AI Medical Compendium

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

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NPSV-deep: a deep learning method for genotyping structural variants in short read genome sequencing data.

Bioinformatics (Oxford, England)
MOTIVATION: Structural variants (SVs) play a causal role in numerous diseases but can be difficult to detect and accurately genotype (determine zygosity) with short-read genome sequencing data (SRS). Improving SV genotyping accuracy in SRS data, part...

Incorporating temporal information during feature engineering bolsters emulation of spatio-temporal emergence.

Bioinformatics (Oxford, England)
MOTIVATION: Emergent biological dynamics derive from the evolution of lower-level spatial and temporal processes. A long-standing challenge for scientists and engineers is identifying simple low-level rules that give rise to complex higher-level dyna...

CryoTransformer: a transformer model for picking protein particles from cryo-EM micrographs.

Bioinformatics (Oxford, England)
MOTIVATION: Cryo-electron microscopy (cryo-EM) is a powerful technique for determining the structures of large protein complexes. Picking single protein particles from cryo-EM micrographs (images) is a crucial step in reconstructing protein structure...

RT-Transformer: retention time prediction for metabolite annotation to assist in metabolite identification.

Bioinformatics (Oxford, England)
MOTIVATION: Liquid chromatography retention times prediction can assist in metabolite identification, which is a critical task and challenge in nontargeted metabolomics. However, different chromatographic conditions may result in different retention ...

Structured Prompt Interrogation and Recursive Extraction of Semantics (SPIRES): a method for populating knowledge bases using zero-shot learning.

Bioinformatics (Oxford, England)
MOTIVATION: Creating knowledge bases and ontologies is a time consuming task that relies on manual curation. AI/NLP approaches can assist expert curators in populating these knowledge bases, but current approaches rely on extensive training data, and...

EvoAug-TF: extending evolution-inspired data augmentations for genomic deep learning to TensorFlow.

Bioinformatics (Oxford, England)
SUMMARY: Deep neural networks (DNNs) have been widely applied to predict the molecular functions of the non-coding genome. DNNs are data hungry and thus require many training examples to fit data well. However, functional genomics experiments typical...

Insights into the inner workings of transformer models for protein function prediction.

Bioinformatics (Oxford, England)
MOTIVATION: We explored how explainable artificial intelligence (XAI) can help to shed light into the inner workings of neural networks for protein function prediction, by extending the widely used XAI method of integrated gradients such that latent ...

Geometry-complete perceptron networks for 3D molecular graphs.

Bioinformatics (Oxford, England)
MOTIVATION: The field of geometric deep learning has recently had a profound impact on several scientific domains such as protein structure prediction and design, leading to methodological advancements within and outside of the realm of traditional m...

scSemiGCN: boosting cell-type annotation from noise-resistant graph neural networks with extremely limited supervision.

Bioinformatics (Oxford, England)
MOTIVATION: Cell-type annotation is fundamental in revealing cell heterogeneity for single-cell data analysis. Although a host of works have been developed, the low signal-to-noise-ratio single-cell RNA-sequencing data that suffers from batch effects...

DeepLocRNA: an interpretable deep learning model for predicting RNA subcellular localization with domain-specific transfer-learning.

Bioinformatics (Oxford, England)
MOTIVATION: Accurate prediction of RNA subcellular localization plays an important role in understanding cellular processes and functions. Although post-transcriptional processes are governed by trans-acting RNA binding proteins (RBPs) through intera...