Search

Early detection and treatment of Alzheimer's Disease (AD) are significant. Recently, multi-modality imaging data have promoted the development of the automatic diagnosis of AD. This paper proposes a method based on latent feature fusion to make full use of multi-modality image data information. Specifically, we learn a specific projection matrix for each modality by introducing a binary label matrix and local geometry constraints and then project the original features of each modality into a low-dimensional target space. In this space, we fuse latent feature representations of different modalities for AD classification. The experimental results on Alzheimer's Disease Neuroimaging Initiative database demonstrate the proposed methods effectiveness in classifying AD.

Semi-supervised classification methods attempt to improve classification performance based on a small amount of labeled data through full use of abundant unlabeled data. Although existing semi-supervised classification methods have exhibited promising results in many applications, they still have drawbacks, including performance degeneration, due to the introduction of unlabeled data and partially false labels in a small amount of labeled data. To circumvent such drawbacks, a new semi-supervised classification method OCHS-SSC through oversampling and a common hidden space is proposed in the paper. The primary characteristics of the proposed method include two aspects. One is that unlabeled data are only used to generate new synthetic data to extend the minimal amount of labeled data. The other is that the final classifier is learned in the extended feature space, which is composed of the original feature space and the common hidden space found between labeled data and the synthetic data instead of the original feature space. Extensive experiments on 23 datasets indicate the effectiveness of the proposed method.

SummaryThe Hippo pathway has been implicated in suppressing tissue overgrowth and tumor formation by restricting the oncogenic activity of YAP. However, transcriptional regulators that inhibit YAP activity have not been well studied. Here, we uncover clinical importance for VGLL4 in gastric cancer suppression and find that VGLL4 directly competes with YAP for binding TEADs. Importantly, VGLL4’s tandem Tondu domains are not only essential but also sufficient for its inhibitory activity toward YAP. A peptide mimicking this function of VGLL4 potently suppressed tumor growth in vitro and in vivo. These findings suggest that disruption of YAP-TEADs interaction by a VGLL4-mimicking peptide may be a promising therapeutic strategy against YAP-driven human cancers.