AIMC Topic: Deep Learning

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Predicting RNA structure and dynamics with deep learning and solution scattering.

Biophysical journal
Advanced deep learning and statistical methods can predict structural models for RNA molecules. However, RNAs are flexible, and it remains difficult to describe their macromolecular conformations in solutions where varying conditions can induce confo...

Deep Learning-Driven Insights into Enzyme-Substrate Interaction Discovery.

Journal of chemical information and modeling
Enzymes are ubiquitous catalysts with enormous application potential in biomedicine, green chemistry, and biotechnology. However, accurately predicting whether a molecule serves as a substrate for a specific enzyme, especially for novel entities, rem...

Epileptic seizure detection in EEG signals via an enhanced hybrid CNN with an integrated attention mechanism.

Mathematical biosciences and engineering : MBE
Epileptic seizures, a prevalent neurological condition, necessitate precise and prompt identification for optimal care. Nevertheless, the intricate characteristics of electroencephalography (EEG) signals, noise, and the want for real-time analysis re...

12 lead surface ECGs as a surrogate of atrial electrical remodeling - a deep learning based approach.

Journal of electrocardiology
BACKGROUND AND PURPOSE: Atrial fibrillation (AF), a common arrhythmia, is linked with atrial electrical and structural changes, notably low voltage areas (LVAs) which are associated with poor ablation outcomes and increased thromboembolic risk. This ...

Radiomics and deep learning models for glioblastoma treatment outcome prediction based on tumor invasion modeling.

Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB)
PURPOSE: We investigate the feasibility of using a biophysically guided approach for delineating the Clinical Target Volume (CTV) in Glioblastoma Multiforme (GBM) by evaluating its impact on the treatment outcomes, specifically Overall Survival (OS) ...

AVP-GPT2: A Transformer-Powered Platform for De Novo Generation, Screening, and Explanation of Antiviral Peptides.

Viruses
Human respiratory syncytial virus (RSV) remains a significant global health threat, particularly for vulnerable populations. Despite extensive research, effective antiviral therapies are still limited. To address this urgent need, we present AVP-GPT2...

Real-Time PPG-Based Biometric Identification: Advancing Security with 2D Gram Matrices and Deep Learning Models.

Sensors (Basel, Switzerland)
The integration of liveness detection into biometric systems is crucial for countering spoofing attacks and enhancing security. This study investigates the efficacy of photoplethysmography (PPG) signals, which offer distinct advantages over tradition...

Prior-FOVNet: A Multimodal Deep Learning Framework for Megavoltage Computed Tomography Truncation Artifact Correction and Field-of-View Extension.

Sensors (Basel, Switzerland)
Megavoltage computed tomography (MVCT) plays a crucial role in patient positioning and dose reconstruction during tomotherapy. However, due to the limited scan field of view (sFOV), the entire cross-section of certain patients may not be fully covere...

Learnable color space conversion and fusion for stain normalization in pathology images.

Medical image analysis
Variations in hue and contrast are common in H&E-stained pathology images due to differences in slide preparation across various institutions. Such stain variations, while not affecting pathologists much in diagnosing the biopsy, pose significant cha...

Predicting lymph node metastasis in thyroid cancer: systematic review and meta-analysis on the CT/MRI-based radiomics and deep learning models.

Clinical imaging
BACKGROUND: Thyroid cancer, a common endocrine malignancy, has seen increasing incidence, making lymph node metastasis (LNM) a critical factor for recurrence and survival. Radiomics and deep learning (DL) advancements offer the potential for improved...