AIMC Topic: Computational Biology
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scAMZI: attention-based deep autoencoder with zero-inflated layer for clustering scRNA-seq data.
BMC genomics
Apr 7, 2025
BACKGROUND: Clustering scRNA-seq data plays a vital role in scRNA-seq data analysis and downstream analyses. Many computational methods have been proposed and achieved remarkable results. However, there are several limitations of these methods. First...
Machine learning-based in-silico analysis identifies signatures of lysyl oxidases for prognostic and therapeutic response prediction in cancer.
Cell communication and signaling : CCS
Apr 5, 2025
BACKGROUND: Lysyl oxidases (LOX/LOXL1-4) are crucial for cancer progression, yet their transcriptional regulation, potential therapeutic targeting, prognostic value and involvement in immune regulation remain poorly understood. This study comprehensi...
CGLoop: a neural network framework for chromatin loop prediction.
BMC genomics
Apr 5, 2025
BACKGROUND: Chromosomes of species exhibit a variety of high-dimensional organizational features, and chromatin loops, which are fundamental structures in the three-dimensional (3D) structure of the genome. Chromatin loops are visible speckled patter...
TransBind allows precise detection of DNA-binding proteins and residues using language models and deep learning.
Communications biology
Apr 5, 2025
Identifying DNA-binding proteins and their binding residues is critical for understanding diverse biological processes, but conventional experimental approaches are slow and costly. Existing machine learning methods, while faster, often lack accuracy...
RPI-GGCN: Prediction of RNA-Protein Interaction Based on Interpretability Gated Graph Convolution Neural Network and Co-Regularized Variational Autoencoders.
IEEE transactions on neural networks and learning systems
Apr 4, 2025
RNA-protein interactions (RPIs) play an important role in several fundamental cellular physiological processes, including cell motility, chromosome replication, transcription and translation, and signaling. Predicting RPI can guide the exploration of...
SeqNovo: De Novo Peptide Sequencing Prediction in IoMT via Seq2Seq.
IEEE journal of biomedical and health informatics
Apr 4, 2025
In the Internet of Medical Things (IoMT), de novo peptide sequencing prediction is one of the most important techniques for the fields of disease prediction, diagnosis, and treatment. Recently, deep-learning-based peptide sequencing prediction has be...
Mitigating ambient RNA and doublets effects on single cell transcriptomics analysis in cancer research.
Cancer letters
Apr 3, 2025
In cancer biology, where understanding the tumor microenvironment at high resolution is vital, ambient RNA contamination becomes a considerable problem. This hinders accurate delineation of intratumoral heterogeneity, complicates the identification o...
Machine learning approaches enable the discovery of therapeutics across domains.
Molecular therapy : the journal of the American Society of Gene Therapy
Apr 3, 2025
Multi-modal datasets have grown exponentially in the last decade. This has created an enormous demand for machine learning models that can predict complex outcomes by leveraging cellular, molecular, and humoral profiles. Corresponding inference of me...
How did we get there? AI applications to biological networks and sequences.
Computers in biology and medicine
Apr 3, 2025
The rapidly advancing field of artificial intelligence (AI) has transformed numerous scientific domains, including biology, where a vast and complex volume of data is available for analysis. This paper provides a comprehensive overview of the current...
Integrating multiple spatial transcriptomics data using community-enhanced graph contrastive learning.
PLoS computational biology
Apr 3, 2025
Due to the rapid development of spatial sequencing technologies, large amounts of spatial transcriptomic datasets have been generated across various technological platforms or different biological conditions (e.g., control vs. treatment). Spatial tra...
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