A Question-Answering Approach to Key Value Pair Extraction from Form-Like Document Images

In this paper, we present a new question-answering (QA) based key-value pair extraction approach, called KVPFormer, to robustly extracting key-value relationships between entities from form-like document images. Specifically, KVPFormer first identifies key entities from all entities in an image with a Transformer encoder, then takes these key entities as questions and feeds them into a Transformer decoder to predict their corresponding answers (i.e., value entities) in parallel. To achieve higher answer prediction accuracy, we propose a coarse-to-fine answer prediction approach further, which first extracts multiple answer candidates for each identified question in the coarse stage and then selects the most likely one among these candidates in the fine stage. In this way, the learning difficulty of answer prediction can be effectively reduced so that the prediction accuracy can be improved. Moreover, we introduce a spatial compatibility attention bias into the self-attention/cross-attention mechanism for KVPFormer to better model the spatial interactions between entities. With these new techniques, our proposed KVPFormer achieves state-of-the-art results on FUNSD and XFUND datasets, outperforming the previous best-performing method by 7.2% and 13.2% in F1 score, respectively

A prognostic signature consisting of N6-methyladenosine modified mRNAs demonstrates clinical potential in prediction of biochemical recurrence and guidance on precision therapy in prostate cancer

Novel biomarkers are urgently needed to improve the prediction of clinical outcomes and guide personalized treatment for prostate cancer (PCa) patients. However, the role of N6-methyladenosine (m6A) modifications in PCa initiation and progression remains largely elusive. In our study, we collected benign Prostate Hyperplasia (BPH), localized PCa, and metastatic PCa samples from patients and performed methylated RNA immunoprecipitation sequencing (MeRIP-Seq) to map m6A-methylated mRNAs. Furthermore, we developed a prognostic signature based on 239 differentially methylated RNAs and the TCGA-PRAD dataset, which can be used to calculate an m6A-modified mRNA (MMM) score for a PCa patient, validated by independent multi-center cohorts. Our findings revealed that differential m6A modifications were positively correlated with altered expressions of mapped m6A-modified mRNAs. Higher MMM scores were associated with shorter times to biochemical recurrence (BCR) in PCa patients, and the MMM scoring system outperformed three well-established signatures in nine independent validation cohorts, as demonstrated by Kaplan-Meier survival analysis, C-index and ROC. Patients who did not respond to androgen receptor signaling inhibitor (ARSI) therapy and immunotherapy were found to have high MMM scores. Two hub genes, TLE1 and PFKL, were confirmed to have m6A sites through MeRIP-qPCR, and their knockdown promoted PCa cell invasion. Bioinformatics analysis of single-cell databases identified cell types with high transcript abundance levels of these two genes. In summary, our study is the first to perform transcriptome-wide m6A mapping in prostate tissues. The translational potential of a prognostic signature, comprising m6A-methylated mRNAs, in predicting clinical outcomes and therapy responses for PCa patients, is demonstrated