AIMC Topic: Deep Learning

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NMRformer: A Transformer-Based Deep Learning Framework for Peak Assignment in 1D H NMR Spectroscopy.

Analytical chemistry
Metabolite identification from 1D H NMR spectra is a major challenge in NMR-based metabolomics. This study introduces NMRformer, a Transformer-based deep learning framework for accurate peak assignment and metabolite identification in 1D H NMR spectr...

Non-invasive blood glucose monitoring using PPG signals with various deep learning models and implementation using TinyML.

Scientific reports
Accurate and continuous blood glucose monitoring is essential for effective diabetes management, yet traditional finger pricking methods are often inconvenient and painful. To address this issue, photoplethysmography (PPG) presents a promising non-in...

Weakly supervised deep learning-based classification for histopathology of gliomas: a single center experience.

Scientific reports
Multiple artificial intelligence systems have been created to facilitate accurate and prompt histopathological diagnosis of tumors using hematoxylin-eosin-stained slides. We aimed to investigate whether weakly supervised deep learning can aid in glio...

Enhancing the performance of SSVEP-based BCIs by combining task-related component analysis and deep neural network.

Scientific reports
Steady-State Visually Evoked Potential (SSVEP) signals can be decoded by either a traditional machine learning algorithm or a deep learning network. Combining the two methods is expected to enhance the performance of an SSVEP-based brain-computer int...

The development of an efficient artificial intelligence-based classification approach for colorectal cancer response to radiochemotherapy: deep learning vs. machine learning.

Scientific reports
Colorectal cancer (CRC) is a form of cancer that impacts both the rectum and colon. Typically, it begins with a small abnormal growth known as a polyp, which can either be non-cancerous or cancerous. Therefore, early detection of colorectal cancer as...

Drug molecular representations for drug response predictions: a comprehensive investigation via machine learning methods.

Scientific reports
The integration of drug molecular representations into predictive models for Drug Response Prediction (DRP) is a standard procedure in pharmaceutical research and development. However, the comparative effectiveness of combining these representations ...

A deep learning-based multi-view approach to automatic 3D landmarking and deformity assessment of lower limb.

Scientific reports
Anatomical Landmark detection in CT-Scan images is widely used in the identification of skeletal disorders. However, the traditional process of manually detecting anatomical landmarks, especially in three dimensions, is both time-consuming and prone ...

pACP-HybDeep: predicting anticancer peptides using binary tree growth based transformer and structural feature encoding with deep-hybrid learning.

Scientific reports
Worldwide, Cancer remains a significant health concern due to its high mortality rates. Despite numerous traditional therapies and wet-laboratory methods for treating cancer-affected cells, these approaches often face limitations, including high cost...

DNA promoter task-oriented dictionary mining and prediction model based on natural language technology.

Scientific reports
Promoters are essential DNA sequences that initiate transcription and regulate gene expression. Precisely identifying promoter sites is crucial for deciphering gene expression patterns and the roles of gene regulatory networks. Recent advancements in...