MOTIVATION: Transcription factors (TFs) and their target genes form regulatory networks that control gene expression and influence diverse biological processes and disease outcomes. Although multiple computational methods and curated databases have been developed to identify TF-target interactions, they often require specialized expertise. Large language models (LLMs) chatbots offer a more accessible alternative for querying TF-target interactions. In this study, we benchmarked four prominent LLMs, Anthropic's Claude 3.5 Sonnet, Google's Gemini 1.0 Pro, OpenAI's GPT-4o, and Meta's Llama3 8b, using 8432 literature-curated human TF-target interactions. We examined four regulatory categories: bidirectional, ambiguous, self-regulated, and unidirectional interactions. RESULTS: Under single-turn queries, Claude 3.5 Sonnet and GPT-4o outperformed the others, with balanced accuracies reaching 50.0 ± 7.6% (GPT-4o, self-regulated) and 48.2 ± 1.0% (Claude 3.5 Sonnet, unidirectional). Zero-temperature settings generally enhanced reproducibility, and multi-turn prompting improved performance for most models, increasing Claude 3.5 Sonnet's accuracy on self-regulated pairs by 32.6%. Excluding TF-target pairs with all unknown regulation types also generally improved accuracy, with unidirectional regulation reaching near 70% balanced accuracy in some cases. We also benchmarked Anthropic's Claude 3.5 Sonnet, Google's Gemini 2.0 Flash, OpenAI's GPT-4o, and Meta's Llama3 using 5148 experimentally derived TF-target interactions. Claude 3.5 Sonnet consistently outperformed the other models across conditions. Our findings highlight that prompt engineering and strategic use of model parameters consistently influence LLM chatbots' performance on TF-target identifications. This study establishes a benchmarking framework and demonstrates the potential of pre-trained general-purpose LLMs to support regulatory biology research, especially for researchers without extensive computational expertise. AVAILABILITY AND IMPLEMENTATION: The literature-based TF-target interactions ground truth were obtained from TRRUST v2 human dataset (www.grnpedia.org/trrust). The experimental derived TF-target interactions ground truth were obtained from TFLink Home Sapiens small-scale interaction table (https://tflink.net/). Processed TF-target interactions data and the analytical pipeline has been compiled as an interactive Python notebook file and is available at https://github.com/pengpclab/LLM-TF-interactions.

The reliable operation of industrial systems requires not only the prompt detection of faults but also their accurate classification into the appropriate categories. At present, numerous data-driven industrial fault detection and diagnosis models, which have been developed based on historical fault data, frequently neglect the issue of label noise. When labels are corrupted by noise, a significant degradation in the performance of industrial fault detection models can be observed. In this article, alabel-noise-resistanttime-seriesclassification (LNRTSC) method based on consistency-driven label correction is proposed. First, an attention-based temporal correlation-enhanced encoder is introduced to extract low-dimensional representations of industrial time series. Then, label confidence, which is assessed based on local label consistency, is utilized to correct noisy labels during training. In addition, a two-stage self-supervised enhancement strategy is designed to guarantee the reliability of the corrected labels. Specifically, a reconstruction loss term is introduced to assist feature extraction in the warming-up stage, and a newly designed contrastive loss term is added to the loss function for the LNL training stage, which mitigates the effect of false negatives. Finally, the effectiveness of the LNRTSC method is validated on the Tennessee Eastman process and the SEU-gearbox datasets. When compared to peer methods, the LNRTSC approach demonstrates substantial improvements in fault classification performance on corrupted data.

Probabilistic latent variable models (PLVMs), such as probabilistic principal component analysis (PPCA), are widely employed in process monitoring and fault detection of industrial processes. This article proposes a novel deep PPCA (DePPCA) model, which has the advantages of both probabilistic modeling and deep learning. The construction of DePPCA includes a greedy layer-wise pretraining phase and a unified end-to-end fine-tuning phase. The former establishes a hierarchical deep structure based on cascading multiple layers of the PPCA module to extract high-level features. The latter builds an end-to-end connection between the raw inputs and the final outputs to further improve the representation of the model to high-level features. After constructing the model structure of DePPCA, we first present the detailed training processes of the pretraining and fine-tuning stages, then clarify the theoretical merits of the proposed model from the perspective of variational inference. For process monitoring purposes, we develop two statistics based on the established DePPCA. The monitoring performance of these two statistics can remain superior even if the features extracted by DePPCA are significantly compressed to univariate. This makes the feature extraction process and online monitoring procedure of DePPCA quite fast. In other words, the proposed DePPCA can achieve accurate and efficient process monitoring by only extracting one feature for each sample. Finally, the effectiveness of DePPCA is evaluated on the Tennessee Eastman (TE) process and the multiphase flow (MPF) facility.

Due to the complex reaction mechanisms of industrial process units, causality and correlations exist between industrial process variables. Causal discovery algorithms have been utilized to discover the knowledge on variable relationships and guide process modeling and control optimization. However, most of them are limited by strict assumptions, such as linear relationships, additive noise, steady-state process, etc. Therefore, these methods cannot gain good performance for most practical industrial processes. To solve these problems, a novel weight comparison causal mining (WCCM) algorithm is proposed in this article for industrial causal graph discovery. It first trains a group of hidden layer neural networks with process data, then mines an undirected skeleton of the process variables according to the comparison of the network weights, and further determines the causal directions of the undirected edges in the skeleton to get a directed causal graph. The effectiveness of WCCM is verified on a benchmark and a practical industrial case from the urea synthesis process. The undirected and direct edges mined by WCCM show high consistency with the ground truths. Moreover, the causal discovery results of WCCM are utilized to guide the feature selection of soft sensor modeling, resulting in improved prediction accuracy and enhanced model interpretability.