Interformer: an interaction-aware model for protein-ligand docking and affinity prediction.

In recent years, the application of deep learning models to protein-ligand docking and affinity prediction, both vital for structure-based drug design, has garnered increasing interest. However, many of these models overlook the intricate modeling of interactions between ligand and protein atoms in the complex, consequently limiting their capacity for generalization and interpretability. In this work, we propose Interformer, a unified model built upon the Graph-Transformer architecture.

BloodPatrol: Revolutionizing Blood Cancer Diagnosis - Advanced Real-Time Detection Leveraging Deep Learning & Cloud Technologies.

Cloud computing and Internet of Things (IoT) technologies are gradually becoming the technological changemakers in cancer diagnosis. Blood cancer is an aggressive disease affecting the blood, bone marrow, and lymphatic system, and its early detection is crucial for subsequent treatment. Flow cytometry has been widely studied as a commonly used method for detecting blood cancer.

Comprehensive evaluation of molecule property prediction with ChatGPT.

The versatility of ChatGPT in performing a diverse range of tasks has elicited considerable interest on its potential applications within professional fields. Taking drug discovery as a testbed, this paper provides a comprehensive evaluation of ChatGPT's ability on molecule property prediction. The study focuses on three aspects: 1) Effects of different prompt settings, where we investigate the impact of varying prompts on the prediction outcomes of ChatGPT; 2) Comprehensive evaluation on molecule property prediction, where we conduct a comprehensive evaluation on 53 ADMET-related endpoints; 3) Analysis of ChatGPT's potential and limitations, where we make comparisons with models tailored for molecule property prediction, thus gaining a more accurate understanding of ChatGPT's capabilities and limitations in this area.

Impact of interaction between interleukin-6 gene polymorphism and Helicobacter pylori infection on susceptibility to gastric cancer.

Objective: This study aimed to evaluate the association between four single nucleotide polymorphisms (SNPs) of the interleukin-6 (IL-6) gene and gastric cancer (GC), and impact of interaction between IL-6 SNPs and Helicobacter pylori (H. pylori ) infection on susceptibility to GC.

Methods: Logistic regression was used to test the relationships between four SNPs of IL-6 gene and GC susceptibility.

CircRNA circSLIT2 is a novel diagnostic and prognostic biomarker for gastric cancer.

Background: Many diseases can mimic the symptoms of gastric cancer (GC). Therefore, misdiagnosis of GC is common. Our preliminary sequencing analysis revealed the altered expression of circSLIT2 in GC.

Interaction Between Vascular Endothelial Growth Factor Gene Polymorphism and Smoking on Gastric Cancer Risk in Chinese Han Population.

In this study, we aimed to evaluate the associations of vascular endothelial growth factor () gene single nucleotide polymorphisms (SNPs) and its interaction with current smoking with gastric cancer (GC) risk in the Chinese Han population. We used logistic regression model to test the association between gene polymorphism and the risk of GC. The association strength was evaluated by odds ratio (OR) and 95% confidence interval (CI) calculated using logistic regression.

A systematic comparison of data selection criteria for SMT domain adaptation.

Data selection has shown significant improvements in effective use of training data by extracting sentences from large general-domain corpora to adapt statistical machine translation (SMT) systems to in-domain data. This paper performs an in-depth analysis of three different sentence selection techniques. The first one is cosine tf-idf, which comes from the realm of information retrieval (IR).