Internet Financial Credit Risk Assessment with Sliding Window and Attention Mechanism LSTM Model

With the accelerated pace of market-oriented reform, Internet finance has gained a broad and healthy development environment. Existing studies lack consideration of time trends in financial risk, and treating all features equally may lead to inaccurate predictions. To address the above problems, we propose an LSTM model based on sliding window and attention mechanism. The model uses sliding windows to enable the model to effectively exploit the contextual relevance of loan data. And we introduce the attention mechanism into the model, which enables the model to focus on important information. The result on the Lending Club public desensitization dataset shows that our model outperforms ARIMA, SVM, ANN, LSTM, and GRU models

Internet Financial Credit Risk Assessment with Sliding Window and Attention Mechanism LSTM Model

With the accelerated pace of market-oriented reform, Internet finance has gained a broad and healthy development environment. Existing studies lack consideration of time trends in financial risk, and treating all features equally may lead to inaccurate predictions. To address the above problems, we propose an LSTM model based on sliding window and attention mechanism. The model uses sliding windows to enable the model to effectively exploit the contextual relevance of loan data. And we introduce the attention mechanism into the model, which enables the model to focus on important information. The result on the Lending Club public desensitization dataset shows that our model outperforms ARIMA, SVM, ANN, LSTM, and GRU models

Oncogenic Mutations in Armadillo Repeats 5 and 6 of β-Catenin Reduce Binding to APC, Increasing Signaling and Transcription of Target Genes

Background & Aims: The β-catenin signaling pathway is one of the most commonly deregulated pathways in cancer cells. Amino acid substitutions within armadillo repeats 5 and 6 (K335, W383, and N387) of β-catenin are found in several tumor types, including liver tumors. We investigated the mechanisms by which these substitutions increase signaling and the effects on liver carcinogenesis in mice. Methods: Plasmids encoding tagged full-length β-catenin (CTNNB1) or β-catenin with the K335I or N387K substitutions, along with MET, were injected into tails of FVB/N mice. Tumor growth was monitored, and livers were collected and analyzed by histology, immunohistochemistry, and quantitative reverse-transcription polymerase chain reaction. Tagged full-length and mutant forms of β-catenin were expressed in HEK293, HCT116, and SNU449 cells, which were analyzed by immunoblots and immunoprecipitation. A panel of β-catenin variants and cell lines with knock-in mutations were analyzed for differences in N-terminal phosphorylation, half-life, and association with other proteins in the signaling pathway. Results: Mice injected with plasmids encoding K335I or N387K β-catenin and MET developed larger, more advanced tumors than mice injected with plasmids encoding WT β-catenin and MET. K335I and N387K β-catenin bound APC with lower affinity than WT β-catenin but still interacted with scaffold protein AXIN1 and in the nucleus with TCF7L2. This interaction resulted in increased transcription of genes regulated by β-catenin. Studies of protein structures supported the observed changes in relative binding affinities. Conclusion: Expression of β-catenin with mutations in armadillo repeats 5 and 6, along with MET, promotes formation of liver tumors in mice. In contrast to N-terminal mutations in β-catenin that directly impair its phosphorylation by GSK3 or binding to BTRC, the K335I or N387K substitutions increase signaling via reduced binding to APC. However, these mutant forms of β-catenin still interact with the TCF family of transcription factors in the nucleus. These findings show how these amino acid substitutions increase β-catenin signaling in cancer

Personalised treatment assignment maximising expected benefit with smooth hinge loss

In personalised medicine, the goal is to make a treatment recommendation for each patient with a given set of covariates to maximise the treatment benefit measured by patient's response to the treatment. In application, such a treatment assignment rule is constructed using a sample training data consisting of patients’ responses and covariates. Instead of modelling responses using treatments and covariates, an alternative approach is maximising a response-weighted target function whose value directly reflects the effectiveness of treatment assignments. Since the target function involves a loss function, efforts have been made recently on the choice of the loss function to ensure a computationally feasible and theoretically sound solution. We propose to use a smooth hinge loss function so that the target function is convex and differentiable, which possesses good asymptotic properties and numerical advantages. To further simplify the computation and interpretability, we focus on the rules that are linear functions of covariates and discuss their asymptotic properties. We also examine the performances of our method with simulation studies and real data analysis