ion GRR = 1.9, P = 8.0 × 10-4]. These results confirm that recent consanguinity and autozygosity in the outbred population increase risk for LOAD. Subsequent work, with increased samples sizes of consanguineous subjects, should accelerate the discovery of non-additive genetic effects in LOAD.To study the pathophysiology of human diseases, develop innovative treatments, and refine approaches for regenerative medicine require appropriate preclinical models. Pigs share physiologic and anatomic characteristics with humans and are genetically more similar to humans than are mice. Genetically modified pigs are essential where rodent models do not mimic the human disease phenotype. The male germline stem cell or spermatogonial stem cell (SSC) is unique; it is the only cell type in an adult male that divides and contributes genes to future generations, making it an ideal target for genetic modification. Here we report that CRISPR/Cas9 ribonucleoprotein (RNP)-mediated gene editing in porcine spermatogonia that include SSCs is significantly more efficient than previously reported editing with TALENs and allows precise gene editing by homology directed repair (HDR). We also established homology-mediated end joining (HMEJ) as a second approach to targeted gene editing to enable introduction of larger transgenes and/or humanizing parts of the pig genome for disease modeling or regenerative medicine. In summary, the approaches established in the current study result in efficient targeted genome editing in porcine germ cells for precise replication of human disease alleles.The functions of proteins are mainly determined by their subcellular localizations in cells. Currently, many computational methods for predicting the subcellular localization of proteins have been proposed. However, these methods require further improvement, especially when used in protein representations. In this study, we present an embedding-based method for predicting the subcellular localization of proteins. We first learn the functional embeddings of KEGG/GO terms, which are further used in representing proteins. Then, we characterize the network embeddings of proteins on a protein-protein network. The functional and network embeddings are combined as novel representations of protein locations for the construction of the final classification model. In our collected benchmark dataset with 4,861 proteins from 16 locations, the best model shows a Matthews correlation coefficient of 0.872 and is thus superior to multiple conventional methods.Early and precise prediction is an important way to reduce the poor prognosis of lung adenocarcinoma (LUAD) patients. Nevertheless, the widely used tumor, node, and metastasis (TNM) staging system based on anatomical information only often could not achieve adequate performance on foreseeing the prognosis of LUAD patients. This study thus aimed to examine whether the long non-coding RNAs (lncRNAs), known highly involved in the tumorigenesis of LUAD through the competing endogenous RNAs (ceRNAs) mechanism, could provide additional information to improve prognosis prediction of LUAD patients. To prove the hypothesis, a dataset consisting of both RNA sequencing data and clinical pathological data, obtained from The Cancer Genome Atlas (TCGA) database, was analyzed. Then, differentially expressed RNAs (DElncRNAs, DEmiRNAs, and DEmRNAs) were identified and a lncRNA-miRNA-mRNA ceRNA network was constructed based on those differentially expressed RNAs. Functional enrichment analysis revealed that this ceRNA network strata of the stage, gender, or age, rendering it to be a wide application. Finally, a ceRNA subnetwork related to the signature was extracted, demonstrating its high involvement in the tumorigenesis mechanism of LUAD. In conclusion, the present study established a lncRNA-based molecular signature, which can significantly improve prognosis prediction for LUAD patients.Adverse drug reactions (ADRs) are a major public health concern, and early detection is crucial for drug development and patient safety. Together with the increasing availability of large-scale literature data, machine learning has the potential to predict unknown ADRs from current knowledge. By the machine learning methods, we constructed a Tumor-Biomarker Knowledge Graph (TBKG) which contains four types of node Tumor, Biomarker, Drug, and ADR using biomedical literatures. Based on this knowledge graph, we not only discovered potential ADRs of antitumor drugs but also provided explanations. Experiments on real-world data show that our model can achieve 0.81 accuracy of three cross-validation and the ADRs discovery of Osimertinib was chosen for the clinical validation. Calculated ADRs of Osimertinib by our model consisted of the known ADRs which were in line with the official manual and some unreported rare ADRs in clinical cases.  https://www.selleckchem.com/products/cetuximab.html  Results also showed that our model outperformed traditional co-occurrence methods. Moreover, each calculated ADRs were attached with the corresponding paths of "tumor-biomarker-drug" in the knowledge graph which could help to obtain in-depth insights into the underlying mechanisms. In conclusion, the tumor-biomarker knowledge-graph based approach is an explainable method for potential ADRs discovery based on biomarkers and might be valuable to the community working on the emerging field of biomedical literature mining and provide impetus for the mechanism research of ADRs.Balanced chromosomal abnormalities (BCAs) are changes in the localization or orientation of a chromosomal segment without visible gain or loss of genetic material. BCAs occur at a frequency of 1 in 500 newborns and are associated with an increased risk of multiple congenital anomalies and/or neurodevelopmental disorders, especially if it is a de novo mutation. In this pilot project, we used short read genome sequencing (GS) to retrospectively re-sequence ten prenatal subjects with de novo BCAs and compared the performance of GS with the original karyotyping. GS characterized all BCAs found by conventional karyotyping with the added benefit of precise sub-band delineation. By identifying BCA breakpoints at the nucleotide level using GS, we found disruption of OMIM genes in three cases and identified cryptic gain/loss at the breakpoints in two cases. Of these five cases, four cases reached a definitive genetic diagnosis while the other one case had a BCA interpreted as unknown clinical significance. The additional information gained from GS can change the interpretation of the BCAs and has the potential to improve the genetic counseling and perinatal management by providing a more specific genetic diagnosis.