定量方法
Structure-based drug design (SBDD) stands at the forefront of drug discovery, emphasizing the creation of molecules that target specific binding pockets. Recent advances in this area have witnessed the adoption of deep generative models and…
Cells are the fundamental unit of biological organization, and identifying them in imaging data - cell segmentation - is a critical task for various cellular imaging experiments. While deep learning methods have led to substantial progress…
The most common gene regulation mechanism is when a transcription factor protein binds to a regulatory sequence to increase or decrease RNA transcription. However, transcription factors face two main challenges when searching for these…
In this paper, we focus on the classification of tweets as sources of potential signals for adverse drug effects (ADEs) or drug reactions (ADRs). Following the intuition that text and drug structure representations are complementary, we…
Purpose: To develop biophysics-based method for estimating perfusion Q from arterial spin labeling (ASL) images using deep learning. Methods: A 3D U-Net (QTMnet) was trained to estimate perfusion from 4D tracer propagation images. The…
The Waddington landscape serves as a metaphor illustrating the developmental process of cells, likening it to a small ball rolling down various trajectories into valleys. Constructing an epigenetic landscape of this nature aids in…
Phenotype-based screening has attracted much attention for identifying cell-active compounds. Transcriptional and proteomic profiles of cell population or single cells are informative phenotypic measures of cellular responses to…
Accurately predicting drug-drug interactions (DDI) for emerging drugs, which offer possibilities for treating and alleviating diseases, with computational methods can improve patient care and contribute to efficient drug development.…
Background: Shigella is one of the major cause of dysenteric diarrhea, which is known shigelosis. Shigelosis causes 160,000 deaths annually of diarrheal disease in a global scale especially children less than 5 years old. No licensed…
Drug-target interaction (DTI) prediction has become a foundational task in drug repositioning, polypharmacology, drug discovery, as well as drug resistance and side-effect prediction. DTI identification using machine learning is gaining…
Non-alcoholic fatty liver disease (NAFLD) is a prevalent chronic liver disorder characterized by the excessive accumulation of fat in the liver in individuals who do not consume significant amounts of alcohol, including risk factors like…
Glioblastoma is a highly aggressive and lethal form of brain cancer. Magnetic resonance imaging (MRI) plays a significant role in the diagnosis, treatment planning, and follow-up of glioblastoma patients due to its non-invasive and…
Theta oscillations, ranging from 4-8 Hz, play a significant role in spatial learning and memory functions during navigation tasks. Frontal theta oscillations are thought to play an important role in spatial navigation and memory.…
Proteomics is the large scale study of protein structure and function from biological systems through protein identification and quantification. "Shotgun proteomics" or "bottom-up proteomics" is the prevailing strategy, in which proteins…
Purpose: The purpose of this study is to evaluate the effect of an innovative prophylactic knee orthosis on postural balance. This prophylactic knee orthosis is designed with a compression that is oriented in a chosen direction. The purpose…
Prognostic signatures play an important role in clinical research, offering insights into the potential health outcomes of patients and guiding therapeutic decisions. Although single-gene prognostic biomarkers are valuable, multi-gene…
Identifying and discovering drug-target interactions(DTIs) are vital steps in drug discovery and development. They play a crucial role in assisting scientists in finding new drugs and accelerating the drug development process. Recently,…
We hypothesize that large language models (LLMs) based on the transformer architecture can enable automated detection of clinical phenotype terms, including terms not documented in the HPO. In this study, we developed two types of models:…
Throughout the life sciences we routinely seek to interpret measurements and observations using parameterised mechanistic mathematical models. A fundamental and often overlooked choice in this approach involves relating the solution of a…
Reaction-diffusion systems have been proposed as a model for pattern formation and morphogenesis. The Fickian diffusion typically employed in these constructions model the Brownian motion of particles. The biological and chemical elements…