AI Medical Compendium

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

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SuPreMo: a computational tool for streamlining in silico perturbation using sequence-based predictive models.

Bioinformatics (Oxford, England)
SUMMARY: The increasing development of sequence-based machine learning models has raised the demand for manipulating sequences for this application. However, existing approaches to edit and evaluate genome sequences using models have limitations, suc...

Genotype sampling for deep-learning assisted experimental mapping of a combinatorially complete fitness landscape.

Bioinformatics (Oxford, England)
MOTIVATION: Experimental characterization of fitness landscapes, which map genotypes onto fitness, is important for both evolutionary biology and protein engineering. It faces a fundamental obstacle in the astronomical number of genotypes whose fitne...

Chainsaw: protein domain segmentation with fully convolutional neural networks.

Bioinformatics (Oxford, England)
MOTIVATION: Protein domains are fundamental units of protein structure and play a pivotal role in understanding folding, function, evolution, and design. The advent of accurate structure prediction techniques has resulted in an influx of new structur...

Molecular property prediction based on graph structure learning.

Bioinformatics (Oxford, England)
MOTIVATION: Molecular property prediction (MPP) is a fundamental but challenging task in the computer-aided drug discovery process. More and more recent works employ different graph-based models for MPP, which have achieved considerable progress in i...

DeepAVP-TPPred: identification of antiviral peptides using transformed image-based localized descriptors and binary tree growth algorithm.

Bioinformatics (Oxford, England)
MOTIVATION: Despite the extensive manufacturing of antiviral drugs and vaccination, viral infections continue to be a major human ailment. Antiviral peptides (AVPs) have emerged as potential candidates in the pursuit of novel antiviral drugs. These p...

Hi-GeoMVP: a hierarchical geometry-enhanced deep learning model for drug response prediction.

Bioinformatics (Oxford, England)
MOTIVATION: Personalized cancer treatments require accurate drug response predictions. Existing deep learning methods show promise but higher accuracy is needed to serve the purpose of precision medicine. The prediction accuracy can be improved with ...

Effect of tokenization on transformers for biological sequences.

Bioinformatics (Oxford, England)
MOTIVATION: Deep-learning models are transforming biological research, including many bioinformatics and comparative genomics algorithms, such as sequence alignments, phylogenetic tree inference, and automatic classification of protein functions. Amo...

Coding genomes with gapped pattern graph convolutional network.

Bioinformatics (Oxford, England)
MOTIVATION: Genome sequencing technologies reveal a huge amount of genomic sequences. Neural network-based methods can be prime candidates for retrieving insights from these sequences because of their applicability to large and diverse datasets. Howe...

Improving the performance of supervised deep learning for regulatory genomics using phylogenetic augmentation.

Bioinformatics (Oxford, England)
MOTIVATION: Supervised deep learning is used to model the complex relationship between genomic sequence and regulatory function. Understanding how these models make predictions can provide biological insight into regulatory functions. Given the compl...

ViNe-Seg: deep-learning-assisted segmentation of visible neurons and subsequent analysis embedded in a graphical user interface.

Bioinformatics (Oxford, England)
SUMMARY: Segmentation of neural somata is a crucial and usually the most time-consuming step in the analysis of optical functional imaging of neuronal microcircuits. In recent years, multiple auto-segmentation tools have been developed to improve the...