Hybrid Approach of Relation Network and Localized Graph Convolutional Filtering for Breast Cancer Subtype Classification

Network biology has been successfully used to help reveal complex mechanisms of disease, especially cancer. On the other hand, network biology requires in-depth knowledge to construct disease-specific networks, but our current knowledge is very limited even with the recent advances in human cancer biology. Deep learning has shown a great potential to address the difficult situation like this. However, deep learning technologies conventionally use grid-like structured data, thus application of deep learning technologies to the classification of human disease subtypes is yet to be explored. Recently, graph based deep learning techniques have emerged, which becomes an opportunity to leverage analyses in network biology. In this paper, we proposed a hybrid model, which integrates two key components 1) graph convolution neural network (graph CNN) and 2) relation network (RN). We utilize graph CNN as a component to learn expression patterns of cooperative gene community, and RN as a component to learn associations between learned patterns. The proposed model is applied to the PAM50 breast cancer subtype classification task, the standard breast cancer subtype classification of clinical utility. In experiments of both subtype classification and patient survival analysis, our proposed method achieved significantly better performances than existing methods. We believe that this work is an important starting point to realize the upcoming personalized medicine.Comment: 8 pages, To be published in proceeding of IJCAI 201

MarioNETte: Few-shot Face Reenactment Preserving Identity of Unseen Targets

When there is a mismatch between the target identity and the driver identity, face reenactment suffers severe degradation in the quality of the result, especially in a few-shot setting. The identity preservation problem, where the model loses the detailed information of the target leading to a defective output, is the most common failure mode. The problem has several potential sources such as the identity of the driver leaking due to the identity mismatch, or dealing with unseen large poses. To overcome such problems, we introduce components that address the mentioned problem: image attention block, target feature alignment, and landmark transformer. Through attending and warping the relevant features, the proposed architecture, called MarioNETte, produces high-quality reenactments of unseen identities in a few-shot setting. In addition, the landmark transformer dramatically alleviates the identity preservation problem by isolating the expression geometry through landmark disentanglement. Comprehensive experiments are performed to verify that the proposed framework can generate highly realistic faces, outperforming all other baselines, even under a significant mismatch of facial characteristics between the target and the driver.Comment: In AAAI 202

A secure SNP panel scheme using homomorphically encrypted K-mers without SNP calling on the user side

Background Single Nucleotide Polymorphism (SNP) in the genome has become crucial information for clinical use. For example, the targeted cancer therapy is primarily based on the information which clinically important SNPs are detectable from the tumor. Many hospitals have developed their own panels that include clinically important SNPs. The genome information exchange between the patient and the hospital has become more popular. However, the genome sequence information is innate and irreversible and thus its leakage has serious consequences. Therefore, protecting ones genome information is critical. On the other side, hospitals may need to protect their own panels. There is no known secure SNP panel scheme to protect both. Results In this paper, we propose a secure SNP panel scheme using homomorphically encrypted K-mers without requiring SNP calling on the user side and without revealing the panel information to the user. Use of the powerful homomorphic encryption technique is desirable, but there is no known algorithm to efficiently align two homomorphically encrypted sequences. Thus, we designed and implemented a novel secure SNP panel scheme utilizing the computationally feasible equality test on two homomorphically encrypted K-mers. To make the scheme work correctly, in addition to SNPs in the panel, sequence variations at the population level should be addressed. We designed a concept of Point Deviation Tolerance (PDT) level to address the false positives and false negatives. Using the TCGA BRCA dataset, we demonstrated that our scheme works at the level of over a hundred thousand somatic mutations. In addition, we provide a computational guideline for the panel design, including the size of K-mer and the number of SNPs. Conclusions The proposed method is the first of its kind to protect both the users sequence and the hospitals panel information using the powerful homomorphic encryption scheme. We demonstrated that the scheme works with a simulated dataset and the TCGA BRCA dataset. In this study, we have shown only the feasibility of the proposed scheme and much more efforts should be done to make the scheme usable for clinical use.This research is supported by National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT (No. NRF-2017M3C4A7065887), The Collaborative Genome Program for Fostering New Post-Genome Industry of the National Research Foundation (NRF) funded by the Ministry of Science and ICT (MSIT) (No. NRF-2014M3C9A3063541), A grant of the Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health & Welfare, Republic of Korea (grant number: HI15C3224), and Institute for Information & communications Technology Promotion (IITP) grant funded by the Korea government (MSIP) (B0717-16-0098, Development of homomorphic encryption for DNA analysis and biometry authentication). The publication cost will be paid by the Seoul National University Office of Research

A selective fluorescent probe for cysteine and its imaging in live cells

A probe for the detection of cysteine over homocysteine based on 2-(2'-hydroxyphenyl) benzothiazole (HBT) was prepared and used in confocal microscopy experiments. The probe was designed to block excited state intramolecular proton transfer (ESIPT). When bromopropionyl group protection is removed, HBT is recovered via nucleophilic substitution and intramolecular cyclization. The probe was found to have a detection limit of 2.8 mu M and exhibits a similar to 20-fold increase. The probe showed cell membrane permeability and efficacy in living Hep3B cells © The Royal Society of Chemistry 2014191

Additional file 1 of CLIP-GENE: a web service of the condition specific context-laid integrative analysis for gene prioritization in mouse TF knockout experiments

Table S1. Performance comparison of CLIP-GENE (excluding and including network) while analyzing Gata3, Setd2, and Barx2 knockout data. Table S2. Performance comparison of CLIP-GENE (applied network and RegNetwork) while analyzing Gata3, Setd2, and Barx2 knockout data. Table S3. Performance comparison of CLIP-GENE (no-context, best context, worst context, combination of context) while analyzing Gata3, Setd2, and Barx2 knockout data. (DOCX 20.4 kb

[Cu-64]Cu-Albumin Clearance Imaging to Evaluate Lymphatic Efflux of Cerebrospinal Space Fluid in Mouse Model

Purpose Clearance of brain waste in the cerebrospinal fluid (CSF) through the meningeal lymphatic vessels (mLV) has been evaluated mostly through the fluorescent imaging which has inherent limitations in the context of animal physiology and clinical translatability. The study aimed to establish molecular imaging for the evaluation of mLV clearance function. Methods Radionuclide imaging after intrathecal (IT) injection was acquired in C57BL/6 mice of 2-9 months. The distribution of [Tc-99m]Tc-diethylenetriamine pentaacetate (DTPA) and [Cu-64]Cu-human serum albumin (HSA) was comparatively evaluated. Evans Blue and [Cu-64]Cu-HSA were used to evaluate the distribution of tracer under various speed and volume conditions. Results [Tc-99m]Tc-DTPA is not a suitable tracer for evaluation of CSF clearance via mLV as no cervical lymph node uptake was observed while it was cleared from the body. A total volume of 3 to 9 mu L at an infusion rate of 300 to 500 nL/min was not sufficient for the tracer to reach the cranial subarachnoid space and clear throughout the mLV. As a result, whole-body positron emission tomography imaging using [Cu-64]Cu-HSA at 700 nL/min, to deliver 6 mu L of injected volume, was set for characterization of the CSF to mLV clearance. Through this protocol, the mean terminal CSF clearance half-life was measured to be 123.6 min (range 117.0-135.0) in normal mice. Conclusions We established molecular imaging to evaluate CSF drainage through mLV using [Cu-64]Cu-HSA. This imaging method is expected to be extended in animal models of dysfunctional meningeal lymphatic clearance and translational research for disease-modifying therapeutic approaches.N