AI Medical Compendium

Explore the latest research on artificial intelligence and machine learning in medicine.

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LncLocFormer: a Transformer-based deep learning model for multi-label lncRNA subcellular localization prediction by using localization-specific attention mechanism.

Bioinformatics (Oxford, England)
MOTIVATION: There is mounting evidence that the subcellular localization of lncRNAs can provide valuable insights into their biological functions. In the real world of transcriptomes, lncRNAs are usually localized in multiple subcellular localization...

survex: an R package for explaining machine learning survival models.

Bioinformatics (Oxford, England)
SUMMARY: Due to their flexibility and superior performance, machine learning models frequently complement and outperform traditional statistical survival models. However, their widespread adoption is hindered by a lack of user-friendly tools to expla...

ProteinMAE: masked autoencoder for protein surface self-supervised learning.

Bioinformatics (Oxford, England)
SUMMARY: The biological functions of proteins are determined by the chemical and geometric properties of their surfaces. Recently, with the booming progress of deep learning, a series of learning-based surface descriptors have been proposed and achie...

deepFPlearn +: enhancing toxicity prediction across the chemical universe using graph neural networks.

Bioinformatics (Oxford, England)
SUMMARY: Sophisticated approaches for the in silico prediction of toxicity are required to support the risk assessment of chemicals. The number of chemicals on the global chemical market and the speed of chemical innovation stand in massive contrast ...

PLM-ARG: antibiotic resistance gene identification using a pretrained protein language model.

Bioinformatics (Oxford, England)
MOTIVATION: Antibiotic resistance presents a formidable global challenge to public health and the environment. While considerable endeavors have been dedicated to identify antibiotic resistance genes (ARGs) for assessing the threat of antibiotic resi...

Ensemble-GNN: federated ensemble learning with graph neural networks for disease module discovery and classification.

Bioinformatics (Oxford, England)
SUMMARY: Federated learning enables collaboration in medicine, where data is scattered across multiple centers without the need to aggregate the data in a central cloud. While, in general, machine learning models can be applied to a wide range of dat...

ReGeNNe: genetic pathway-based deep neural network using canonical correlation regularizer for disease prediction.

Bioinformatics (Oxford, England)
MOTIVATION: Common human diseases result from the interplay of genes and their biologically associated pathways. Genetic pathway analyses provide more biological insight as compared to conventional gene-based analysis. In this article, we propose a f...

PiDeeL: metabolic pathway-informed deep learning model for survival analysis and pathological classification of gliomas.

Bioinformatics (Oxford, England)
MOTIVATION: Online assessment of tumor characteristics during surgery is important and has the potential to establish an intra-operative surgeon feedback mechanism. With the availability of such feedback, surgeons could decide to be more liberal or c...

Exploiting the structure of biochemical pathways to investigate dynamical properties with neural networks for graphs.

Bioinformatics (Oxford, England)
MOTIVATION: Dynamical properties of biochemical pathways (BPs) help in understanding the functioning of living cells. Their in silico assessment requires simulating a dynamical system with a large number of parameters such as kinetic constants and sp...

SSLpheno: a self-supervised learning approach for gene-phenotype association prediction using protein-protein interactions and gene ontology data.

Bioinformatics (Oxford, England)
MOTIVATION: Medical genomics faces significant challenges in interpreting disease phenotype and genetic heterogeneity. Despite the establishment of standardized disease phenotype databases, computational methods for predicting gene-phenotype associat...