general and efficient way that could be adapted to new tasks with decent performance given data sparsity, which aligns the user cold start problem settings well.

Causation is gradually paid more attention to in industry as compared with correlation statement, it straightly targets on answering what-if questions, which generally delivers deeper and more insightful conclusions. Therefore, causal inference is naturally called. Mainly targeting on modeling counter-factual relationship that is usually not directly observable, causal inference has various of challenges on both problem setup and modeling side, which makes it a more complex topic than regular supervised learning task. As one of the heated discussed specific causal inference problems, conditional average treatment effect (CATE), or heterogeneous treatment effect (HTE), estimation model serves as a powerful tool in many applications, like personalized medicine and a series of uplift problems from user segmentation to ads budget optimization. Recently, several new CATE methods were proposed and we would like to do a short survey from the perspective of forest-based model to cover both meta-learners that could take random forest as base learner and forest-based specific CATE models. In total, we discussed 7 meta-learners and 5 forest-based specific models. We empirically evaluate these models with both synthetic data and real dataset.

As human-computer interaction increasingly focuses on mediated interactions among groups of individuals, there is a need to develop techniques for measurement and analysis of groups that have been scoped at the level of the group. Bandura's construct of perceived self-efficacy has been used to understand individual behavior as a function of domain-specific beliefs about personal capacities. The construct of collective efficacy extends self-efficacy to organizations and groups, referring to beliefs about collective capacities in specific domains. We describe the development and refinement of a collective efficacy scale, the factor analysis of the construct, and its external validation in path models of community-oriented attitudes, beliefs, and behaviors.