A Chromosome-Scale and Annotated Reference Genome Assembly of Plecia longiforceps Duda, 1934 (Diptera: Bibionidae).

Urbanization is a leading factor effecting global biodiversity, driving rapid evolutionary processes in the local biota. Species that adapt and proliferate in city environments can become pests, with human activities facilitating their dispersal and excessive outbreaks. Here we present the first genome data of Plecia longiforceps, a lovebug pest in Eastern Asia with intensive aggregations recently occurring in the Seoul Metropolitan Area of Korea.

De novo drug design through gradient-based regularized search in information-theoretically controlled latent space.

Over the last decade, automatic chemical design frameworks for discovering molecules with drug-like properties have significantly progressed. Among them, the variational autoencoder (VAE) is a cutting-edge approach that models the tractable latent space of the molecular space. In particular, the usage of a VAE along with a property estimator has attracted considerable interest because it enables gradient-based optimization of a given molecule.

ReBADD-SE: Multi-objective molecular optimisation using SELFIES fragment and off-policy self-critical sequence training.

The discovery of drugs to selectively remove disease-related cells is challenging in computer-aided drug design. Many studies have proposed multi-objective molecular generation methods and demonstrated their superiority using the public benchmark dataset for kinase inhibitor generation tasks. However, the dataset does not contain many molecules that violate Lipinski's rule of five.

COMA: efficient structure-constrained molecular generation using contractive and margin losses.

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.

Intrahepatic inflammatory IgAPD-L1 monocytes in hepatocellular carcinoma development and immunotherapy.

Background: IgA neutralizes pathogens to prevent infection at mucosal sites. However, emerging evidence shows that IgA contributes to aggravating inflammation or dismantling antitumor immunity in human diseased liver. The aim of this study was to elucidate the roles of inflammation-induced intrahepatic inflammatory IgA monocytes in the development of hepatocellular carcinoma (HCC).

Binding affinity prediction for protein-ligand complex using deep attention mechanism based on intermolecular interactions.

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.

GVES: machine learning model for identification of prognostic genes with a small dataset.

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.

RefDNN: a reference drug based neural network for more accurate prediction of anticancer drug resistance.

Cancer is one of the most difficult diseases to treat owing to the drug resistance of tumour cells. Recent studies have revealed that drug responses are closely associated with genomic alterations in cancer cells. Numerous state-of-the-art machine learning models have been developed for prediction of drug responses using various genomic data and diverse drug molecular information, but those methods are ineffective to predict drug response to untrained drugs and gene expression patterns, which is known as the cold-start problem.

G2Vec: Distributed gene representations for identification of cancer prognostic genes.

Identification of cancer prognostic genes is important in that it can lead to accurate outcome prediction and better therapeutic trials for cancer patients. Many computational approaches have been proposed to achieve this goal; however, there is room for improvement. Recent developments in deep learning techniques can aid in the identification of better prognostic genes and more accurate outcome prediction, but one of the main problems in the adoption of deep learning for this purpose is that data from cancer patients have too many dimensions, while the number of samples is relatively small.

Prediction of GPCR-Ligand Binding Using Machine Learning Algorithms.

We propose a novel method that predicts binding of G-protein coupled receptors (GPCRs) and ligands. The proposed method uses hub and cycle structures of ligands and amino acid motif sequences of GPCRs, rather than the 3D structure of a receptor or similarity of receptors or ligands. The experimental results show that these new features can be effective in predicting GPCR-ligand binding (average area under the curve [AUC] of 0.

Improved prediction of breast cancer outcome by identifying heterogeneous biomarkers.

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.