Daniel Buchan
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View article: An extensive evaluation of single-cell RNA-Seq contrastive learning generative networks for intrinsic cell-types distribution estimation
An extensive evaluation of single-cell RNA-Seq contrastive learning generative networks for intrinsic cell-types distribution estimation Open
Contrastive learning has already been widely used to handle single-cell RNA-Seq data due to its outstanding performance in transforming original data distributions into hypersphere feature spaces. In this work, we conduct a large-scale emp…
View article: Less is more: improving cell-type identification with augmentation-free single-cell RNA-Seq contrastive learning
Less is more: improving cell-type identification with augmentation-free single-cell RNA-Seq contrastive learning Open
Motivation Cell-type identification is one of the most important tasks in single-cell RNA Sequencing (scRNA-Seq) analysis. Recent research has revealed contrastive learning’s great potential in handling multiple cell-type identification ta…
View article: Foldclass and Merizo-search: scalable structural similarity search for single- and multi-domain proteins using geometric learning
Foldclass and Merizo-search: scalable structural similarity search for single- and multi-domain proteins using geometric learning Open
Motivation The availability of very large numbers of protein structures from accurate computational methods poses new challenges in storing, searching and detecting relationships between these structures. In particular, the new-found abund…
View article: Characterising Protein Search Drift using exhaustive protein search and Alphafold2
Characterising Protein Search Drift using exhaustive protein search and Alphafold2 Open
In this paper we present the first exhaustive analysis of iterative protein search drift and show how such results may impact downstream modelling. Assembling and extracting evolutionary information from families of related proteins is a c…
View article: Designing minimal <i>E. coli</i> genomes using variational autoencoders
Designing minimal <i>E. coli</i> genomes using variational autoencoders Open
Designing minimal bacterial genomes remains a key challenge in synthetic biology. There is currently a lack of efficient tools for the rapid generation of streamlined bacterial genomes, limiting research in this area. Here, using a pangeno…
View article: Deep learning for the PSIPRED Protein Analysis Workbench
Deep learning for the PSIPRED Protein Analysis Workbench Open
The PSIRED Workbench is a long established and popular bioinformatics web service offering a wide range of machine learning based analyses for characterizing protein structure and function. In this paper we provide an update of the recent …
View article: Improving cell type identification with Gaussian noise-augmented single-cell RNA-seq contrastive learning
Improving cell type identification with Gaussian noise-augmented single-cell RNA-seq contrastive learning Open
Cell type identification is an important task for single-cell RNA-sequencing (scRNA-seq) data analysis. Many prediction methods have recently been proposed, but the predictive accuracy of difficult cell type identification tasks is still l…
View article: Improving cell type identification with Gaussian noise-augmented single-cell RNA-seq contrastive learning
Improving cell type identification with Gaussian noise-augmented single-cell RNA-seq contrastive learning Open
The benchmark datasets used to evaluate Gaussian noise augmentation-based scRNA-seq contrastive learning (GsRCL) against scRNA-seq cell-type identification tasks.
View article: Improving cell type identification with Gaussian noise-augmented single-cell RNA-seq contrastive learning
Improving cell type identification with Gaussian noise-augmented single-cell RNA-seq contrastive learning Open
The benchmark datasets used to evaluate Gaussian noise augmentation-based scRNA-seq contrastive learning (GsRCL) against scRNA-seq cell-type identification tasks.
View article: Improving cell-type identification with Gaussian noise-augmented single-cell RNA-seq contrastive learning
Improving cell-type identification with Gaussian noise-augmented single-cell RNA-seq contrastive learning Open
Cell-type identification is an important task for single-cell RNA-seq (scRNA-seq) data analysis. In this work, we proposed a novel Gaussian noise augmented scRNA-seq contrastive learning framework (GsRCL) to learn a type of discriminative …
View article: Locked Up and Locked Down: How the Covid-19 Pandemic has Impacted the Mental Health of Male Prisoners and Support Staff
Locked Up and Locked Down: How the Covid-19 Pandemic has Impacted the Mental Health of Male Prisoners and Support Staff Open
Background: The impact of the Covid-19 pandemic on prisons across the world has been of much concern due to the increased risk of virus spread among a particularly vulnerable population. Efforts made to prevent spread of the virus have res…
View article: Genome3D: integrating a collaborative data pipeline to expand the depth and breadth of consensus protein structure annotation
Genome3D: integrating a collaborative data pipeline to expand the depth and breadth of consensus protein structure annotation Open
Genome3D (https://www.genome3d.eu) is a freely available resource that provides consensus structural annotations for representative protein sequences taken from a selection of model organisms. Since the last NAR update in 2015, the method …
View article: Inferring Protein Domain Semantic Roles Using word2vec
Inferring Protein Domain Semantic Roles Using word2vec Open
In this paper, using word2vec, we demonstrate that proteins domains may have semantic “meaning” in the context of multi-domain proteins. Word2vec is a group of models which can be used to produce semantically meaningful embeddings of words…
View article: The PSIPRED Protein Analysis Workbench: 20 years on
The PSIPRED Protein Analysis Workbench: 20 years on Open
The PSIPRED Workbench is a web server offering a range of predictive methods to the bioscience community for 20 years. Here, we present the work we have completed to update the PSIPRED Protein Analysis Workbench and make it ready for the n…
View article: Improved protein contact predictions with the MetaPSICOV2 server in CASP12
Improved protein contact predictions with the MetaPSICOV2 server in CASP12 Open
In this paper, we present the results for the MetaPSICOV2 contact prediction server in the CASP12 community experiment ( http://predictioncenter.org ). Over the 35 assessed Free Modelling target domains the MetaPSICOV2 server achieved a me…
View article: EigenTHREADER: analogous protein fold recognition by efficient contact map threading
EigenTHREADER: analogous protein fold recognition by efficient contact map threading Open
Motivation Protein fold recognition when appropriate, evolutionarily-related, structural templates can be identified is often trivial and may even be viewed as a solved problem. However in cases where no homologous structural templates can…