Search

The species of the genus Cordilura Fallén are not well studied in Korea with only one known species. In this study, Cordilura shatalkini and Cordilura nubecula are reported for the first time in Korea with a new key to the species of Cordilura from Korea.

Abstract Background Structure-constrained molecular generation is a promising approach to drug discovery. The goal of structure-constrained molecular generation is to produce a novel molecule that is similar to a given source molecule (e.g. hit molecules) but has enhanced chemical properties (for lead optimization). Many structure-constrained molecular generation models with superior performance in improving chemical properties have been proposed; however, they still have difficulty producing many novel molecules that satisfy both the high structural similarities to each source molecule and improved molecular properties. Methods We propose a structure-constrained molecular generation model that utilizes contractive and margin loss terms to simultaneously achieve property improvement and high structural similarity. The proposed model has two training phases; a generator first learns molecular representation vectors using metric learning with contractive and margin losses and then explores optimized molecular structure for target property improvement via reinforcement learning. Results We demonstrate the superiority of our proposed method by comparing it with various state-of-the-art baselines and through ablation studies. Furthermore, we demonstrate the use of our method in drug discovery using an example of sorafenib-like molecular generation in patients with drug resistance.

The identification of patient-specific cancer driver genes plays a crucial role in the development of personalized cancer treatment and drug development. Several computational methods have been proposed for identifying patient-specific cancer driver genes, most of which rank driver genes ac-cording to scores calculated from various gene or protein network information. In this paper, we propose a machine learning model for more accurate identification of patient-specific cancer driver genes. The training data for the proposed model is composed of the gene vectors, which indicate the impacts that one gene can have on or receive from all the genes. The gene vector is patient-specific, in other words, one gene can have many gene vectors from many cancer patients. To make gene vectors, first a patient-specific gene network is built using the gene expression data of each cancer patient and gene regulatory network, then modified PageRank is applied to the patient-specific gene network to make the impact matrix, from which gene vectors can be extracted. We used the Random Forest model to train gene vectors to find and discriminate patterns that show how known driver genes affect, or are affected by, other genes. The proposed model was tested through cross validations and independent tests using different sets of known cancer driver genes and six cancer types from The Cancer Genome Atlas (TCGA) data, and showed higher F-scores than existing patient-specific driver gene identification algorithms. The majority of predicted driver genes were rare, and F-scores calculated with these rare genes are higher than or comparable to those of frequently identified driver genes.

Abstract Background Accurate prediction of protein–ligand binding affinity is important for lowering the overall cost of drug discovery in structure-based drug design. For accurate predictions, many classical scoring functions and machine learning-based methods have been developed. However, these techniques tend to have limitations, mainly resulting from a lack of sufficient energy terms to describe the complex interactions between proteins and ligands. Recent deep-learning techniques can potentially solve this problem. However, the search for more efficient and appropriate deep-learning architectures and methods to represent protein–ligand complex is ongoing. Results In this study, we proposed a deep-neural network model to improve the prediction accuracy of protein–ligand complex binding affinity. The proposed model has two important features, descriptor embeddings with information on the local structures of a protein–ligand complex and an attention mechanism to highlight important descriptors for binding affinity prediction. The proposed model performed better than existing binding affinity prediction models on most benchmark datasets. Conclusions We confirmed that an attention mechanism can capture the binding sites in a protein–ligand complex to improve prediction performance. Our code is available at https://github.com/Blue1993/BAPA .

Precise prognosis of cancer patients is important because it is associated with suggesting appropriate therapeutic strategies. Several computational and statistical methods have been proposed, but further improvement of these methods in terms of prediction accuracy is required. This paper presents a deep learning-based method for learning networks of prognostic genes, instead of only individual biomarker genes, for more accurate cancer prognosis prediction. This method utilizes generative adversarial networks, where the generator uses a biological network instead of a traditional fully connected network to learn the distributions of gene expression (mRNA), copy number variation, single nucleotide polymorphism, and DNA methylation data from cancer patients. The proposed model was applied to seven cancer types and exhibited higher prediction accuracy as compared to the existing state-of-the-art methods. On average, the area under the curve (AUC) was improved by 4% compared to the best performing existing methods for seven cancer types. In particular, for pancreatic adenocarcinoma, AUC was improved by 27.9%. The identified prognostic genes were reproducible and functionally meaningful. To the best of our knowledge, the proposed method represents the first attempt to learn genetic networks from multi-omics data.

Abstract Machine learning may be a powerful approach to more accurate identification of genes that may serve as prognosticators of cancer outcomes using various types of omics data. However, to date, machine learning approaches have shown limited prediction accuracy for cancer outcomes, primarily owing to small sample numbers and relatively large number of features. In this paper, we provide a description of GVES (Gene Vector for Each Sample), a proposed machine learning model that can be efficiently leveraged even with a small sample size, to increase the accuracy of identification of genes with prognostic value. GVES, an adaptation of the continuous bag of words (CBOW) model, generates vector representations of all genes for all samples by leveraging gene expression and biological network data. GVES clusters samples using their gene vectors, and identifies genes that divide samples into good and poor outcome groups for the prediction of cancer outcomes. Because GVES generates gene vectors for each sample, the sample size effect is reduced. We applied GVES to six cancer types and demonstrated that GVES outperformed existing machine learning methods, particularly for cancer datasets with a small number of samples. Moreover, the genes identified as prognosticators were shown to reside within a number of significant prognostic genetic pathways associated with pancreatic cancer.

With the increasing dynamic nature of customer demand, production, product, and manufacturing design changes have become more frequent. Moreover, inadequate validation during the manufacturing design phase may result in additional issues, such as process redesign and layout reallocation, during the operation phase. Therefore, systems that can pre-validate and allow accurate and reliable analysis in the manufacturing design phase, as well as apply and optimize variations in production lines in real time, are required. Previously, digital twin (DT) has been studied a lot in product design and facility prognostics and management fields. Research on the system framework leading to DT utilization and optimization and analysis through DT in complex manufacturing systems with continuous processes such as production lines is insufficient. In this study, a system based on a DT and simulation results is developed; this system can reflect, analyze, and optimize dynamic changes in the design of processes and production lines in real time. First, the framework and application of the proposed system are designed. Subsequently, optimization methodologies based on heuristics and reinforcement learning (RL) are developed. Finally, the effectiveness and applicability of the proposed system are verified by implementing an actual DT application at a real manufacturing site.

Database systems usually have many parameters that must be configured by database administrators and users. RocksDB achieves fast data writing performance using a log-structured merged tree. This database has many parameters associated with write and space amplifications. Write amplification degrades the database performance, and space amplification leads to an increased storage space owing to the storage of unwanted data. Previously, it was proven that significant performance improvements can be achieved by tuning the database parameters. However, tuning the multiple parameters of a database is a laborious task owing to the large number of potential configuration combinations. To address this problem, we selected the important parameters that affect the performance of RocksDB using random forest. We then analyzed the effects of the selected parameters on write and space amplifications using analysis of variance. We used a genetic algorithm to obtain optimized values of the major parameters. The experimental results indicate an insignificant reduction (-5.64%) in the execution time when using these optimized values; however, write amplification, space amplification, and data processing rates improved considerably by 20.65%, 54.50%, and 89.68%, respectively, as compared to the performance when using the default settings. [ABSTRACT FROM AUTHOR]

Motivation: Identification of genes that can be used to predict prognosis in patients with cancer is important in that it can lead to improved therapy, and can also promote our understanding of tumor progression on the molecular level. One of the common but fundamental problems that render identification of prognostic genes and prediction of cancer outcomes difficult is the heterogeneity of patient samples. Results: To reduce the effect of sample heterogeneity, we clustered data samples using K-means algorithm and applied modified PageRank to functional interaction (FI) networks weighted using gene expression values of samples in each cluster. Hub genes among resulting prioritized genes were selected as biomarkers to predict the prognosis of samples. This process outperformed traditional feature selection methods as well as several network-based prognostic gene selection methods when applied to Random Forest. We were able to find many cluster-specific prognostic genes for each dataset. Functional study showed that distinct biological processes were enriched in each cluster, which seems to reflect different aspect of tumor progression or oncogenesis among distinct patient groups. Taken together, these results provide support for the hypothesis that our approach can effectively identify heterogeneous prognostic genes, and these are complementary to each other, improving prediction accuracy. [ABSTRACT FROM AUTHOR]