AIMC Topic: Deep Learning

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Multimodal Multitask Learning for Predicting Depression Severity and Suicide Risk Using Pretrained Audio and Text Embeddings: Methodology Development and Application.

JMIR medical informatics
BACKGROUND: Depression is a critical psychological disorder necessitating urgent assessment and treatment, given its strong association with increased suicide risk (SR). Effective management hinges on promptly identifying individuals with high depres...

OpenSpliceAI provides an efficient modular implementation of SpliceAI enabling easy retraining across nonhuman species.

eLife
The SpliceAI deep learning system is currently one of the most accurate methods for identifying splicing signals directly from DNA sequences. However, its utility is limited by its reliance on older software frameworks and human-centric training data...

CS-Net: convolutional spider neural network for surface-EMG-based hybrid gesture recognition.

Journal of neural engineering
In this paper, we propose a novel neural network architecture, the convolutional spider neural network (CS-Net), combined with a transfer learning (TL) strategy, to classify hybrid gestures that integrate wrist postures and hand movements.The CS-Net ...

Enhanced audience sentiment analysis in IoT-integrated metaverse media communication.

PloS one
The convergence of Metaverse technologies, Internet of Things (IoT), and consumer electronics has given rise to an imperative need for scalable, real-time sentiment analysis that can process heterogeneous, high-velocity media flows. The traditional a...

Artificial intelligence (AI)-Enabled behavioral health application for college students: Pilot study protocol.

PloS one
Given the prevalence of depression among young adults, particularly those aged 18-25, this study aims to address a critical need in higher education institutions for proactive, private, automated mental health self-awareness. This study protocol outl...

Integrative Deep Learning of Genomic and Clinical Data for Predicting Treatment Response in Newly Diagnosed Epilepsy.

Neurology
BACKGROUND AND OBJECTIVES: Epilepsy is a common neurologic disorder. Although antiseizure medications (ASMs) are the first-line treatment, identifying the most effective ASM for each individual remains a trial-and-error process. Genetic variation may...

Comparing radiomics, deep learning, and fusion models for predicting occult pleural dissemination in patients with non-small cell lung cancer: a retrospective multicenter study.

BMC cancer
BACKGROUND: Occult pleural dissemination (PD) in non-small cell lung cancer (NSCLC) patients is likely to be missed on computed tomography (CT) scans, associated with poor survival, and generally contraindicated for radical surgery. This study aimed ...

DeepBPred: blood-brain barrier peptide predictor using stacked BiGRU model with novel features.

BMC biology
BACKGROUND: The blood-brain barrier (B) acts as a membrane that is a major concern in treating central nervous system (CNS) disorders. The B penetrating peptides (BPPs) play a significant role in delivering therapeutic drugs to a wide range of disord...

Deep learning-based bacterial foraging optimization algorithm to improve digital mammography-based breast cancer detection.

Scientific reports
This study focuses on improving the detection of breast cancer at an early stage. The common approach for diagnosing breast cancer is mammography, but it is quite tedious as it is subject to subjective analysis. To address these challenges, the resea...

Intra- and inter-field strength reproducibility of deep-learning based real-time cardiac MRI cine sequences with breath hold and in free breathing.

Scientific reports
To assess intra- and inter-field strength reproducibility of volumetric parameters using deep-learning-based real-time cardiac cine MRI during breath-hold (BH) and free-breathing (FB). In this prospective single-center study, 56 healthy adults underw...