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Book ; Online ; E-Book: Continuous EEG monitoring

Husain, Aatif M. / Sinha, Saurabh R.

principles and practice

2017

Author's details	Aatif M. Husain, Saurabh R. Sinha editors
Keywords	Seizures / diagnosis ; Seizures / prevention & control ; Status Epilepticus / prevention & control ; Electroencephalography / methods ; Neurophysiological Monitoring / methods ; Critical Illness / therapy
Language	English
Size	1 Online-Ressource (xiii, 671 Seiten), Illustrationen
Publisher	Springer
Publishing place	Cham
Publishing country	Switzerland
Document type	Book ; Online ; E-Book
Remark	Zugriff für angemeldete ZB MED-Nutzerinnen und -Nutzer
HBZ-ID	HT019346218
ISBN	978-3-319-31230-9 ; 9783319312286 ; 3-319-31230-8 ; 3319312286
Database	ZB MED Catalogue: Medicine, Health, Nutrition, Environment, Agriculture

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Article ; Online: SPREd: a simulation-supervised neural network tool for gene regulatory network reconstruction.

Wu, Zijun / Sinha, Saurabh

Bioinformatics advances

2024 Volume 4, Issue 1, Page(s) vbae011

Abstract: Summary: Reconstruction of gene regulatory networks (GRNs) from expression data is a significant open problem. Common approaches train a machine learning (ML) model to predict a gene's expression using transcription factors' (TFs') expression as ... ...

Abstract	Summary: Reconstruction of gene regulatory networks (GRNs) from expression data is a significant open problem. Common approaches train a machine learning (ML) model to predict a gene's expression using transcription factors' (TFs') expression as features and designate important features/TFs as regulators of the gene. Here, we present an entirely different paradigm, where GRN edges are directly predicted by the ML model. The new approach, named "SPREd," is a simulation-supervised neural network for GRN inference. Its inputs comprise expression relationships (e.g. correlation, mutual information) between the target gene and each TF and between pairs of TFs. The output includes binary labels indicating whether each TF regulates the target gene. We train the neural network model using synthetic expression data generated by a biophysics-inspired simulation model that incorporates linear as well as non-linear TF-gene relationships and diverse GRN configurations. We show SPREd to outperform state-of-the-art GRN reconstruction tools GENIE3, ENNET, PORTIA, and TIGRESS on synthetic datasets with high co-expression among TFs, similar to that seen in real data. A key advantage of the new approach is its robustness to relatively small numbers of conditions (columns) in the expression matrix, which is a common problem faced by existing methods. Finally, we evaluate SPREd on real data sets in yeast that represent gold-standard benchmarks of GRN reconstruction and show it to perform significantly better than or comparably to existing methods. In addition to its high accuracy and speed, SPREd marks a first step toward incorporating biophysics principles of gene regulation into ML-based approaches to GRN reconstruction. Availability and implementation: Data and code are available from https://github.com/iiiime/SPREd.
Language	English
Publishing date	2024-01-23
Publishing country	England
Document type	Journal Article
ISSN	2635-0041
ISSN (online)	2635-0041
DOI	10.1093/bioadv/vbae011
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Article ; Online: Food: the tuberculosis vaccine we already have.

Sinha, Pranay / Mehta, Saurabh

Lancet (London, England)

2023 Volume 402, Issue 10402, Page(s) 588–590

MeSH term(s)	Humans ; Tuberculosis Vaccines ; BCG Vaccine ; Tuberculosis/epidemiology ; Tuberculosis/prevention & control ; Food ; Mycobacterium tuberculosis
Chemical Substances	Tuberculosis Vaccines ; BCG Vaccine
Language	English
Publishing date	2023-08-08
Publishing country	England
Document type	Journal Article ; Comment
ZDB-ID	3306-6
ISSN	1474-547X ; 0023-7507 ; 0140-6736
ISSN (online)	1474-547X
ISSN	0023-7507 ; 0140-6736
DOI	10.1016/S0140-6736(23)01321-1
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Article ; Online: CIMLA: Interpretable AI for inference of differential causal networks.

Dibaeinia, Payam / Sinha, Saurabh

ArXiv

2023

Abstract: The discovery of causal relationships from high-dimensional data is a major open problem in bioinformatics. Machine learning and feature attribution models have shown great promise in this context but lack causal interpretation. Here, we show that a ... ...

Abstract	The discovery of causal relationships from high-dimensional data is a major open problem in bioinformatics. Machine learning and feature attribution models have shown great promise in this context but lack causal interpretation. Here, we show that a popular feature attribution model estimates a causal quantity reflecting the influence of one variable on another, under certain assumptions. We leverage this insight to implement a new tool, CIMLA, for discovering condition-dependent changes in causal relationships. We then use CIMLA to identify differences in gene regulatory networks between biological conditions, a problem that has received great attention in recent years. Using extensive benchmarking on simulated data sets, we show that CIMLA is more robust to confounding variables and is more accurate than leading methods. Finally, we employ CIMLA to analyze a previously published single-cell RNA-seq data set collected from subjects with and without Alzheimer's disease (AD), discovering several potential regulators of AD.
Language	English
Publishing date	2023-04-25
Publishing country	United States
Document type	Preprint
ISSN	2331-8422
ISSN (online)	2331-8422
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Article ; Online: Integrative modeling of lncRNA-chromatin interaction maps reveals diverse mechanisms of nuclear retention.

Tabe-Bordbar, Shayan / Sinha, Saurabh

BMC genomics

2023 Volume 24, Issue 1, Page(s) 395

Abstract: Background: Many long non-coding RNAs, known to be involved in transcriptional regulation, are enriched in the nucleus and interact with chromatin. However, their mechanisms of chromatin interaction and the served cellular functions are poorly ... ...

Abstract	Background: Many long non-coding RNAs, known to be involved in transcriptional regulation, are enriched in the nucleus and interact with chromatin. However, their mechanisms of chromatin interaction and the served cellular functions are poorly understood. We sought to characterize the mechanisms of lncRNA nuclear retention by systematically mapping the sequence and chromatin features that distinguish lncRNA-interacting genomic segments. Results: We found DNA 5-mer frequencies to be predictive of chromatin interactions for all lncRNAs, suggesting sequence-specificity as a global theme in the interactome. Sequence features representing protein-DNA and protein-RNA binding motifs revealed potential mechanisms for specific lncRNAs. Complementary to these global themes, transcription-related features and DNA-RNA triplex formation potential were noted to be highly predictive for two mutually exclusive sets of lncRNAs. DNA methylation was also noted to be a significant predictor, but only when combined with other epigenomic features. Conclusions: Taken together, our statistical findings suggest that a group of lncRNAs interacts with transcriptionally inactive chromatin through triplex formation, whereas another group interacts with transcriptionally active regions and is involved in DNA Damage Response (DDR) through formation of R-loops. Curiously, we observed a strong pattern of enrichment of 5-mers in four potentially interacting entities: lncRNA-bound DNA tiles, lncRNAs, miRNA seed sequences, and repeat elements. This finding points to a broad sequence-based network of interactions that may underlie regulation of fundamental cellular functions. Overall, this study reveals diverse sequence and chromatin features related to lncRNA-chromatin interactions, suggesting potential mechanisms of nuclear retention and regulatory function.
MeSH term(s)	RNA, Long Noncoding/metabolism ; Chromatin/genetics ; DNA/chemistry ; Gene Expression Regulation
Chemical Substances	RNA, Long Noncoding ; Chromatin ; DNA (9007-49-2)
Language	English
Publishing date	2023-07-13
Publishing country	England
Document type	Journal Article
ZDB-ID	2041499-7
ISSN	1471-2164 ; 1471-2164
ISSN (online)	1471-2164
ISSN	1471-2164
DOI	10.1186/s12864-023-09498-9
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Article: SPREd: A simulation-supervised neural network tool for gene regulatory network reconstruction.

Wu, Zijun / Sinha, Saurabh

bioRxiv : the preprint server for biology

2023

Abstract: Reconstruction of gene regulatory networks (GRNs) from expression data is a significant open problem. Common approaches train a machine learning (ML) model to predict a gene's expression using transcription factors' (TFs') expression as features and ... ...

Abstract	Reconstruction of gene regulatory networks (GRNs) from expression data is a significant open problem. Common approaches train a machine learning (ML) model to predict a gene's expression using transcription factors' (TFs') expression as features and designate important features/TFs as regulators of the gene. Here, we present an entirely different paradigm, where GRN edges are directly predicted by the ML model. The new approach, named "SPREd" is a simulation-supervised neural network for GRN inference. Its inputs comprise expression relationships (e.g., correlation, mutual information) between the target gene and each TF and between pairs of TFs. The output includes binary labels indicating whether each TF regulates the target gene. We train the neural network model using synthetic expression data generated by a biophysics-inspired simulation model that incorporates linear as well as non-linear TF-gene relationships and diverse GRN configurations. We show SPREd to outperform state-of-the-art GRN reconstruction tools GENIE3, ENNET, PORTIA and TIGRESS on synthetic datasets with high co-expression among TFs, similar to that seen in real data. A key advantage of the new approach is its robustness to relatively small numbers of conditions (columns) in the expression matrix, which is a common problem faced by existing methods. Finally, we evaluate SPREd on real data sets in yeast that represent gold standard benchmarks of GRN reconstruction and show it to perform significantly better than or comparably to existing methods. In addition to its high accuracy and speed, SPREd marks a first step towards incorporating biophysics principles of gene regulation into ML-based approaches to GRN reconstruction.
Language	English
Publishing date	2023-11-13
Publishing country	United States
Document type	Preprint
DOI	10.1101/2023.11.09.566399
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Article ; Online: Commentary: Subthreshold micropulse yellow laser for central serous chorioretinopathy: Finding the right protocol.

Saurabh, Kumar / Roy, Rupak / Biswas, Rupak Kanti / Sinha, Sourav

Indian journal of ophthalmology

2022 Volume 70, Issue 9, Page(s) 3346

MeSH term(s)	Central Serous Chorioretinopathy ; Chronic Disease ; Fluorescein Angiography ; Humans ; Laser Coagulation ; Lasers ; Photochemotherapy ; Tomography, Optical Coherence
Language	English
Publishing date	2022-09-20
Publishing country	India
Document type	Journal Article ; Comment
ZDB-ID	187392-1
ISSN	1998-3689 ; 0301-4738
ISSN (online)	1998-3689
ISSN	0301-4738
DOI	10.4103/ijo.IJO_1461_22
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Article ; Online: Thermodynamics-based modeling reveals regulatory effects of indirect transcription factor-DNA binding.

Bhogale, Shounak / Sinha, Saurabh

iScience

2022 Volume 25, Issue 5, Page(s) 104152

Abstract: Transcription factors (TFs) influence gene expression by binding to DNA, yet experimental data suggests that they also frequently bind regulatory DNA indirectly by interacting with other DNA-bound proteins. Here, we used a data modeling approach to test ... ...

Abstract	Transcription factors (TFs) influence gene expression by binding to DNA, yet experimental data suggests that they also frequently bind regulatory DNA indirectly by interacting with other DNA-bound proteins. Here, we used a data modeling approach to test if such indirect binding by TFs plays a significant role in gene regulation. We first incorporated regulatory function of indirectly bound TFs into a thermodynamics-based model for predicting enhancer-driven expression from its sequence. We then fit the new model to a rich data set comprising hundreds of enhancers and their regulatory activities during mesoderm specification in Drosophila embryogenesis and showed that the newly incorporated mechanism results in significantly better agreement with data. In the process, we derived the first sequence-level model of this extensively characterized regulatory program. We further showed that allowing indirect binding of a TF explains its localization at enhancers more accurately than with direct binding only. Our model also provided a simple explanation of how a TF may switch between activating and repressive roles depending on context.
Language	English
Publishing date	2022-03-24
Publishing country	United States
Document type	Journal Article
ISSN	2589-0042
ISSN (online)	2589-0042
DOI	10.1016/j.isci.2022.104152
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Article: Effect of different doses of dexmedetomidine as an adjuvant to lignocaine nebulization: A comparative study during awake flexible fiberoptic bronchoscopy.

Kumar, Amarjeet / Kumari, Poonam / Sinha, Chandni / Kumar, Ajeet / Karmakar, Saurabh

Journal of anaesthesiology, clinical pharmacology

2024 Volume 40, Issue 1, Page(s) 56–62

Abstract: Background and aims: Mild to moderate sedation during bronchoscopy is essential for patient safety, comfort during and after the procedure, and to facilitate the performance of the bronchoscopist. Dexmedetomidine is a highly selective, centrally acting ... ...

Abstract	Background and aims: Mild to moderate sedation during bronchoscopy is essential for patient safety, comfort during and after the procedure, and to facilitate the performance of the bronchoscopist. Dexmedetomidine is a highly selective, centrally acting α-2 agonist used to provide conscious sedation during various procedures. The aim of this study was to compare the efficacy of three different doses of dexmedetomidine nebulization as an adjuvant to lignocaine during bronchoscopy. Material and methods: Ninety American Society of Anesthesiologists physical status I/II patients, aged from 18 to 60 years, scheduled for an elective bronchoscopy, were recruited. They were divided into three groups: 30 patients in each group. Group I: The patient was nebulized with a mixture of 4 ml of 4% lignocaine and dexmedetomidine 0.5 μg/kg. Group II: The patient was nebulized with a mixture of 4% lignocaine, 4 ml, and dexmedetomidine, 1 μg/kg. Group III: The patient was nebulized with 4% lignocaine 4 ml and dexmedetomidine 1.5 μg/kg. Results: The mean cough score was (1.17 ± 0.37), (1.40 ± 0.49), and (1.70 ± 0.75) in group III, group II, and group I, respectively. A significant difference was found between the groups. Patients were more comfortable with a statistically significant difference in the comfort score in group III as compared to group II and group I. Conclusion: Dexmedetomidine nebulization in a dose of 1.5 μg/kg (compared to 1 μg/kg or 0.5 μg/kg) as an adjuvant to lignocaine, provides better bronchoscopy conditions and patient satisfaction.
Language	English
Publishing date	2024-03-14
Publishing country	India
Document type	Journal Article
ZDB-ID	1401760-x
ISSN	0970-9185
ISSN	0970-9185
DOI	10.4103/joacp.joacp_60_22
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Article ; Online: Thermodynamics-based modeling reveals regulatory effects of indirect transcription factor-DNA binding

Shounak Bhogale / Saurabh Sinha

iScience, Vol 25, Iss 5, Pp 104152- (2022)

2022

Abstract: Summary: Transcription factors (TFs) influence gene expression by binding to DNA, yet experimental data suggests that they also frequently bind regulatory DNA indirectly by interacting with other DNA-bound proteins. Here, we used a data modeling approach ...

Abstract	Summary: Transcription factors (TFs) influence gene expression by binding to DNA, yet experimental data suggests that they also frequently bind regulatory DNA indirectly by interacting with other DNA-bound proteins. Here, we used a data modeling approach to test if such indirect binding by TFs plays a significant role in gene regulation. We first incorporated regulatory function of indirectly bound TFs into a thermodynamics-based model for predicting enhancer-driven expression from its sequence. We then fit the new model to a rich data set comprising hundreds of enhancers and their regulatory activities during mesoderm specification in Drosophila embryogenesis and showed that the newly incorporated mechanism results in significantly better agreement with data. In the process, we derived the first sequence-level model of this extensively characterized regulatory program. We further showed that allowing indirect binding of a TF explains its localization at enhancers more accurately than with direct binding only. Our model also provided a simple explanation of how a TF may switch between activating and repressive roles depending on context.
Keywords	Biological sciences ; Biochemistry ; Bioinformatics ; Science ; Q
Subject code	612
Language	English
Publishing date	2022-05-01T00:00:00Z
Publisher	Elsevier
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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