AIMC Topic: Deep Learning

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Motivations for Artificial Intelligence, for Deep Learning, for ALife: Mortality and Existential Risk.

Artificial life
We survey the general trajectory of artificial intelligence (AI) over the last century, in the context of influences from Artificial Life. With a broad brush, we can divide technical approaches to solving AI problems into two camps: GOFAIstic (or com...

DeepLocRNA: an interpretable deep learning model for predicting RNA subcellular localization with domain-specific transfer-learning.

Bioinformatics (Oxford, England)
MOTIVATION: Accurate prediction of RNA subcellular localization plays an important role in understanding cellular processes and functions. Although post-transcriptional processes are governed by trans-acting RNA binding proteins (RBPs) through intera...

De novo protein design-From new structures to programmable functions.

Cell
Methods from artificial intelligence (AI) trained on large datasets of sequences and structures can now "write" proteins with new shapes and molecular functions de novo, without starting from proteins found in nature. In this Perspective, I will disc...

Multitask Deep Learning for Joint Detection of Necrotizing Viral and Noninfectious Retinitis From Common Blood and Serology Test Data.

Investigative ophthalmology & visual science
PURPOSE: Necrotizing viral retinitis is a serious eye infection that requires immediate treatment to prevent permanent vision loss. Uncertain clinical suspicion can result in delayed diagnosis, inappropriate administration of corticosteroids, or repe...

StructuralDPPIV: a novel deep learning model based on atom structure for predicting dipeptidyl peptidase-IV inhibitory peptides.

Bioinformatics (Oxford, England)
MOTIVATION: Diabetes is a chronic metabolic disorder that has been a major cause of blindness, kidney failure, heart attacks, stroke, and lower limb amputation across the world. To alleviate the impact of diabetes, researchers have developed the next...

PractiCPP: a deep learning approach tailored for extremely imbalanced datasets in cell-penetrating peptide prediction.

Bioinformatics (Oxford, England)
MOTIVATION: Effective drug delivery systems are paramount in enhancing pharmaceutical outcomes, particularly through the use of cell-penetrating peptides (CPPs). These peptides are gaining prominence due to their ability to penetrate eukaryotic cells...

Artificial neural networks and deep learning.

American journal of orthodontics and dentofacial orthopedics : official publication of the American Association of Orthodontists, its constituent societies, and the American Board of Orthodontics

Deep Learning Detection of Early Retinal Peripheral Degeneration From Ultra-Widefield Fundus Photographs of Asymptomatic Young Adult (17-19 Years) Candidates to Airforce Cadets.

Translational vision science & technology
PURPOSE: Artificial intelligence (AI)-assisted ultra-widefield (UWF) fundus photographic interpretation is beneficial to improve the screening of fundus abnormalities. Therefore we constructed an AI machine-learning approach and performed preliminary...

Multi-indicator comparative evaluation for deep learning-based protein sequence design methods.

Bioinformatics (Oxford, England)
MOTIVATION: Proteins found in nature represent only a fraction of the vast space of possible proteins. Protein design presents an opportunity to explore and expand this protein landscape. Within protein design, protein sequence design plays a crucial...