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Article ; Online: Computational approaches to address data challenges in intellectual and developmental disabilities research.

Wang, Daifeng / Pruett, John R

2023 Volume 15, Issue 1, Page(s) 2

MeSH term(s)	Child ; Humans ; Developmental Disabilities ; Intellectual Disability
Language	English
Publishing date	2023-01-12
Publishing country	England
Document type	Letter ; Research Support, N.I.H., Extramural
ZDB-ID	2487174-6
ISSN	1866-1955 ; 1866-1955
ISSN (online)	1866-1955
ISSN	1866-1955
DOI	10.1186/s11689-022-09472-8
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Article ; Online: CMOT: Cross-Modality Optimal Transport for multimodal inference.

Alatkar, Sayali Anil / Wang, Daifeng

Genome biology

2023 Volume 24, Issue 1, Page(s) 163

Abstract: Multimodal measurements of single-cell sequencing technologies facilitate a comprehensive understanding of specific cellular and molecular mechanisms. However, simultaneous profiling of multiple modalities of single cells is challenging, and data ... ...

Abstract	Multimodal measurements of single-cell sequencing technologies facilitate a comprehensive understanding of specific cellular and molecular mechanisms. However, simultaneous profiling of multiple modalities of single cells is challenging, and data integration remains elusive due to missing modalities and cell-cell correspondences. To address this, we developed a computational approach, Cross-Modality Optimal Transport (CMOT), which aligns cells within available multi-modal data (source) onto a common latent space and infers missing modalities for cells from another modality (target) of mapped source cells. CMOT outperforms existing methods in various applications from developing brain, cancers to immunology, and provides biological interpretations improving cell-type or cancer classifications.
MeSH term(s)	Single-Cell Analysis/methods
Language	English
Publishing date	2023-07-11
Publishing country	England
Document type	Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't ; Research Support, U.S. Gov't, Non-P.H.S.
ZDB-ID	2040529-7
ISSN	1474-760X ; 1474-760X
ISSN (online)	1474-760X
ISSN	1474-760X
DOI	10.1186/s13059-023-02989-8
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Article ; Online: Predicting brain-regional gene regulatory networks from multi-omics for Alzheimer's disease phenotypes and Covid-19 severity.

Khullar, Saniya / Wang, Daifeng

Human molecular genetics

2023 Volume 32, Issue 11, Page(s) 1797–1813

Abstract: Neuroinflammation and immune dysregulation play a key role in Alzheimer's disease (AD) and are also associated with severe Covid-19 and neurological symptoms. Also, genome-wide association studies found many risk single nucleotide polymorphisms (SNPs) ... ...

Abstract	Neuroinflammation and immune dysregulation play a key role in Alzheimer's disease (AD) and are also associated with severe Covid-19 and neurological symptoms. Also, genome-wide association studies found many risk single nucleotide polymorphisms (SNPs) for AD and Covid-19. However, our understanding of underlying gene regulatory mechanisms from risk SNPs to AD, Covid-19 and phenotypes is still limited. To this end, we performed an integrative multi-omics analysis to predict gene regulatory networks for major brain regions from population data in AD. Our networks linked transcription factors (TFs) to TF binding sites (TFBSs) on regulatory elements to target genes. Comparative network analyses revealed cross-region-conserved and region-specific regulatory networks, in which many immunological genes are present. Furthermore, we identified a list of AD-Covid genes using our networks involving known and Covid-19 genes. Our machine learning analysis prioritized 36 AD-Covid candidate genes for predicting Covid severity. Our independent validation analyses found that these genes outperform known genes for classifying Covid-19 severity and AD. Finally, we mapped genome-wide association study SNPs of AD and severe Covid that interrupt TFBSs on our regulatory networks, revealing potential mechanistic insights of those disease risk variants. Our analyses and results are open-source available, providing an AD-Covid functional genomic resource at the brain region level.
MeSH term(s)	Humans ; Alzheimer Disease/genetics ; Alzheimer Disease/metabolism ; Gene Regulatory Networks ; Genome-Wide Association Study ; Multiomics ; COVID-19/genetics ; Brain/metabolism ; Phenotype
Language	English
Publishing date	2023-01-14
Publishing country	England
Document type	Journal Article ; Research Support, N.I.H., Extramural
ZDB-ID	1108742-0
ISSN	1460-2083 ; 0964-6906
ISSN (online)	1460-2083
ISSN	0964-6906
DOI	10.1093/hmg/ddad009
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Article: NetREm: Network Regression Embeddings reveal cell-type transcription factor coordination for gene regulation.

Khullar, Saniya / Huang, Xiang / Ramesh, Raghu / Svaren, John / Wang, Daifeng

bioRxiv : the preprint server for biology

2024

Abstract: Transcription factor (TF) coordination plays a key role in target gene (TG) regulation via protein-protein interactions (PPIs) and DNA co-binding to regulatory elements. Single-cell technologies facilitate gene expression measurement for individual cells ...

Abstract	Transcription factor (TF) coordination plays a key role in target gene (TG) regulation via protein-protein interactions (PPIs) and DNA co-binding to regulatory elements. Single-cell technologies facilitate gene expression measurement for individual cells and cell-type identification, yet the connection between TF coordination and TG regulation of various cell types remains unclear. To address this, we have developed a novel computational approach, Network Regression Embeddings (NetREm), to reveal cell-type TF-TF coordination activities for TG regulation. NetREm leverages network-constrained regularization using prior knowledge of direct and/or indirect PPIs among TFs to analyze single-cell gene expression data. We test NetREm by simulation data and benchmark its performance in 4 real-world applications that have gold standard TF-TG networks available: mouse (mESCs) and simulated human (hESCs) embryonic stem (ESCs), human hematopoietic stem (HSCs), and mouse dendritic (mDCs) cells. Further, we use NetREm to prioritize valid novel TF-TF coordination links in human Peripheral Blood Mononuclear cell (PBMC) sub-types. We apply NetREm to analyze various cell types in both central (CNS) and peripheral (PNS) nerve system (NS) (e.g. neuronal, glial, Schwann cells (SCs)) as well as in Alzheimers disease (AD). Our findings uncover cell-type coordinating TFs and identify new TF-TG candidate links. We validate our top predictions using Cut&Run and knockout loss-of-function expression data in rat/mouse models and compare results with additional functional genomic data, including expression quantitative trait loci (eQTL) and Genome-Wide Association Studies (GWAS) to link genetic variants (single nucleotide polymorphisms (SNPs)) to TF coordination.
Language	English
Publishing date	2024-05-06
Publishing country	United States
Document type	Preprint
DOI	10.1101/2023.10.25.563769
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Article: MANGEM: a web app for Multimodal Analysis of Neuronal Gene expression, Electrophysiology and Morphology.

Olson, Robert Hermod / Kalafut, Noah Cohen / Wang, Daifeng

bioRxiv : the preprint server for biology

2023

Abstract: Single-cell techniques have enabled the acquisition of multi-modal data, particularly for neurons, to characterize cellular functions. Patch-seq, for example, combines patch-clamp recording, cell imaging, and single-cell RNA-seq to obtain ... ...

Abstract	Single-cell techniques have enabled the acquisition of multi-modal data, particularly for neurons, to characterize cellular functions. Patch-seq, for example, combines patch-clamp recording, cell imaging, and single-cell RNA-seq to obtain electrophysiology, morphology, and gene expression data from a single neuron. While these multi-modal data offer potential insights into neuronal functions, they can be heterogeneous and noisy. To address this, machine-learning methods have been used to align cells from different modalities onto a low-dimensional latent space, revealing multi-modal cell clusters. However, the use of those methods can be challenging for biologists and neuroscientists without computational expertise and also requires suitable computing infrastructure for computationally expensive methods. To address these issues, we developed a cloud-based web application, MANGEM (Multimodal Analysis of Neuronal Gene expression, Electrophysiology, and Morphology) at https://ctc.waisman.wisc.edu/mangem. MANGEM provides a step-by-step accessible and user-friendly interface to machine-learning alignment methods of neuronal multi-modal data while enabling real-time visualization of characteristics of raw and aligned cells. It can be run asynchronously for large-scale data alignment, provides users with various downstream analyses of aligned cells and visualizes the analytic results such as identifying multi-modal cell clusters of cells and detecting correlated genes with electrophysiological and morphological features. We demonstrated the usage of MANGEM by aligning Patch-seq multimodal data of neuronal cells in the mouse visual cortex.
Language	English
Publishing date	2023-04-04
Publishing country	United States
Document type	Preprint
DOI	10.1101/2023.04.03.535322
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Article ; Online: MANGEM: A web app for multimodal analysis of neuronal gene expression, electrophysiology, and morphology.

Olson, Robert Hermod / Cohen Kalafut, Noah / Wang, Daifeng

Patterns (New York, N.Y.)

2023 Volume 4, Issue 11, Page(s) 100847

Abstract: Single-cell techniques like Patch-seq have enabled the acquisition of multimodal data from individual neuronal cells, offering systematic insights into neuronal functions. However, these data can be heterogeneous and noisy. To address this, machine ... ...

Abstract	Single-cell techniques like Patch-seq have enabled the acquisition of multimodal data from individual neuronal cells, offering systematic insights into neuronal functions. However, these data can be heterogeneous and noisy. To address this, machine learning methods have been used to align cells from different modalities onto a low-dimensional latent space, revealing multimodal cell clusters. The use of those methods can be challenging without computational expertise or suitable computing infrastructure for computationally expensive methods. To address this, we developed a cloud-based web application, MANGEM (multimodal analysis of neuronal gene expression, electrophysiology, and morphology). MANGEM provides a step-by-step accessible and user-friendly interface to machine learning alignment methods of neuronal multimodal data. It can run asynchronously for large-scale data alignment, provide users with various downstream analyses of aligned cells, and visualize the analytic results. We demonstrated the usage of MANGEM by aligning multimodal data of neuronal cells in the mouse visual cortex.
Language	English
Publishing date	2023-09-25
Publishing country	United States
Document type	Journal Article
ISSN	2666-3899
ISSN (online)	2666-3899
DOI	10.1016/j.patter.2023.100847
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Article: Predicting gene regulatory networks from multi-omics to link genetic risk variants and neuroimmunology to Alzheimer's disease phenotypes.

Khullar, Saniya / Wang, Daifeng

bioRxiv : the preprint server for biology

2021

Abstract: Background: Genome-wide association studies have found many genetic risk variants associated with Alzheimer's disease (AD). However, how these risk variants affect deeper phenotypes such as disease progression and immune response remains elusive. Also, ... ...

Abstract	Background: Genome-wide association studies have found many genetic risk variants associated with Alzheimer's disease (AD). However, how these risk variants affect deeper phenotypes such as disease progression and immune response remains elusive. Also, our understanding of cellular and molecular mechanisms from disease risk variants to various phenotypes is still limited. To address these problems, we performed an integrative multi-omics analysis of genotype, transcriptomics, and epigenomics for revealing gene regulatory mechanisms from disease variants to AD phenotypes. Method: First, given the population gene expression data of a cohort, we construct and cluster its gene co-expression network to identify gene co-expression modules for various AD phenotypes. Next, we predict transcription factors (TFs) regulating co-expressed genes and AD risk SNPs that interrupt TF binding sites on regulatory elements. Finally, we construct a gene regulatory network (GRN) linking SNPs, interrupted TFs, and regulatory elements to target genes and gene modules for each phenotype in the cohort. This network thus provides systematic insights into gene regulatory mechanisms from risk variants to AD phenotypes. Results: Our analysis predicted GRNs in three major AD-relevant regions: Hippocampus, Dorsolateral Prefrontal Cortex (DLPFC), Lateral Temporal Lobe (LTL). Comparative analyses revealed cross-region-conserved and region-specific GRNs, in which many immunological genes are present. For instance, SNPs rs13404184 and rs61068452 disrupt SPI1 binding and regulation of AD gene INPP5D in the Hippocampus and LTL. However, SNP rs117863556 interrupts bindings of REST to regulate GAB2 in DLPFC only. Driven by emerging neuroinflammation in AD, we used Covid-19 as a proxy to identify possible regulatory mechanisms for neuroimmunology in AD. To this end, we looked at the GRN subnetworks relating to genes from shared AD-Covid pathways. From those subnetworks, our machine learning analysis prioritized the AD-Covid genes for predicting Covid-19 severity. Decision Curve Analysis also validated our AD-Covid genes outperform known Covid-19 genes for classifying severe Covid-19 patients. This suggests AD-Covid genes along with linked SNPs can be potential novel biomarkers for neuroimmunology in AD. Finally, our results are open-source available as a comprehensive functional genomic map for AD, providing a deeper mechanistic understanding of the interplay among multi-omics, brain regions, gene functions like neuroimmunology, and phenotypes.
Language	English
Publishing date	2021-09-20
Publishing country	United States
Document type	Preprint
DOI	10.1101/2021.06.21.449165
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Article ; Online: A deep manifold-regularized learning model for improving phenotype prediction from multi-modal data.

Nguyen, Nam D / Huang, Jiawei / Wang, Daifeng

Nature computational science

2022 Volume 2, Issue 1, Page(s) 38–46

Abstract: The phenotypes of complex biological systems are fundamentally driven by various multi-scale mechanisms. Multi-modal data, such as single cell multi-omics data, enables a deeper understanding of underlying complex mechanisms across scales for phenotypes. ...

Abstract	The phenotypes of complex biological systems are fundamentally driven by various multi-scale mechanisms. Multi-modal data, such as single cell multi-omics data, enables a deeper understanding of underlying complex mechanisms across scales for phenotypes. We developed an interpretable regularized learning model, deepManReg, to predict phenotypes from multi-modal data. First, deepManReg employs deep neural networks to learn cross-modal manifolds and then to align multi-modal features onto a common latent space. Second, deepManReg uses cross-modal manifolds as a feature graph to regularize the classifiers for improving phenotype predictions and also for prioritizing the multi-modal features and cross-modal interactions for the phenotypes. We applied deepManReg to (1) an image dataset of handwritten digits with multi-features and (2) single cell multi-modal data (Patch-seq data) including transcriptomics and electrophysiology for neuronal cells in the mouse brain. We show that deepManReg improved phenotype prediction in both datasets, and also prioritized genes and electrophysiological features for the phenotypes of neuronal cells.
Language	English
Publishing date	2022-01-31
Publishing country	United States
Document type	Journal Article
ISSN	2662-8457
ISSN (online)	2662-8457
DOI	10.1038/s43588-021-00185-x
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Article ; Online: Multiview learning for understanding functional multiomics.

Nguyen, Nam D / Wang, Daifeng

PLoS computational biology

2020 Volume 16, Issue 4, Page(s) e1007677

Abstract: The molecular mechanisms and functions in complex biological systems currently remain elusive. Recent high-throughput techniques, such as next-generation sequencing, have generated a wide variety of multiomics datasets that enable the identification of ... ...

Abstract	The molecular mechanisms and functions in complex biological systems currently remain elusive. Recent high-throughput techniques, such as next-generation sequencing, have generated a wide variety of multiomics datasets that enable the identification of biological functions and mechanisms via multiple facets. However, integrating these large-scale multiomics data and discovering functional insights are, nevertheless, challenging tasks. To address these challenges, machine learning has been broadly applied to analyze multiomics. This review introduces multiview learning-an emerging machine learning field-and envisions its potentially powerful applications to multiomics. In particular, multiview learning is more effective than previous integrative methods for learning data's heterogeneity and revealing cross-talk patterns. Although it has been applied to various contexts, such as computer vision and speech recognition, multiview learning has not yet been widely applied to biological data-specifically, multiomics data. Therefore, this paper firstly reviews recent multiview learning methods and unifies them in a framework called multiview empirical risk minimization (MV-ERM). We further discuss the potential applications of each method to multiomics, including genomics, transcriptomics, and epigenomics, in an aim to discover the functional and mechanistic interpretations across omics. Secondly, we explore possible applications to different biological systems, including human diseases (e.g., brain disorders and cancers), plants, and single-cell analysis, and discuss both the benefits and caveats of using multiview learning to discover the molecular mechanisms and functions of these systems.
MeSH term(s)	Algorithms ; Alzheimer Disease/physiopathology ; Brain/physiology ; Brain/physiopathology ; Chlamydomonas reinhardtii ; Cluster Analysis ; Computational Biology/methods ; DNA ; Data Interpretation, Statistical ; Genomics/methods ; High-Throughput Nucleotide Sequencing ; Humans ; Machine Learning ; Metabolomics/methods ; Proteomics/methods ; Single-Cell Analysis ; Software
Chemical Substances	DNA (9007-49-2)
Language	English
Publishing date	2020-04-02
Publishing country	United States
Document type	Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't ; Review
ZDB-ID	2193340-6
ISSN	1553-7358 ; 1553-734X
ISSN (online)	1553-7358
ISSN	1553-734X
DOI	10.1371/journal.pcbi.1007677
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Article ; Online: Manifold learning analysis suggests strategies to align single-cell multimodal data of neuronal electrophysiology and transcriptomics.

Huang, Jiawei / Sheng, Jie / Wang, Daifeng

Communications biology

2021 Volume 4, Issue 1, Page(s) 1308

Abstract: Recent single-cell multimodal data reveal multi-scale characteristics of single cells, such as transcriptomics, morphology, and electrophysiology. However, integrating and analyzing such multimodal data to deeper understand functional genomics and gene ... ...

Abstract	Recent single-cell multimodal data reveal multi-scale characteristics of single cells, such as transcriptomics, morphology, and electrophysiology. However, integrating and analyzing such multimodal data to deeper understand functional genomics and gene regulation in various cellular characteristics remains elusive. To address this, we applied and benchmarked multiple machine learning methods to align gene expression and electrophysiological data of single neuronal cells in the mouse brain from the Brain Initiative. We found that nonlinear manifold learning outperforms other methods. After manifold alignment, the cells form clusters highly corresponding to transcriptomic and morphological cell types, suggesting a strong nonlinear relationship between gene expression and electrophysiology at the cell-type level. Also, the electrophysiological features are highly predictable by gene expression on the latent space from manifold alignment. The aligned cells further show continuous changes of electrophysiological features, implying cross-cluster gene expression transitions. Functional enrichment and gene regulatory network analyses for those cell clusters revealed potential genome functions and molecular mechanisms from gene expression to neuronal electrophysiology.
MeSH term(s)	Animals ; Brain/physiology ; Electrophysiological Phenomena ; Electrophysiology ; Gene Expression Profiling ; Machine Learning ; Mice ; Neurons/physiology ; Single-Cell Analysis/methods ; Transcriptome
Language	English
Publishing date	2021-11-19
Publishing country	England
Document type	Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
ISSN	2399-3642
ISSN (online)	2399-3642
DOI	10.1038/s42003-021-02807-6
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