Motivation
Sequence-based protein–protein interaction (PPI) prediction represents a fundamental computational biology problem. To address this problem, extensive research efforts have been made to extract predefined features from the sequences. Based on these features, statistical algorithms are learned to classify the PPIs. However, such explicit features are usually costly to extract, and typically have limited coverage on the PPI information.
Results
We present an end-to-end framework, PIPR (Protein–Protein Interaction Prediction Based on Siamese Residual RCNN), for PPI predictions using only the protein sequences. PIPR incorporates a deep residual recurrent convolutional neural network in the Siamese architecture, which leverages both robust local features and contextualized information, which are significant for capturing the mutual influence of proteins sequences. PIPR relieves the data pre-processing efforts that are required by other systems, and generalizes well to different application scenarios. Experimental evaluations show that PIPR outperforms various state-of-the-art systems on the binary PPI prediction problem. Moreover, it shows a promising performance on more challenging problems of interaction type prediction and binding affinity estimation, where existing approaches fall short.
Availability and implementation
The implementation is available at https://github.com/muhaochen/seq_ppi.git.
Supplementary information
Supplementary data are available at Bioinformatics online.
The transcription factor hypoxia inducible factor-1α (HIF-1α) mediates adaptive responses to oxidative stress by nuclear translocation and regulation of gene expression. Mitochondrial changes are critical for the adaptive response to oxidative stress. However, the transcriptional and non-transcriptional mechanisms by which HIF-1α regulates mitochondria in response to oxidative stress are poorly understood. Here, we examined the subcellular localization of HIF-1α in human cells and identified a small fraction of HIF-1α that translocated to the mitochondria after exposure to hypoxia or H2O2 treatment. Moreover, the livers of mice with CCl4-induced fibrosis showed a progressive increase in HIF-1α association with the mitochondria, indicating the clinical relevance of this finding. To probe the function of this HIF-1α population, we ectopically expressed a mitochondrial-targeted form of HIF-1α (mito-HIF-1α). Expression of mito-HIF-1α was sufficient to attenuate apoptosis induced by exposure to hypoxia or H2O2-induced oxidative stress. Moreover, mito-HIF-1α expression reduced the production of reactive oxygen species, the collapse of mitochondrial membrane potential, and the expression of mitochondrial DNA-encoded mRNA in response to hypoxia or H2O2 treatment independently of nuclear pathways. These data suggested that mitochondrial HIF-1α protects against oxidative stress induced-apoptosis independently of its well-known role as a transcription factor.
Embedding models for deterministic Knowledge Graphs (KG) have been extensively studied, with the purpose of capturing latent semantic relations between entities and incorporating the structured knowledge they contain into machine learning. However, there are many KGs that model uncertain knowledge, which typically model the inherent uncertainty of relations facts with a confidence score, and embedding such uncertain knowledge represents an unresolved challenge. The capturing of uncertain knowledge will benefit many knowledge-driven applications such as question answering and semantic search by providing more natural characterization of the knowledge. In this paper, we propose a novel uncertain KG embedding model UKGE, which aims to preserve both structural and uncertainty information of relation facts in the embedding space. Unlike previous models that characterize relation facts with binary classification techniques, UKGE learns embeddings according to the confidence scores of uncertain relation facts. To further enhance the precision of UKGE, we also introduce probabilistic soft logic to infer confidence scores for unseen relation facts during training. We propose and evaluate two variants of UKGE based on different confidence score modeling strategies. Experiments are conducted on three real-world uncertain KGs via three tasks, i.e. confidence prediction, relation fact ranking, and relation fact classification. UKGE shows effectiveness in capturing uncertain knowledge by achieving promising results, and it consistently outperforms baselines on these tasks.
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