With the quick advancement in text generation ability of Large Language Mode(LLM), concerns about the misuse of machine-generated content have grown, raising potential violations of legal and ethical standards. Some existing studies concentrate on detecting machine-generated text in open-source models using in-model features, but their performance on closed-source large models is limited. This limitation occurs because, in the closed-source model detection, the only reference that can be obtained is the texts, which may differ significantly due to random sampling. In this paper, we demonstrate that texts generated by the same model can align both semantically and statistically under similar prompts, facilitating effective detection and traceability. Specifically, we fine-tune a BERT encoder through contrastive learning to achieve semantic alignment in randomly generated texts from the same model. Then, we propose a method called Machine-Generated Text Detection with Rewritten Texts, which designed several prompt refactoring methods and used them to request rewritten text from LLMs. Semantic and statistical relationships between rewritten and original texts provide a basis for detection and traceability. Finally, we expanded the text dataset with multi-parameter random sampling and verified the performance of MAGRET on three text-generated datasets. Experimental results show that previous methods struggle with closed-source model detection, while our approach significantly outperforms baseline methods in this regard. It also shows MagRet’s stable performance in detection and tracing tasks across various randomly sampled texts.

Topologically associating domains (TADs) are fundamental 3D genome structures that facilitate key gene regulatory interactions. The boundaries of TADs are rich in functional elements critical for maintaining structural integrity, making their identification essential for understanding the relationship between genome organization and gene expression. However, existing algorithms for identifying TAD boundaries often rely on fixed boundary sizes and neglect the varying predictive power of different features. To address these limitations, we introduce multiTAD, an advanced attention-based deep learning model that leverages 12 epigenetic signals to accurately detect TAD boundaries of diverse sizes. multiTAD significantly outperforms mainstream approaches, revealing distinct boundary size preferences across different cell lines. Additionally, multiTAD demonstrates strong cross-cell line predictive capabilities, further highlighting its broad applicability in genomic research.

MOTIVATION: Transcription factors are pivotal in the regulation of gene expression, and accurate identification of transcription factor binding sites (TFBSs) at high resolution is crucial for understanding the mechanisms underlying gene regulation. The task of identifying TFBSs from DNA sequences is a significant challenge in the field of computational biology today. To address this challenge, a variety of computational approaches have been developed. However, these methods face limitations in their ability to achieve high-resolution identification and often lack interpretability. RESULTS: We propose BertSNR, an interpretable deep learning framework for identifying TFBSs at single-nucleotide resolution. BertSNR integrates sequence-level and token-level information by multi-task learning based on pre-trained DNA language models. Benchmarking comparisons show that our BertSNR outperforms the existing state-of-the-art methods in TFBS predictions. Importantly, we enhanced the interpretability of the model through attentional weight visualization and motif analysis, and discovered the subtle relationship between attention weight and motif. Moreover, BertSNR effectively identifies TFBSs in promoter regions, facilitating the study of intricate gene regulation. AVAILABILITY AND IMPLEMENTATION: The BertSNR source code can be found at https://github.com/lhy0322/BertSNR.

Event-based Social Network (EBSN) is a special online social network that not only provides users with a convenient virtual platform to communicate, but also helps them to participate in offline face-to-face social activities on a group basis. This paper studies the group popularity prediction problem in EBSN which is important for online advertising and event recommendation. Firstly, we construct the EBSN temporal network model with a timeline involved and propose a novel definition of group popularity in EBSN. Then on the basis of the above, we extract corresponding key features from four aspects: inherent group characteristics (i.e., founder, created time), historical popularity, the sentiment of users towards events as an internal factor, and the initiative of newly added users as an external factor. Combining these four features, we propose a group popularity prediction algorithm in EBSN based on an improved self-excited Hawkes Process: EBSN-Hawkes Process Algorithm (EHP). Experiments conducted by real EBSN datasets demonstrate that the proposed EHP algorithm has better prediction results than other comparative algorithms. We also conclude that the external dynamic feature plays a more important role than the internal sentiment feature by the ablation study.