AIMC Topic: Computational Biology
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Identification of Immune-Related Biomarkers of Schizophrenia in the Central Nervous System Using Bioinformatic Methods and Machine Learning Algorithms.
Molecular neurobiology
Sep 7, 2024
Schizophrenia is a disastrous mental disorder. Identification of diagnostic biomarkers and therapeutic targets is of significant importance. In this study, five datasets of schizophrenia post-mortem prefrontal cortex samples were downloaded from the ...
DeepPhoPred: Accurate Deep Learning Model to Predict Microbial Phosphorylation.
Proteins
Sep 6, 2024
Phosphorylation is a substantial posttranslational modification of proteins that refers to adding a phosphate group to the amino acid side chain after translation process in the ribosome. It is vital to coordinate cellular functions, such as regulati...
MMGCN: Multi-modal multi-view graph convolutional networks for cancer prognosis prediction.
Computer methods and programs in biomedicine
Sep 6, 2024
BACKGROUND AND OBJECTIVE: Accurate prognosis prediction for cancer patients plays a significant role in the formulation of treatment strategies, considerably impacting personalized medicine. Recent advancements in this field indicate that integrating...
Cracking the neural code for word recognition in convolutional neural networks.
PLoS computational biology
Sep 6, 2024
Learning to read places a strong challenge on the visual system. Years of expertise lead to a remarkable capacity to separate similar letters and encode their relative positions, thus distinguishing words such as FORM and FROM, invariantly over a lar...
Combining network pharmacology, machine learning, molecular docking and molecular dynamic to explore the mechanism of Chufeng Qingpi decoction in treating schistosomiasis.
Frontiers in cellular and infection microbiology
Sep 6, 2024
BACKGROUND: Although the Chufeng Qingpi Decoction (CQD) has demonstrated clinical effectiveness in the treatment of schistosomiasis, the precise active components and the underlying mechanisms of its therapeutic action remain elusive. To achieve a pr...
Multi-omics Analysis to Identify Key Immune Genes for Osteoporosis based on Machine Learning and Single-cell Analysis.
Orthopaedic surgery
Sep 5, 2024
OBJECTIVE: Osteoporosis is a severe bone disease with a complex pathogenesis involving various immune processes. With the in-depth understanding of bone immune mechanisms, discovering new therapeutic targets is crucial for the prevention and treatmen...
DTVF: A User-Friendly Tool for Virulence Factor Prediction Based on ProtT5 and Deep Transfer Learning Models.
Genes
Sep 5, 2024
Virulencefactors (VFs) are key molecules that enable pathogens to evade the immune systems of the host. These factors are crucial for revealing the pathogenic processes of microbes and drug discovery. Identification of virulence factors in microbes b...
A Physics-Informed Neural Network approach for compartmental epidemiological models.
PLoS computational biology
Sep 5, 2024
Compartmental models provide simple and efficient tools to analyze the relevant transmission processes during an outbreak, to produce short-term forecasts or transmission scenarios, and to assess the impact of vaccination campaigns. However, their ca...
Characterizing Secretion System Effector Proteins With Structure-Aware Graph Neural Networks and Pre-Trained Language Models.
IEEE journal of biomedical and health informatics
Sep 5, 2024
The Type III Secretion Systems (T3SSs) play a pivotal role in host-pathogen interactions by mediating the secretion of type III secretion system effectors (T3SEs) into host cells. These T3SEs mimic host cell protein functions, influencing interaction...
Development of a Diagnostic Model for Pancreatic Ductal Adenocarcinoma Using Machine Learning and Blood-Based miRNAs.
Oncology
Sep 4, 2024
INTRODUCTION: Pancreatic ductal adenocarcinoma (PDAC) has the lowest survival rate among all major cancers due to a lack of symptoms in early stages, early detection tools, and optimal therapies for late-stage patients. Thus, effective and non-invasi...
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