AIMC Topic: Computational Biology
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Joint inference of discrete cell types and continuous type-specific variability in single-cell datasets with MMIDAS.
Nature computational science
Sep 23, 2024
Reproducible definition and identification of cell types is essential to enable investigations into their biological function and to understand their relevance in the context of development, disease and evolution. Current approaches model variability...
The next revolution in computational simulations: Harnessing AI and quantum computing in molecular dynamics.
Current opinion in structural biology
Sep 21, 2024
The integration of artificial intelligence, machine learning and quantum computing into molecular dynamics simulations is catalyzing a revolution in computational biology, improving the accuracy and efficiency of simulations. This review describes th...
Ten quick tips for ensuring machine learning model validity.
PLoS computational biology
Sep 19, 2024
Artificial Intelligence (AI) and Machine Learning (ML) models are increasingly deployed on biomedical and health data to shed insights on biological mechanism, predict disease outcomes, and support clinical decision-making. However, ensuring model va...
Intelligence model on sequence-based prediction of PPI using AISSO deep concept with hyperparameter tuning process.
Scientific reports
Sep 18, 2024
Protein-protein interaction (PPI) prediction is vital for interpreting biological activities. Even though many diverse sorts of data and machine learning approaches have been employed in PPI prediction, performance still has to be enhanced. As a resu...
Multi-omics features of immunogenic cell death in gastric cancer identified by combining single-cell sequencing analysis and machine learning.
Scientific reports
Sep 18, 2024
Gastric cancer (GC) is a prevalent malignancy with high mortality rates. Immunogenic cell death (ICD) is a unique form of programmed cell death that is closely linked to antitumor immunity and plays a critical role in modulating the tumor microenviro...
Virus-host interactions predictor (VHIP): Machine learning approach to resolve microbial virus-host interaction networks.
PLoS computational biology
Sep 18, 2024
Viruses of microbes are ubiquitous biological entities that reprogram their hosts' metabolisms during infection in order to produce viral progeny, impacting the ecology and evolution of microbiomes with broad implications for human and environmental ...
MHIPM: Accurate Prediction of Microbe-Host Interactions Using Multiview Features from a Heterogeneous Microbial Network.
Journal of chemical information and modeling
Sep 17, 2024
Current studies have demonstrated that microbe-host interactions (MHIs) play important roles in human public health. Therefore, identifying the interactions between microbes and hosts is beneficial to understanding the role of the microbiome and thei...
From whole-slide image to biomarker prediction: end-to-end weakly supervised deep learning in computational pathology.
Nature protocols
Sep 16, 2024
Hematoxylin- and eosin-stained whole-slide images (WSIs) are the foundation of diagnosis of cancer. In recent years, development of deep learning-based methods in computational pathology has enabled the prediction of biomarkers directly from WSIs. Ho...
Tissue-aware interpretation of genetic variants advances the etiology of rare diseases.
Molecular systems biology
Sep 16, 2024
Pathogenic variants underlying Mendelian diseases often disrupt the normal physiology of a few tissues and organs. However, variant effect prediction tools that aim to identify pathogenic variants are typically oblivious to tissue contexts. Here we r...
Deep neural network models for cell type prediction based on single-cell Hi-C data.
BMC genomics
Sep 16, 2024
BACKGROUND: Cell type prediction is crucial to cell type identification of genomics, cancer diagnosis and drug development, and it can solve the time-consuming and difficult problem of cell classification in biological experiments. Therefore, a compu...
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