AI Medical Compendium

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

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Deep learning for survival analysis in breast cancer with whole slide image data.

Bioinformatics (Oxford, England)
MOTIVATION: Whole slide tissue images contain detailed data on the sub-cellular structure of cancer. Quantitative analyses of this data can lead to novel biomarkers for better cancer diagnosis and prognosis and can improve our understanding of cancer...

Effective drug-target interaction prediction with mutual interaction neural network.

Bioinformatics (Oxford, England)
MOTIVATION: Accurately predicting drug-target interaction (DTI) is a crucial step to drug discovery. Recently, deep learning techniques have been widely used for DTI prediction and achieved significant performance improvement. One challenge in buildi...

Prior knowledge facilitates low homologous protein secondary structure prediction with DSM distillation.

Bioinformatics (Oxford, England)
MOTIVATION: Protein secondary structure prediction (PSSP) is one of the fundamental and challenging problems in the field of computational biology. Accurate PSSP relies on sufficient homologous protein sequences to build the multiple sequence alignme...

DeepLUCIA: predicting tissue-specific chromatin loops using Deep Learning-based Universal Chromatin Interaction Annotator.

Bioinformatics (Oxford, England)
MOTIVATION: The importance of chromatin loops in gene regulation is broadly accepted. There are mainly two approaches to predict chromatin loops: transcription factor (TF) binding-dependent approach and genomic variation-based approach. However, neit...

transferGWAS: GWAS of images using deep transfer learning.

Bioinformatics (Oxford, England)
MOTIVATION: Medical images can provide rich information about diseases and their biology. However, investigating their association with genetic variation requires non-standard methods. We propose transferGWAS, a novel approach to perform genome-wide ...

Effector-GAN: prediction of fungal effector proteins based on pretrained deep representation learning methods and generative adversarial networks.

Bioinformatics (Oxford, England)
MOTIVATION: Phytopathogenic fungi secrete effector proteins to subvert host defenses and facilitate infection. Systematic analysis and prediction of candidate fungal effector proteins are crucial for experimental validation and biological control of ...

Conditional generative modeling for de novo protein design with hierarchical functions.

Bioinformatics (Oxford, England)
MOTIVATION: Protein design has become increasingly important for medical and biotechnological applications. Because of the complex mechanisms underlying protein formation, the creation of a novel protein requires tedious and time-consuming computatio...

HelixADMET: a robust and endpoint extensible ADMET system incorporating self-supervised knowledge transfer.

Bioinformatics (Oxford, England)
MOTIVATION: Accurate ADMET (an abbreviation for 'absorption, distribution, metabolism, excretion and toxicity') predictions can efficiently screen out undesirable drug candidates in the early stage of drug discovery. In recent years, multiple compreh...

Predicting protein-peptide binding residues via interpretable deep learning.

Bioinformatics (Oxford, England)
SUMMARY: Identifying the protein-peptide binding residues is fundamentally important to understand the mechanisms of protein functions and explore drug discovery. Although several computational methods have been developed, most of them highly rely on...

Powerful molecule generation with simple ConvNet.

Bioinformatics (Oxford, England)
MOTIVATION: Automated molecule generation is a crucial step in in-silico drug discovery. Graph-based generation algorithms have seen significant progress over recent years. However, they are often complex to implement, hard to train and can under-per...