AIMC Topic: Deep Learning

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Flexible protein-ligand docking with diffusion-based side-chain packing.

Proceedings of the National Academy of Sciences of the United States of America
Understanding protein structure and dynamics is crucial for basic biology and drug design. Conventional methods often provide static conformations that inadequately capture protein flexibility. We present PackDock, a framework that integrates deep le...

Enhanced classification prostate cancer based on generative adversarial networks and integrated deep learning with vision transformer models.

Scientific reports
By eliminating the need to alter the source images, this paper introduces a secure technique for coverless image steganography that strengthens defense against steganalysis attacks. Our method makes use of a hybrid Generative Adversarial Network (GAN...

DeepMLP: A Proteomics-Driven Deep Learning Framework for Identifying Mis-Localized Proteins across Pan-Cancer.

Journal of chemical information and modeling
Accurate protein subcellular localization (PSL) is essential for proteins to perform their biological functions, whereas protein mis-localization can disrupt cellular processes and contribute to various diseases, particularly cancer. Although spatial...

AttentionScore: A Target-Specific, Bias-Aware Scoring Function for Structure-Based Virtual Screening: A Case Study on METTL3.

Journal of chemical information and modeling
Target-specific scoring functions offer a promising route to improve structure-based virtual screening beyond generic, bias-prone scoring schemes. Here, we introduce AttentionScore, a deep learning-based scoring function for METTL3 that integrates li...

Deep learning-based 3D automatic segmentation of impacted canines in CBCT scans.

BMC oral health
BACKGROUND: Impacted canines are one of the most frequently encountered dental anomalies in maxillofacial practice. Accurate localization of these teeth is crucial for treatment planning, and Cone Beam Computed Tomography (CBCT) offers detailed 3D im...

HiSTaR: identifying spatial domains with hierarchical spatial transcriptomics variational autoencoder.

Journal of translational medicine
BACKGROUND: The development of spatial transcriptomics (ST) has enabled biologists to measure transcriptome data on an entire tissue and retain spatial information. It gives us the opportunity to fully understand the tissue microenvironment and ident...

Deep learning-based detection of murine congenital heart defects from µCT scans.

Communications biology
Micro-computed tomography (μCT) provides 3D images of congenital heart defects (CHD) in mice. However, diagnosing CHD from μCT scans is time-consuming and requires clinical expertise. Here, we present a deep learning approach to automatically segment...

Pattern and structural detection in grayscale images through the application of quantile graphs in higher-dimensional spaces.

Scientific reports
Deep Learning (DL) and Machine Learning (ML) algorithms are adept at managing and classifying a wide range of data formats, including time series, text, and images, addressing challenges in both supervised and unsupervised learning. However, the prac...

Investigating cis-regulatory elements and gene expression in multiple tomato varieties using interpretable deep learning.

TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik
Cis-regulatory elements (CREs) govern gene expression, and the relationship between non-coding regulatory elements and gene expression is inherently complex. To further elucidate how these elements influence gene expression, we refined previous model...