Semantic Invariant Multi-View Clustering With Fully Incomplete Information.

Robust multi-view learning with incomplete information has received significant attention due to issues such as incomplete correspondences and incomplete instances that commonly affect real-world multi-view applications. Existing approaches heavily rely on paired samples to realign or impute defective ones, but such preconditions cannot always be satisfied in practice due to the complexity of data collection and transmission. To address this problem, we present a novel framework called SeMantic Invariance LEarning (SMILE) for multi-view clustering with incomplete information that does not require any paired samples. To be specific, we discover the existence of invariant semantic distribution across different views, which enables SMILE to alleviate the cross-view discrepancy to learn consensus semantics without requiring any paired samples. The resulting consensus semantics remains unaffected by cross-view distribution shifts, making them useful for realigning/imputing defective instances and forming clusters. We demonstrate the effectiveness of SMILE through extensive comparison experiments with 13 state-of-the-art baselines on five benchmarks. Our approach improves the clustering accuracy of NoisyMNIST from 19.3%/23.2% to 82.7%/69.0% when the correspondences/instances are fully incomplete. We will release the code after acceptance.

Link prediction in drug-target interactions network using similarity indices.

BACKGROUND: In silico drug-target interaction (DTI) prediction plays an integral role in drug repositioning: the discovery of new uses for existing drugs. One popular method of drug repositioning is network-based DTI prediction, which uses complex network theory to predict DTIs from a drug-target network. Currently, most network-based DTI prediction is based on machine learning - methods such as Restricted Boltzmann Machines (RBM) or Support Vector Machines (SVM). These methods require additional information about the characteristics of drugs, targets and DTIs, such as chemical structure, genome sequence, binding types, causes of interactions, etc., and do not perform satisfactorily when such information is unavailable. We propose a new, alternative method for DTI prediction that makes use of only network topology information attempting to solve this problem. RESULTS: We compare our method for DTI prediction against the well-known RBM approach. We show that when applied to the MATADOR database, our approach based on node neighborhoods yield higher precision for high-ranking predictions than RBM when no information regarding DTI types is available. CONCLUSION: This demonstrates that approaches purely based on network topology provide a more suitable approach to DTI prediction in the many real-life situations where little or no prior knowledge is available about the characteristics of drugs, targets, or their interactions.

Up-regulated miR-199a-5p in gastric cancer functions as an oncogene and targets klotho.

BACKGROUND: Recent studies have shown that miR-199a-5p plays opposite roles in cancer initiation and progression of different cancer types, acting as oncogene for some cancer types but as tumor suppressor gene for others. However, the role and molecular mechanism of miR-199a-5p in gastric cancer are largely unknown. METHODS: In this study, miR-199a-5p expression level in gastric cancer was first analyzed by qPCRand then validated in 103 gastric cancer patients by in situ hybridization (ISH). Gastric cancer cell lines were transfected with miR-199a-5p inhibitor and mimic, and underwent in vitro transwell assays. Target genes (klotho) were identified using Luciferase reporter assay. Immunohistochemical staining was also used to investigate on how miR-199a-5p regulates the tumour-suppressive effects of klotho in gastric cancer. RESULTS: In our present study, we found that miR-199a-5p level was significantly increased in gastric cancer tissues compared to paired normal tissues. We observed that miR-199a-5p could promote migration and invasion of gastric cancer cells. In situ hybridization of miR-199a-5p also confirmed that higher miR-199a-5p expression level was associated with increased likelihood of lymph node metastasis and later TNM stage. Luciferase reporter assay and immunohistochemistry revealed that klotho might be the downstream target of miR-199a-5p. CONCLUSIONS: Our present study suggests that miR-199a-5p acts as an oncogene in gastric cancer and functions by targeting klotho.