AIMC Topic: Deep Learning

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A Deep Learning Framework for Identifying Essential Proteins by Integrating Multiple Types of Biological Information.

IEEE/ACM transactions on computational biology and bioinformatics
Computational methods including centrality and machine learning-based methods have been proposed to identify essential proteins for understanding the minimum requirements of the survival and evolution of a cell. In centrality methods, researchers are...

Protein structure search to support the development of protein structure prediction methods.

Proteins
Protein structure prediction is a long-standing unsolved problem in molecular biology that has seen renewed interest with the recent success of deep learning with AlphaFold at CASP13. While developing and evaluating protein structure prediction metho...

A deep learning approach using synthetic images for segmenting and estimating 3D orientation of nanoparticles in EM images.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Nanoparticles present properties that can be applied to a wide range of fields such as biomedicine, electronics or optics. The type of properties depends on several characteristics, being some of them related with the partic...

Using weak supervision and deep learning to classify clinical notes for identification of current suicidal ideation.

Journal of psychiatric research
Mental health concerns, such as suicidal thoughts, are frequently documented by providers in clinical notes, as opposed to structured coded data. In this study, we evaluated weakly supervised methods for detecting "current" suicidal ideation from uns...

Use of a Commercially Available Deep Learning Algorithm to Measure the Solid Portions of Lung Cancer Manifesting as Subsolid Lesions at CT: Comparisons with Radiologists and Invasive Component Size at Pathologic Examination.

Radiology
Background The solid portion size of lung cancer lesions manifesting as subsolid lesions is key in their management, but the automatic measurement of such lesions by means of a deep learning (DL) algorithm needs evaluation. Purpose To evaluate the pe...

A Deep Learning Algorithm for High-Dimensional Exploratory Item Factor Analysis.

Psychometrika
Marginal maximum likelihood (MML) estimation is the preferred approach to fitting item response theory models in psychometrics due to the MML estimator's consistency, normality, and efficiency as the sample size tends to infinity. However, state-of-t...

Hidden Variables in Deep Learning Digital Pathology and Their Potential to Cause Batch Effects: Prediction Model Study.

Journal of medical Internet research
BACKGROUND: An increasing number of studies within digital pathology show the potential of artificial intelligence (AI) to diagnose cancer using histological whole slide images, which requires large and diverse data sets. While diversification may re...

Interpretation and visualization techniques for deep learning models in medical imaging.

Physics in medicine and biology
Deep learning (DL) approaches to medical image analysis tasks have recently become popular; however, they suffer from a lack of human interpretability critical for both increasing understanding of the methods' operation and enabling clinical translat...

Combined Spiral Transformation and Model-Driven Multi-Modal Deep Learning Scheme for Automatic Prediction of TP53 Mutation in Pancreatic Cancer.

IEEE transactions on medical imaging
Pancreatic cancer is a malignant form of cancer with one of the worst prognoses. The poor prognosis and resistance to therapeutic modalities have been linked to TP53 mutation. Pathological examinations, such as biopsies, cannot be frequently performe...

Weakly Supervised Deep Learning-Based Optical Coherence Tomography Angiography.

IEEE transactions on medical imaging
Optical coherence tomography angiography (OCTA) is a promising imaging modality for microvasculature studies. Deep learning networks have been widely applied in the field of OCTA reconstruction, benefiting from its powerful mapping capability among i...