AIMC Topic: Deep Learning

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A model for epileptic EEG detection and recognition based on Multi-Attention mechanism and Spatiotemporal.

Scientific reports
In the field of neuroscience, epilepsy is a chronic non-communicable brain disease that affects approximately 50 million people worldwide. Electroencephalography (EEG) has become a key tool in detecting and characterizing human neurological diseases ...

Deep indel mutagenesis reveals the regulatory and modulatory architecture of alternative exon splicing.

Nature communications
While altered pre-mRNA splicing is a frequent mechanism by which genetic variants cause disease, the regulatory architecture of human exons remains poorly understood. Antisense oligonucleotides (AONs) that target pre-mRNA splicing have been approved ...

External validation of deep learning-derived 18F-FDG PET/CT delta biomarkers for loco-regional control in head and neck cancer.

Acta oncologica (Stockholm, Sweden)
BACKGROUND AND PURPOSE: Delta biomarkers that reflect changes in tumour burden over time can support personalised follow-up in head and neck cancer. However, their clinical use can be limited by the need for manual image segmentation. This study exte...

Graph Learning-Based Scoring of RNA-Protein Complex Structures.

Journal of chemical theory and computation
Development of suitable scoring functions is essential for the prediction of RNA-protein complex structures. Conventional statistical potential-based scoring functions suffered from deficiencies in handling conformational flexibility. The recent appl...

From AI-Driven Sequence Generation to Molecular Simulation: A Comprehensive Framework for Antimicrobial Peptide Discovery.

Journal of chemical information and modeling
Antimicrobial Peptides (AMPs) are a promising strategy to address bacterial resistance, yet only a limited number have advanced to clinical trials. Recent advances in deep learning provide new opportunities for AMP design. Here, we propose an integra...

Fusion model integrating multi-sequence MRI radiomics and habitat imaging for predicting pathological complete response in breast cancer treated with neoadjuvant therapy.

Cancer imaging : the official publication of the International Cancer Imaging Society
BACKGROUND: This study aimed to develop a predictive model integrating multi-sequence MRI radiomics, deep learning features, and habitat imaging to forecast pathological complete response (pCR) in breast cancer patients undergoing neoadjuvant therapy...

YModPred: an interpretable prediction method for multi-type RNA modification sites in S. cerevisiae based on deep learning.

BMC biology
BACKGROUND: RNA post-transcriptional modifications involve the addition of chemical groups to RNA molecules or alterations to their local structure. These modifications can change RNA base pairing, affect thermal stability, and influence RNA folding,...

Multimodal feature distinguishing and deep learning approach to detect lung disease from MRI images.

Scientific reports
Precise and early detection and diagnosis of lung diseases reduce the severity of life risk and further spread of infections in patients. Computer-based image processing techniques utilize magnetic resonance imaging (MRI) as input for computing, dete...

Optimized hierarchical CLSTM model for sentiment classification of tweets using boosted killer whale predation strategy.

Scientific reports
Opinion mining is more challenging than it was before because of all the user-generated material on social media. People use Twitter (X) to gather opinions on products, advancements, and laws. Sentiment Analysis (SA) examines people's thoughts, feeli...

Personalized health monitoring using explainable AI: bridging trust in predictive healthcare.

Scientific reports
AI has propelled the potential for moving toward personalized health and early prediction of diseases. Unfortunately, a significant limitation of many of these deep learning models is that they are not interpretable, restricting their clinical utilit...