AIMC Topic: Deep Learning

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Accurate single-molecule spot detection for image-based spatial transcriptomics with weakly supervised deep learning.

Cell systems
Image-based spatial transcriptomics methods enable transcriptome-scale gene expression measurements with spatial information but require complex, manually tuned analysis pipelines. We present Polaris, an analysis pipeline for image-based spatial tran...

Prediction of drug-protein interaction based on dual channel neural networks with attention mechanism.

Briefings in functional genomics
The precise identification of drug-protein inter action (DPI) can significantly speed up the drug discovery process. Bioassay methods are time-consuming and expensive to screen for each pair of drug proteins. Machine-learning-based methods cannot acc...

Predicting gastric cancer tumor mutational burden from histopathological images using multimodal deep learning.

Briefings in functional genomics
Tumor mutational burden (TMB) is a significant predictive biomarker for selecting patients that may benefit from immune checkpoint inhibitor therapy. Whole exome sequencing is a common method for measuring TMB; however, its clinical application is li...

Omics-based deep learning approaches for lung cancer decision-making and therapeutics development.

Briefings in functional genomics
Lung cancer has been the most common and the leading cause of cancer deaths globally. Besides clinicopathological observations and traditional molecular tests, the advent of robust and scalable techniques for nucleic acid analysis has revolutionized ...

Artificial intelligence in detection of small bowel lesions and their bleeding risk: A new step forward.

World journal of gastroenterology
The present letter to the editor is related to the study with the title "Automatic detection of small bowel (SB) lesions with different bleeding risk based on deep learning models". Capsule endoscopy (CE) is the main tool to assess SB diseases but it...

Analysis of 3D pathology samples using weakly supervised AI.

Cell
Human tissue, which is inherently three-dimensional (3D), is traditionally examined through standard-of-care histopathology as limited two-dimensional (2D) cross-sections that can insufficiently represent the tissue due to sampling bias. To holistica...

Deep learning path-like collective variable for enhanced sampling molecular dynamics.

The Journal of chemical physics
Several enhanced sampling techniques rely on the definition of collective variables to effectively explore free energy landscapes. The existing variables that describe the progression along a reactive pathway offer an elegant solution but face a numb...

ATN-Res2Unet: an advanced deep learning network for the elimination of saturation artifacts in endoscopy optical coherence tomography.

Optics express
Endoscopic optical coherence tomography (OCT) possesses the capability to non-invasively image internal lumens; however, it is susceptible to saturation artifacts arising from robust reflective structures. In this study, we introduce an innovative de...

Deep learning-based spinal canal segmentation of computed tomography image for disease diagnosis: A proposed system for spinal stenosis diagnosis.

Medicine
BACKGROUND: Lumbar disc herniation was regarded as an age-related degenerative disease. Nevertheless, emerging reports highlight a discernible shift, illustrating the prevalence of these conditions among younger individuals.

Genotype sampling for deep-learning assisted experimental mapping of a combinatorially complete fitness landscape.

Bioinformatics (Oxford, England)
MOTIVATION: Experimental characterization of fitness landscapes, which map genotypes onto fitness, is important for both evolutionary biology and protein engineering. It faces a fundamental obstacle in the astronomical number of genotypes whose fitne...