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ISLRWR: A network diffusion algorithm for drug-target interactions prediction.
PLoS One
January 2025
Shanghai Xinhao Information Technology Co., Ltd., Shanghai, China.
Machine learning techniques and computer-aided methods are now widely used in the pre-discovery tasks of drug discovery, effectively improving the efficiency of drug development and reducing the workload and cost. In this study, we used multi-source heterogeneous network information to build a network model, learn the network topology through multiple network diffusion algorithms, and obtain compressed low-dimensional feature vectors for predicting drug-target interactions (DTIs). We applied the metropolis-hasting random walk (MHRW) algorithm to improve the performance of the random walk with restart (RWR) algorithm, forming the basis by which the self-loop probability of the current node is removed.
View Article and Find Full Text PDFA new computational framework for simulating airway resistance, fraction of exhaled nitric oxide, and diffusing capacity for nitric oxide.
PLoS One
January 2025
Transfers, Interfaces and Processes, Université libre de Bruxelles, Brussels, Belgium.
In this paper, we present a new computational framework for the simulation of airway resistance, the fraction of exhaled nitric oxide, and the diffusion capacity for nitric oxide in healthy and unhealthy lungs. Our approach is firstly based on a realistic representation of the geometry of healthy lungs as a function of body mass, which compares well with data from the literature, particularly in terms of lung volume and alveolar surface area. The original way in which this geometry is created, including an individual definition of the airways in the first seven generations of the lungs, makes it possible to consider the heterogeneous nature of the lungs in terms of perfusion and ventilation.
View Article and Find Full Text PDFAttention-aware differential learning for predicting peptide-MHC class I binding and T cell receptor recognition.
Brief Bioinform
November 2024
School of Computer Science, Northwestern Polytechnical University, Xi'an, 710129 Shaanxi, China.
The identification of neoantigens is crucial for advancing vaccines, diagnostics, and immunotherapies. Despite this importance, a fundamental question remains: how to model the presentation of neoantigens by major histocompatibility complex class I molecules and the recognition of the peptide-MHC-I (pMHC-I) complex by T cell receptors (TCRs). Accurate prediction of pMHC-I binding and TCR recognition remains a significant computational challenge in immunology due to intricate binding motifs and the long-tail distribution of known binding pairs in public databases.
View Article and Find Full Text PDFRoots to the rescue: how plants harness hydraulic redistribution to survive drought across contrasting soil textures.
Adv Biotechnol (Singap)
November 2024
Root-Soil Interaction, TUM School of Life Sciences, Technical University of Munich, 85354, Freising, Germany.
Hydraulic redistribution (HR) is a critical ecological process whereby plant roots transfer water from wetter to drier soil layers, significantly impacting soil moisture dynamics and plant water and nutrient uptake. Yet a comprehensive understanding of the mechanism triggering HR and its influencing factors remains elusive. Here, we conducted a systematic meta-analysis to discuss the influence of soil conditions and plant species characteristics on HR occurrence.
View Article and Find Full Text PDFPrediction of dry matter intake in growing Black Bengal goats using artificial neural networks.
Trop Anim Health Prod
January 2025
Livestock Production and Management Section, ICAR-Indian Veterinary Research Institute, Izatnagar, Bareilly, Uttar Pradesh, 243 122, India.
Dry matter intake (DMI) determination is essential for effective management of meat goats, especially in optimizing feed utilization and production efficiency. Unfortunately, farmers often face challenges in accurately predicting DMI which leads to wastage of feed and an increase in the cost of production. This investigation aimed to predict DMI in Black Bengal goats by using body weight (BW), body condition score (BCS), average daily gain (ADG), and metabolic body weight (MBW) by applying an artificial neural network (ANN) model.
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