Multilevel Typed Graph Transformations
Ontology highlight
ABSTRACT:
SUBMITTER: Gadducci F
PROVIDER: S-EPMC7314735 | biostudies-literature | 2020 May
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:MotivationThanks to the increasing availability of drug-drug interactions (DDI) datasets and large biomedical knowledge graphs (KGs), accurate detection of adverse DDI using machine learning models becomes possible. However, it remains largely an open problem how to effectively utilize large and noisy biomedical KG for DDI detection. Due to its sheer size and amount of noise in KGs, it is often less beneficial to directly integrate KGs with other smaller but higher quality data (e.g. experimental data). Most of existing approaches ignore KGs altogether. Some tries to directly integrate KGs with other data via graph neural networks with limited success. Furthermore most previous works focus on binary DDI prediction whereas the multi-typed DDI pharmacological effect prediction is more meaningful but harder task.ResultsTo fill the gaps, we propose a new method SumGNN: knowledge summarization graph neural network, which is enabled by a subgraph extraction module that can efficiently anchor on relevant subgraphs from a KG, a self-attention based subgraph summarization scheme to generate reasoning path within the subgraph, and a multi-channel knowledge and data integration module that utilizes massive external biomedical knowledge for significantly improved multi-typed DDI predictions. SumGNN outperforms the best baseline by up to 5.54%, and performance gain is particularly significant in low data relation types. In addition, SumGNN provides interpretable prediction via the generated reasoning paths for each prediction.Availability and implementationThe code is available in Supplementary Material.Supplementary informationSupplementary data are available at Bioinformatics online.
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Project description:Synthetic lethality (SL) has shown great promise for the discovery of novel targets in cancer. CRISPR double-knockout (CDKO) technologies can only screen several hundred genes and their combinations, but not genome-wide. Therefore, good SL prediction models are highly needed for genes and gene pairs selection in CDKO experiments. In this paper, we develop a novel multi-layer encoder for individual sample-specific SL prediction (MLEC-iSL). Unlike existing SL prediction models, MLEC-iSL is built to predict SL connectivity first. Because SL connectivity is scalable from existing genes in the training data to new genes in validation data, we hypothesize MLEC-iSL has better SL prediction performance. MLEC-iSL has three encoders, namely gene encoder, graph encoder, and transformer encoder. MLEC-iSL has high performance in K562 (AUPR, 0.73; AUC, 0.72) and Jurkat (AUPR, 0.73; AUC, 0.71) cells while no existing methods exceed 0.62 AUPR and AUC in either cell. MLEC-iSL guided CDKO experiment in 22Rv1 cells yielded a 46.8% SL ratio amongst its selected gene pairs. Six of top ten SL connectivity hub genes are validated in 22Rv1 cells. It reveals SL gene pairs and dependency between apoptosis and mitosis cell death pathways.
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Project description:BackgroundBlood donors exhibiting a weak D or DEL phenotypical expression may be mistyped D- by standard serology hence permitting incompatible transfusion to D- recipients. Molecular methods may overcome these technical limits. Our aim was to estimate the frequency of RHD alleles among the apparently D- Polish donor population and to characterize its molecular background.Study design and methodsPlasma pools collected from 31,200 consecutive Polish donors typed as D- were tested by real-time polymerase chain reaction (PCR) for the presence of RHD-specific markers located in Intron 4 and Exons 7 and 10. RHD+ individuals were characterized by PCR or cDNA sequencing and serology.ResultsPlasma cross-pool strategy revealed 63 RHD+ donors harboring RHD*01N.03 (n = 17), RHD*15 (n = 12), RHD*11 (n = 7), RHD*DEL8 (n = 3), RHD*01W.2 (n = 3), RHD-CE(10) (n = 3), RHD*01W.3, RHD*01W.9, RHD*01N.05, RHD*01N.07, RHD*01N.23, and RHD(IVS1-29G>C) and two novel alleles, RHD*(767C>G) (n = 3) and RHD*(1029C>A). Among 47 cases available for serology, 27 were shown to express the D antigenConclusion1) Plasma cross-pool strategy is a reliable and cost-effective tool for RHD screening. 2) Only 0.2% of D- Polish donors carry some fragments of the RHD gene; all of them were C or E+. 3) Almost 60% of the detected RHD alleles may be potentially immunogenic when transfused to a D- recipient.
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