Buchao Zhan

Objective: The role of fungal microbiota in human carcinogenesis remains largely uncharacterized. Recent evidence suggests normal adjacent tissue (NAT) represents an intermediate state between healthy and malignant tissues, highlighting its potential for early cancer detection. Discriminating fungal compositional profiles between tumor and NAT is thus critical for elucidating fungal involvement in oncogenesis. However, the high similarity between tumor and NAT mycobiota poses significant analytical challenges. Method: To overcome this limitation, we developed a two-level ensemble discriminative model. Base-level classifiers, trained using rigorously filtered fungal microbiota data (based on prevalence, abundance, and quality metrics) via Random Forest, generate initial predictions. A meta-level classifier then integrates these base predictions, transforming high-dimensional, sparse fungal feature data into a low-dimensional, dense representation optimized for discrimination. Model implementation and analysis code are available at: https://github.com/cskyan/FNatPred. Results: Our approach achieved clear separation between tumor and NAT mycobiomes across multiple cancer types, with particularly pronounced discrimination in colorectal cancer (CRC). The proposed model significantly outperformed existing methods in tumor-NAT classification, demonstrating an average AUC improvement of approximately 10%.

The exponentially growing content on the Internet includes online publications, scientific news, and other expert websites. It brings a formidable challenge to extracting pertinent answers from such vast information. We proposed a deep neural network model based on BiLSTM(Bi-directional Long Short-Term Memory) and ALBERT(A Lite BERT for Self-Supervised Learning of Language Representations) for Chinese question-answering in the scientific context. Our model significantly enhances the generalization capabilities of the transformer-based model for question answering. The character extraction and word embedding modules are designed for tackling intricate science-related queries, swiftly assimilating knowledge from cutting-edge scientific literature, and contributing to constructing a comprehensive scientific knowledge graph. Our model is characterized by compactness, swift execution, and satisfactory accuracy. It has been meticulously fine-tuned, emphasizing multi-sentence coherence augmented by attention mechanisms, thereby ensuring robust scalability. Empirical evaluations on the LCQMC and XNLI datasets demonstrate that our approach surpasses the performance results of BERT and ALBERT, showing the potential for large-scale Chinese question-matching problems.