Integrating Protein Sequence and Expression Level to Analysis Molecular Characterization of Breast Cancer Subtypes

Breast cancer's complexity and variability pose significant challenges in understanding its progression and guiding effective treatment. This study aims to integrate protein sequence data with expression levels to improve the molecular characterization of breast cancer subtypes and predict clinical outcomes. Using ProtGPT2, a language model designed for protein sequences, we generated embeddings that capture the functional and structural properties of proteins sequence. These embeddings were integrated with protein expression level to form enriched biological representations, which were analyzed using machine learning methods like ensemble K-means for clustering and XGBoost for classification. Our approach enabled successful clustering of patients into biologically distinct groups and accurately predicted clinical outcomes such as survival and biomarkers status, achieving high performance metrics, notably an F1 score of 0.88 for survival and 0.87 for biomarkers status prediction. Analysis of feature importance highlighted key proteins like KMT2C, GCN1, and CLASP2, linked to hormone receptor and Human Epidermal Growth Factor Receptor 2 (HER2) expression, which play a role in tumor progression and patient outcomes, respectively. Furthermore, protein-protein interaction networks and correlation analyses revealed the interdependence of proteins that may influence breast cancer subtype behaviors. These findings suggest that integrating protein sequence and expression data provides valuable insights into tumor biology and has significant potential to enhance personalized treatment strategies in breast cancer care.

Knowledge in Triples for LLMs: Enhancing Table QA Accuracy with Semantic Extraction

Integrating structured knowledge from tabular formats poses significant challenges within natural language processing (NLP), mainly when dealing with complex, semi-structured tables like those found in the FeTaQA dataset. These tables require advanced methods to interpret and generate meaningful responses accurately. Traditional approaches, such as SQL and SPARQL, often fail to fully capture the semantics of such data, especially in the presence of irregular table structures like web tables. This paper addresses these challenges by proposing a novel approach that extracts triples straightforward from tabular data and integrates it with a retrieval-augmented generation (RAG) model to enhance the accuracy, coherence, and contextual richness of responses generated by a fine-tuned GPT-3.5-turbo-0125 model. Our approach significantly outperforms existing baselines on the FeTaQA dataset, particularly excelling in Sacre-BLEU and ROUGE metrics. It effectively generates contextually accurate and detailed long-form answers from tables, showcasing its strength in complex data interpretation.