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Article: Machine learning-driven exploration of drug therapies for triple-negative breast cancer treatment.

Kaushik, Aman Chandra / Zhao, Zhongming

2023 Volume 10, Page(s) 1215204

Abstract: Breast cancer is the second leading cause of cancer death in women among all cancer types. It is highly heterogeneous in nature, which means that the tumors have different morphologies and there is heterogeneity even among people who have the same type ... ...

Abstract	Breast cancer is the second leading cause of cancer death in women among all cancer types. It is highly heterogeneous in nature, which means that the tumors have different morphologies and there is heterogeneity even among people who have the same type of tumor. Several staging and classifying systems have been developed due to the variability of different types of breast cancer. Due to high heterogeneity, personalized treatment has become a new strategy. Out of all breast cancer subtypes, triple-negative breast cancer (TNBC) comprises ∼10%-15%. TNBC refers to the subtype of breast cancer where cells do not express estrogen receptors, progesterone receptors, or human epidermal growth factor receptors (ERs, PRs, and HERs). Tumors in TNBC have a diverse set of genetic markers and prognostic indicators. We scanned the Cancer Cell Line Encyclopedia (CCLE) and Genomics of Drug Sensitivity in Cancer (GDSC) databases for potential drugs using human breast cancer cell lines and drug sensitivity data. Three different machine-learning approaches were used to evaluate the prediction of six effective drugs against the TNBC cell lines. The top biomarkers were then shortlisted on the basis of their involvement in breast cancer and further subjected to testing for radion resistance using data from the Cleveland database. It was observed that Panobinostat, PLX4720, Lapatinib, Nilotinib, Selumetinib, and Tanespimycin were six effective drugs against the TNBC cell lines. We could identify potential derivates that may be used against approved drugs. Only one biomarker (
Language	English
Publishing date	2023-08-04
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2814330-9
ISSN	2296-889X
ISSN	2296-889X
DOI	10.3389/fmolb.2023.1215204
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Improving drug development in precision psychiatry by ameliorating cognitive biases.

Fernandes, Brisa S / Zhao, Zhongming

European neuropsychopharmacology : the journal of the European College of Neuropsychopharmacology

2023 Volume 70, Page(s) 14–16

MeSH term(s)	Humans ; Mental Disorders ; Precision Medicine ; Psychiatry ; Bias ; Cognition
Language	English
Publishing date	2023-02-14
Publishing country	Netherlands
Document type	Journal Article ; Research Support, N.I.H., Extramural
ZDB-ID	1082947-7
ISSN	1873-7862 ; 0924-977X
ISSN (online)	1873-7862
ISSN	0924-977X
DOI	10.1016/j.euroneuro.2023.02.001
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Zs.A 3288: Show issues			Location: Je nach Verfügbarkeit (siehe Angabe bei Bestand) bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular Jg. 1995 - 2021: Lesesall (2.OG) ab Jg. 2022: Lesesaal (EG)
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Article ; Online: Revisiting the Relative Contribution of Common and Rare Genetic Variants to Attention-Deficit/Hyperactivity Disorder Among Diverse Populations.

Dai, Yulin / Fernandes, Brisa S / Zhao, Zhongming

Biological psychiatry

2024 Volume 95, Issue 9, Page(s) 822–824

MeSH term(s)	Humans ; Attention Deficit Disorder with Hyperactivity/genetics ; Genetic Predisposition to Disease
Language	English
Publishing date	2024-04-10
Publishing country	United States
Document type	Journal Article
ZDB-ID	209434-4
ISSN	1873-2402 ; 0006-3223
ISSN (online)	1873-2402
ISSN	0006-3223
DOI	10.1016/j.biopsych.2024.02.1007
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Zs.A 720: Show issues

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Article: Dissecting the shared genetic architecture between Alzheimer's disease and frailty: a cross-trait meta-analyses of genome-wide association studies.

Enduru, Nitesh / Fernandes, Brisa S / Zhao, Zhongming

Frontiers in genetics

2024 Volume 15, Page(s) 1376050

Abstract: Introduction: ...

Abstract	Introduction:
Language	English
Publishing date	2024-04-19
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2606823-0
ISSN	1664-8021
ISSN	1664-8021
DOI	10.3389/fgene.2024.1376050
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Article ; Online: NetBCE: An Interpretable Deep Neural Network for Accurate Prediction of Linear B-cell Epitopes.

Xu, Haodong / Zhao, Zhongming

Genomics, proteomics & bioinformatics

2022 Volume 20, Issue 5, Page(s) 1002–1012

Abstract: Identification of B-cell epitopes (BCEs) plays an essential role in the development of peptide vaccines and immuno-diagnostic reagents, as well as antibody design and production. In this work, we generated a large benchmark dataset comprising 124,879 ... ...

Abstract	Identification of B-cell epitopes (BCEs) plays an essential role in the development of peptide vaccines and immuno-diagnostic reagents, as well as antibody design and production. In this work, we generated a large benchmark dataset comprising 124,879 experimentally supported linear epitope-containing regions in 3567 protein clusters from over 1.3 million B cell assays. Analysis of this curated dataset showed large pathogen diversity covering 176 different families. The accuracy in linear BCE prediction was found to strongly vary with different features, while all sequence-derived and structural features were informative. To search more efficient and interpretive feature representations, a ten-layer deep learning framework for linear BCE prediction, namely NetBCE, was developed. NetBCE achieved high accuracy and robust performance with the average area under the curve (AUC) value of 0.8455 in five-fold cross-validation through automatically learning the informative classification features. NetBCE substantially outperformed the conventional machine learning algorithms and other tools, with more than 22.06% improvement of AUC value compared to other tools using an independent dataset. Through investigating the output of important network modules in NetBCE, epitopes and non-epitopes tended to be presented in distinct regions with efficient feature representation along the network layer hierarchy. The NetBCE is freely available at https://github.com/bsml320/NetBCE.
MeSH term(s)	Humans ; Epitopes, B-Lymphocyte/chemistry ; Neural Networks, Computer ; Algorithms
Chemical Substances	Epitopes, B-Lymphocyte
Language	English
Publishing date	2022-12-13
Publishing country	England
Document type	Journal Article ; Research Support, N.I.H., Extramural ; Research Support, U.S. Gov't, Non-P.H.S.
ZDB-ID	2240213-5
ISSN	2210-3244 ; 1672-0229
ISSN (online)	2210-3244
ISSN	1672-0229
DOI	10.1016/j.gpb.2022.11.009
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Zs.A 6438: Show issues

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Article ; Online: Unraveling the intercellular communication disruption and key pathways in Alzheimer's disease: an integrative study of single-nucleus transcriptomes and genetic association.

Liu, Andi / Fernandes, Brisa S / Citu, Citu / Zhao, Zhongming

Alzheimer's research & therapy

2024 Volume 16, Issue 1, Page(s) 3

Abstract: Background: Recently, single-nucleus RNA-seq (snRNA-seq) analyses have revealed important cellular and functional features of Alzheimer's disease (AD), a prevalent neurodegenerative disease. However, our knowledge regarding intercellular communication ... ...

Abstract	Background: Recently, single-nucleus RNA-seq (snRNA-seq) analyses have revealed important cellular and functional features of Alzheimer's disease (AD), a prevalent neurodegenerative disease. However, our knowledge regarding intercellular communication mediated by dysregulated ligand-receptor (LR) interactions remains very limited in AD brains. Methods: We systematically assessed the intercellular communication networks by using a discovery snRNA-seq dataset comprising 69,499 nuclei from 48 human postmortem prefrontal cortex (PFC) samples. We replicated the findings using an independent snRNA-seq dataset of 56,440 nuclei from 18 PFC samples. By integrating genetic signals from AD genome-wide association studies (GWAS) summary statistics and whole genome sequencing (WGS) data, we prioritized AD-associated Gene Ontology (GO) terms containing dysregulated LR interactions. We further explored drug repurposing for the prioritized LR pairs using the Therapeutic Targets Database. Results: We identified 190 dysregulated LR interactions across six major cell types in AD PFC, of which 107 pairs were replicated. Among the replicated LR signals, we found globally downregulated communications in the astrocytes-to-neurons signaling axis, characterized, for instance, by the downregulation of APOE-related and Calmodulin (CALM)-related LR interactions and their potential regulatory connections to target genes. Pathway analyses revealed 44 GO terms significantly linked to AD, highlighting Biological Processes such as 'amyloid precursor protein processing' and 'ion transmembrane transport,' among others. We prioritized several drug repurposing candidates, such as cromoglicate, targeting the identified dysregulated LR pairs. Conclusions: Our integrative analysis identified key dysregulated LR interactions in a cell type-specific manner and the associated GO terms in AD, offering novel insights into potential therapeutic targets involved in disrupted cell-cell communication in AD.
MeSH term(s)	Humans ; Alzheimer Disease/genetics ; Alzheimer Disease/metabolism ; Transcriptome ; Neurodegenerative Diseases ; Genome-Wide Association Study ; Cell Communication ; RNA, Small Nuclear
Chemical Substances	RNA, Small Nuclear
Language	English
Publishing date	2024-01-02
Publishing country	England
Document type	Journal Article
ZDB-ID	2506521-X
ISSN	1758-9193 ; 1758-9193
ISSN (online)	1758-9193
ISSN	1758-9193
DOI	10.1186/s13195-023-01372-w
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Article ; Online: Montelukast as a repurposable additive drug for standard-efficacy multiple sclerosis treatment: Emulating clinical trials with retrospective administrative health claims data.

Manuel, Astrid M / Gottlieb, Assaf / Freeman, Leorah / Zhao, Zhongming

Multiple sclerosis (Houndmills, Basingstoke, England)

2024 Volume 30, Issue 6, Page(s) 696–706

Abstract: Background: Effective and safe treatment options for multiple sclerosis (MS) are still needed. Montelukast, a leukotriene receptor antagonist (LTRA) currently indicated for asthma or allergic rhinitis, may provide an additional therapeutic approach.: ... ...

Abstract	Background: Effective and safe treatment options for multiple sclerosis (MS) are still needed. Montelukast, a leukotriene receptor antagonist (LTRA) currently indicated for asthma or allergic rhinitis, may provide an additional therapeutic approach. Objective: The study aimed to evaluate the effects of montelukast on the relapses of people with MS (pwMS). Methods: In this retrospective case-control study, two independent longitudinal claims datasets were used to emulate randomized clinical trials (RCTs). We identified pwMS aged 18-65 years, on MS disease-modifying therapies concomitantly, in de-identified claims from Optum's Clinformatics Results: pwMS treated with montelukast demonstrated a statistically significant 23.6% reduction in relapses compared to non-users in 67.3% of emulated RCTs. Conclusion: Real-world evidence suggested that montelukast reduces MS relapses, warranting future clinical trials and further research on LTRAs' potential mechanism in MS.
MeSH term(s)	Humans ; Sulfides ; Cyclopropanes ; Quinolines/therapeutic use ; Quinolines/administration & dosage ; Acetates/therapeutic use ; Adult ; Middle Aged ; Female ; Male ; Retrospective Studies ; Leukotriene Antagonists/therapeutic use ; Multiple Sclerosis/drug therapy ; Young Adult ; Case-Control Studies ; Adolescent ; Aged ; Administrative Claims, Healthcare/statistics & numerical data ; Recurrence
Chemical Substances	montelukast (MHM278SD3E) ; Sulfides ; Cyclopropanes ; Quinolines ; Acetates ; Leukotriene Antagonists
Language	English
Publishing date	2024-04-25
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	1290669-4
ISSN	1477-0970 ; 1352-4585
ISSN (online)	1477-0970
ISSN	1352-4585
DOI	10.1177/13524585241240398
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Zs.A 4428: Show issues

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ab Jg. 2022: Lesesaal (EG)

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Article: Accurate prediction of functional effect of single amino acid variants with deep learning.

Derbel, Houssemeddine / Zhao, Zhongming / Liu, Qian

Computational and structural biotechnology journal

2023 Volume 21, Page(s) 5776–5784

Abstract: The assessment of functional effect of amino acid variants is a critical biological problem in proteomics for clinical medicine and protein engineering. Although natively occurring variants offer insights into deleterious variants, high-throughput deep ... ...

Abstract	The assessment of functional effect of amino acid variants is a critical biological problem in proteomics for clinical medicine and protein engineering. Although natively occurring variants offer insights into deleterious variants, high-throughput deep mutational experiments enable comprehensive investigation of amino acid variants for a given protein. However, these mutational experiments are too expensive to dissect millions of variants on thousands of proteins. Thus, computational approaches have been proposed, but they heavily rely on hand-crafted evolutionary conservation, limiting their accuracy. Recent advancement in transformers provides a promising solution to precisely estimate the functional effects of protein variants on high-throughput experimental data. Here, we introduce a novel deep learning model, namely Rep2Mut-V2, which leverages learned representation from transformer models. Rep2Mut-V2 significantly enhances the prediction accuracy for 27 types of measurements of functional effects of protein variants. In the evaluation of 38 protein datasets with 118,933 single amino acid variants, Rep2Mut-V2 achieved an average Spearman's correlation coefficient of 0.7. This surpasses the performance of six state-of-the-art methods, including the recently released methods ESM, DeepSequence and EVE. Even with limited training data, Rep2Mut-V2 outperforms ESM and DeepSequence, showing its potential to extend high-throughput experimental analysis for more protein variants to reduce experimental cost. In conclusion, Rep2Mut-V2 provides accurate predictions of the functional effects of single amino acid variants of protein coding sequences. This tool can significantly aid in the interpretation of variants in human disease studies.
Language	English
Publishing date	2023-11-10
Publishing country	Netherlands
Document type	Journal Article
ZDB-ID	2694435-2
ISSN	2001-0370
ISSN	2001-0370
DOI	10.1016/j.csbj.2023.11.017
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Article ; Online: DegronMD: Leveraging Evolutionary and Structural Features for Deciphering Protein-Targeted Degradation, Mutations, and Drug Response to Degrons.

Xu, Haodong / Hu, Ruifeng / Zhao, Zhongming

Molecular biology and evolution

2023 Volume 40, Issue 12

Abstract: Protein-targeted degradation is an emerging and promising therapeutic approach. The specificity of degradation and the maintenance of cellular homeostasis are determined by the interactions between E3 ubiquitin ligase and degradation signals, known as ... ...

Abstract	Protein-targeted degradation is an emerging and promising therapeutic approach. The specificity of degradation and the maintenance of cellular homeostasis are determined by the interactions between E3 ubiquitin ligase and degradation signals, known as degrons. The human genome encodes over 600 E3 ligases; however, only a small number of targeted degron instances have been identified so far. In this study, we introduced DegronMD, an open knowledgebase designed for the investigation of degrons, their associated dysfunctional events, and drug responses. We revealed that degrons are evolutionarily conserved and tend to occur near the sites of protein translational modifications, particularly in the regions of disordered structure and higher solvent accessibility. Through pattern recognition and machine learning techniques, we constructed the degrome landscape across the human proteome, yielding over 18,000 new degrons for targeted protein degradation. Furthermore, dysfunction of degrons disrupts the degradation process and leads to the abnormal accumulation of proteins; this process is associated with various types of human cancers. Based on the estimated phenotypic changes induced by somatic mutations, we systematically quantified and assessed the impact of mutations on degron function in pan-cancers; these results helped to build a global mutational map on human degrome, including 89,318 actionable mutations that may induce the dysfunction of degrons and disrupt protein degradation pathways. Multiomics integrative analysis unveiled over 400 drug resistance events associated with the mutations in functional degrons. DegronMD, accessible at https://bioinfo.uth.edu/degronmd, is a useful resource to explore the biological mechanisms, infer protein degradation, and assist with drug discovery and design on degrons.
MeSH term(s)	Humans ; Proteolysis ; Degrons ; Proteasome Endopeptidase Complex/genetics ; Ubiquitin-Protein Ligases/genetics ; Ubiquitin-Protein Ligases/chemistry ; Ubiquitin-Protein Ligases/metabolism ; Proteome/genetics ; Neoplasms ; Mutation
Chemical Substances	Proteasome Endopeptidase Complex (EC 3.4.25.1) ; Ubiquitin-Protein Ligases (EC 2.3.2.27) ; Proteome
Language	English
Publishing date	2023-11-22
Publishing country	United States
Document type	Journal Article
ZDB-ID	998579-7
ISSN	1537-1719 ; 0737-4038
ISSN (online)	1537-1719
ISSN	0737-4038
DOI	10.1093/molbev/msad253
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Zs.A 2137: Show issues

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Article ; Online: Benchmark of embedding-based methods for accurate and transferable prediction of drug response.

Jia, Peilin / Hu, Ruifeng / Zhao, Zhongming

Briefings in bioinformatics

2023 Volume 24, Issue 3

Abstract: Prediction of therapy response has been a major challenge in cancer precision medicine due to the extensive tumor heterogeneity. Recently, several deep learning methods have been developed to predict drug response by utilizing various omics data. Most of ...

Abstract	Prediction of therapy response has been a major challenge in cancer precision medicine due to the extensive tumor heterogeneity. Recently, several deep learning methods have been developed to predict drug response by utilizing various omics data. Most of them train models by using the drug-response screening data generated from cell lines and then use these models to predict response in cancer patient data. In this study, we focus on and evaluate deep learning methods using transcriptome data for the long-standing question of personalized drug-response prediction. We developed an embedding-based approach for drug-response prediction and benchmarked similar methods for their performance. For all methods, we used pretreatment transcriptome data to train models and then conducted a comprehensive evaluation and comparison of the models using cross-panels, cross-datasets and target genes. We further validated the methods using three independent datasets assessing multiple compounds for their predictive capability of drug response, survival outcome and cell line status. As a result, the methods building on gene embeddings had an overall competitive performance with reduced overfitting when we applied evaluation parameters for model fitting as well as the correlation with clinical outcomes in the validation data. We further developed an ensemble model to combine the results from the three most competitive methods for an overall prediction. Finally, we developed DrVAEN (https://bioinfo.uth.edu/drvaen), a user-friendly and easy-accessible web-server that hosts all these methods for drug-response prediction and model comparison for broad use in cancer research, method evaluation and drug development.
MeSH term(s)	Humans ; Benchmarking ; Neoplasms/drug therapy ; Neoplasms/genetics ; Precision Medicine/methods
Language	English
Publishing date	2023-03-24
Publishing country	England
Document type	Journal Article
ZDB-ID	2068142-2
ISSN	1477-4054 ; 1467-5463
ISSN (online)	1477-4054
ISSN	1467-5463
DOI	10.1093/bib/bbad098
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