AI Medical Compendium

Interpretable deep learning architectures for improving drug response prediction performance: myth or reality?

Bioinformatics (Oxford, England) Jun 1, 2023

MOTIVATION: Interpretable deep learning (DL) models that can provide biological insights, in addition to accurate predictions, are of great interest to the biomedical community. Recently, interpretable DL models that incorporate signaling pathways ha...

Neural Networks, Computer Deep Learning Random Forest

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Ensemble deep learning of embeddings for clustering multimodal single-cell omics data.

Bioinformatics (Oxford, England) Jun 1, 2023

MOTIVATION: Recent advances in multimodal single-cell omics technologies enable multiple modalities of molecular attributes, such as gene expression, chromatin accessibility, and protein abundance, to be profiled simultaneously at a global level in i...

Algorithms Deep Learning Single-Cell Analysis Cluster Analysis Chromatin

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DeepITEH: a deep learning framework for identifying tissue-specific eRNAs from the human genome.

Bioinformatics (Oxford, England) Jun 1, 2023

MOTIVATION: Enhancers are vital cis-regulatory elements that regulate gene expression. Enhancer RNAs (eRNAs), a type of long noncoding RNAs, are transcribed from enhancer regions in the genome. The tissue-specific expression of eRNAs is crucial in th...

Humans Deep Learning RNA, Long Noncoding Genome, Human Histones Transcription, Genetic Enhancer Elements, Genetic

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Mutate and observe: utilizing deep neural networks to investigate the impact of mutations on translation initiation.

Bioinformatics (Oxford, England) Jun 1, 2023

MOTIVATION: The primary regulatory step for protein synthesis is translation initiation, which makes it one of the fundamental steps in the central dogma of molecular biology. In recent years, a number of approaches relying on deep neural networks (D...

Humans Neural Networks, Computer Mutation

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GraphscoreDTA: optimized graph neural network for protein-ligand binding affinity prediction.

Bioinformatics (Oxford, England) Jun 1, 2023

MOTIVATION: Computational approaches for identifying the protein-ligand binding affinity can greatly facilitate drug discovery and development. At present, many deep learning-based models are proposed to predict the protein-ligand binding affinity an...

Neural Networks, Computer Drug Discovery Protein Binding Proteins Ligands

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Multi-modal deep learning improves grain yield prediction in wheat breeding by fusing genomics and phenomics.

Bioinformatics (Oxford, England) Jun 1, 2023

MOTIVATION: Developing new crop varieties with superior performance is highly important to ensure robust and sustainable global food security. The speed of variety development is limited by long field cycles and advanced generation selections in plan...

Phenotype Deep Learning Genomics Genotype Triticum Edible Grain Selection, Genetic Plant Breeding Phenomics

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Deep learning-based multi-functional therapeutic peptides prediction with a multi-label focal dice loss function.

Bioinformatics (Oxford, England) Jun 1, 2023

MOTIVATION: With the great number of peptide sequences produced in the postgenomic era, it is highly desirable to identify the various functions of therapeutic peptides quickly. Furthermore, it is a great challenge to predict accurate multi-functiona...

Humans Neural Networks, Computer Software Peptides Deep Learning

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Letter to the editor: Testing on external independent datasets is necessary to corroborate machine learning model improvement.

Bioinformatics (Oxford, England) Jun 1, 2023

Machine Learning

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AIONER: all-in-one scheme-based biomedical named entity recognition using deep learning.

Bioinformatics (Oxford, England) May 4, 2023

MOTIVATION: Biomedical named entity recognition (BioNER) seeks to automatically recognize biomedical entities in natural language text, serving as a necessary foundation for downstream text mining tasks and applications such as information extraction...

Software Language Deep Learning PubMed Data Mining

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Predicting the pathogenicity of missense variants using features derived from AlphaFold2.

Bioinformatics (Oxford, England) May 4, 2023

MOTIVATION: Missense variants are a frequent class of variation within the coding genome, and some of them cause Mendelian diseases. Despite advances in computational prediction, classifying missense variants into pathogenic or benign remains a major...

Humans Artificial Intelligence Computational Biology Mutation Virulence Mutation, Missense

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AI Medical Compendium

Interpretable deep learning architectures for improving drug response prediction performance: myth or reality?

Ensemble deep learning of embeddings for clustering multimodal single-cell omics data.

DeepITEH: a deep learning framework for identifying tissue-specific eRNAs from the human genome.

Mutate and observe: utilizing deep neural networks to investigate the impact of mutations on translation initiation.

GraphscoreDTA: optimized graph neural network for protein-ligand binding affinity prediction.

Multi-modal deep learning improves grain yield prediction in wheat breeding by fusing genomics and phenomics.

Deep learning-based multi-functional therapeutic peptides prediction with a multi-label focal dice loss function.

Letter to the editor: Testing on external independent datasets is necessary to corroborate machine learning model improvement.

AIONER: all-in-one scheme-based biomedical named entity recognition using deep learning.

Predicting the pathogenicity of missense variants using features derived from AlphaFold2.

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