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Computational Biology

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Cracking the neural code for word recognition in convolutional neural networks.

PLoS computational biology
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
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
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
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
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
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
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...

DCSGMDA: A dual-channel convolutional model based on stacked deep learning collaborative gradient decomposition for predicting miRNA-disease associations.

Computational biology and chemistry
Numerous studies have shown that microRNAs (miRNAs) play a key role in human diseases as critical biomarkers. Its abnormal expression is often accompanied by the emergence of specific diseases. Therefore, studying the relationship between miRNAs and ...

A Contrastive-Learning-Based Deep Neural Network for Cancer Subtyping by Integrating Multi-Omics Data.

Interdisciplinary sciences, computational life sciences
BACKGROUND: Accurate identification of cancer subtypes is crucial for disease prognosis evaluation and personalized patient management. Recent advances in computational methods have demonstrated that multi-omics data provides valuable insights into t...

Predicting splicing patterns from the transcription factor binding sites in the promoter with deep learning.

BMC genomics
BACKGROUND: Alternative splicing is a pivotal mechanism of post-transcriptional modification that contributes to the transcriptome plasticity and proteome diversity in metazoan cells. Although many splicing regulations around the exon/intron regions ...