AIMC Topic: Humans

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Novel deep learning-based survival prediction for oral cancer by analyzing tumor-infiltrating lymphocyte profiles through CIBERSORT.

Oncoimmunology
The tumor microenvironment (TME) within mucosal neoplastic tissue in oral cancer (ORCA) is greatly influenced by tumor-infiltrating lymphocytes (TILs). Here, a clustering method was performed using CIBERSORT profiles of ORCA data that were filtered f...

Edge-Sensitive Left Ventricle Segmentation Using Deep Reinforcement Learning.

Sensors (Basel, Switzerland)
Deep reinforcement learning (DRL) has been utilized in numerous computer vision tasks, such as object detection, autonomous driving, etc. However, relatively few DRL methods have been proposed in the area of image segmentation, particularly in left v...

The Prediction of Body Mass Index from Negative Affectivity through Machine Learning: A Confirmatory Study.

Sensors (Basel, Switzerland)
This study investigates on the relationship between affect-related psychological variables and Body Mass Index (BMI). We have utilized a novel method based on machine learning (ML) algorithms that forecast unobserved BMI values based on psychological...

Development and evaluation of a deep neural network for histologic classification of renal cell carcinoma on biopsy and surgical resection slides.

Scientific reports
Renal cell carcinoma (RCC) is the most common renal cancer in adults. The histopathologic classification of RCC is essential for diagnosis, prognosis, and management of patients. Reorganization and classification of complex histologic patterns of RCC...

Interpretable Conditional Recurrent Neural Network for Weight Change Prediction: Algorithm Development and Validation Study.

JMIR mHealth and uHealth
BACKGROUND: In recent years, mobile-based interventions have received more attention as an alternative to on-site obesity management. Despite increased mobile interventions for obesity, there are lost opportunities to achieve better outcomes due to t...

Deep learning-based six-type classifier for lung cancer and mimics from histopathological whole slide images: a retrospective study.

BMC medicine
BACKGROUND: Targeted therapy and immunotherapy put forward higher demands for accurate lung cancer classification, as well as benign versus malignant disease discrimination. Digital whole slide images (WSIs) witnessed the transition from traditional ...

Predicting antimicrobial mechanism-of-action from transcriptomes: A generalizable explainable artificial intelligence approach.

PLoS computational biology
To better combat the expansion of antibiotic resistance in pathogens, new compounds, particularly those with novel mechanisms-of-action [MOA], represent a major research priority in biomedical science. However, rediscovery of known antibiotics demons...

Using the antibody-antigen binding interface to train image-based deep neural networks for antibody-epitope classification.

PLoS computational biology
High-throughput B-cell sequencing has opened up new avenues for investigating complex mechanisms underlying our adaptive immune response. These technological advances drive data generation and the need to mine and analyze the information contained in...

Graph of graphs analysis for multiplexed data with application to imaging mass cytometry.

PLoS computational biology
Imaging Mass Cytometry (IMC) combines laser ablation and mass spectrometry to quantitate metal-conjugated primary antibodies incubated in intact tumor tissue slides. This strategy allows spatially-resolved multiplexing of dozens of simultaneous prote...

Using machine learning approaches for multi-omics data analysis: A review.

Biotechnology advances
With the development of modern high-throughput omic measurement platforms, it has become essential for biomedical studies to undertake an integrative (combined) approach to fully utilise these data to gain insights into biological systems. Data from ...