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Article: PrePPI: A structure informed proteome-wide database of protein-protein interactions.

Petrey, Donald / Zhao, Haiqing / Trudeau, Stephen / Murray, Diana / Honig, Barry

bioRxiv : the preprint server for biology

2023

Language	English
Publishing date	2023-02-28
Publishing country	United States
Document type	Preprint
DOI	10.1101/2023.02.27.530276
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Article ; Online: PrePPI: A Structure Informed Proteome-wide Database of Protein-Protein Interactions.

Petrey, Donald / Zhao, Haiqing / Trudeau, Stephen J / Murray, Diana / Honig, Barry

Journal of molecular biology

2023 Volume 435, Issue 14, Page(s) 168052

Abstract: We present an updated version of the Predicting Protein-Protein Interactions (PrePPI) webserver which predicts PPIs on a proteome-wide scale. PrePPI combines structural and non-structural evidence within a Bayesian framework to compute a likelihood ratio ...

Abstract	We present an updated version of the Predicting Protein-Protein Interactions (PrePPI) webserver which predicts PPIs on a proteome-wide scale. PrePPI combines structural and non-structural evidence within a Bayesian framework to compute a likelihood ratio (LR) for essentially every possible pair of proteins in a proteome; the current database is for the human interactome. The structural modeling (SM) component is derived from template-based modeling and its application on a proteome-wide scale is enabled by a unique scoring function used to evaluate a putative complex. The updated version of PrePPI leverages AlphaFold structures that are parsed into individual domains. As has been demonstrated in earlier applications, PrePPI performs extremely well as measured by receiver operating characteristic curves derived from testing on E. coli and human protein-protein interaction (PPI) databases. A PrePPI database of ∼1.3 million human PPIs can be queried with a webserver application that comprises multiple functionalities for examining query proteins, template complexes, 3D models for predicted complexes, and related features (https://honiglab.c2b2.columbia.edu/PrePPI). PrePPI is a state-of-the-art resource that offers an unprecedented structure-informed view of the human interactome.
MeSH term(s)	Humans ; Bayes Theorem ; Databases, Protein ; Escherichia coli/metabolism ; Protein Interaction Mapping ; Proteome/metabolism
Chemical Substances	Proteome
Language	English
Publishing date	2023-03-17
Publishing country	Netherlands
Document type	Journal Article ; Research Support, N.I.H., Extramural
ZDB-ID	80229-3
ISSN	1089-8638 ; 0022-2836
ISSN (online)	1089-8638
ISSN	0022-2836
DOI	10.1016/j.jmb.2023.168052
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Fracture prediction and prevention: will newer technologies help?

Chang, Gregory / Honig, Stephen

Current opinion in rheumatology

2018 Volume 30, Issue 4, Page(s) 410–411

MeSH term(s)	Fractures, Bone/prevention & control ; Humans ; Osteoporosis
Language	English
Publishing date	2018-02-19
Publishing country	United States
Document type	Editorial ; Introductory Journal Article ; Research Support, N.I.H., Extramural
ZDB-ID	1045317-9
ISSN	1531-6963 ; 1040-8711
ISSN (online)	1531-6963
ISSN	1040-8711
DOI	10.1097/BOR.0000000000000518
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Article ; Online: PrePCI: A structure- and chemical similarity-informed database of predicted protein compound interactions.

Trudeau, Stephen J / Hwang, Howook / Mathur, Deepika / Begum, Kamrun / Petrey, Donald / Murray, Diana / Honig, Barry

Protein science : a publication of the Protein Society

2023 Volume 32, Issue 4, Page(s) e4594

Abstract: We describe the Predicting Protein-Compound Interactions (PrePCI) database which comprises over 5 billion predicted interactions between 6.8 million chemical compounds and 19,797 human proteins. PrePCI relies on a proteome-wide database of structural ... ...

Abstract	We describe the Predicting Protein-Compound Interactions (PrePCI) database which comprises over 5 billion predicted interactions between 6.8 million chemical compounds and 19,797 human proteins. PrePCI relies on a proteome-wide database of structural models based on both traditional modeling techniques and the AlphaFold Protein Structure Database. Sequence- and structural similarity-based metrics are established between template proteins, T, in the Protein Data Bank that bind compounds, C, and query proteins in the model database, Q. When the metrics exceed threshold values, it is assumed that C also binds to Q with a likelihood ratio (LR) derived from machine learning. If the relationship is based on structural similarity, the LR is based on a scoring function that measures the extent to which C is compatible with the binding site of Q as described in the LT-scanner algorithm. For every predicted complex derived in this way, chemical similarity based on the Tanimoto coefficient identifies other small molecules that may bind to Q. An overall LR for the binding of C to Q is obtained from Naive Bayesian statistics. The PrePCI database can be queried by entering a UniProt ID or gene name for a protein to obtain a list of compounds predicted to bind to it along with associated LRs. Alternatively, entering an identifier for the compound outputs a list of proteins it is predicted to bind. Specific applications of the database to lead discovery, elucidation of drug mechanism of action, and biological function annotation are described.
MeSH term(s)	Humans ; Bayes Theorem ; Databases, Chemical ; Proteins/chemistry ; Algorithms ; Databases, Protein
Chemical Substances	Proteins
Language	English
Publishing date	2023-02-09
Publishing country	United States
Document type	Journal Article ; Research Support, N.I.H., Extramural
ZDB-ID	1106283-6
ISSN	1469-896X ; 0961-8368
ISSN (online)	1469-896X
ISSN	0961-8368
DOI	10.1002/pro.4594
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Article ; Online: Application of Bayesian approaches in drug development: starting a virtuous cycle.

Ruberg, Stephen J / Beckers, Francois / Hemmings, Rob / Honig, Peter / Irony, Telba / LaVange, Lisa / Lieberman, Grazyna / Mayne, James / Moscicki, Richard

Nature reviews. Drug discovery

2023 Volume 22, Issue 3, Page(s) 235–250

Abstract: The pharmaceutical industry and its global regulators have routinely used frequentist statistical methods, such as null hypothesis significance testing and p values, for evaluation and approval of new treatments. The clinical drug development process, ... ...

Abstract	The pharmaceutical industry and its global regulators have routinely used frequentist statistical methods, such as null hypothesis significance testing and p values, for evaluation and approval of new treatments. The clinical drug development process, however, with its accumulation of data over time, can be well suited for the use of Bayesian statistical approaches that explicitly incorporate existing data into clinical trial design, analysis and decision-making. Such approaches, if used appropriately, have the potential to substantially reduce the time and cost of bringing innovative medicines to patients, as well as to reduce the exposure of patients in clinical trials to ineffective or unsafe treatment regimens. Nevertheless, despite advances in Bayesian methodology, the availability of the necessary computational power and growing amounts of relevant existing data that could be used, Bayesian methods remain underused in the clinical development and regulatory review of new therapies. Here, we highlight the value of Bayesian methods in drug development, discuss barriers to their application and recommend approaches to address them. Our aim is to engage stakeholders in the process of considering when the use of existing data is appropriate and how Bayesian methods can be implemented more routinely as an effective tool for doing so.
MeSH term(s)	Humans ; Bayes Theorem ; Research Design ; Drug Industry
Language	English
Publishing date	2023-02-15
Publishing country	England
Document type	Journal Article ; Review
ZDB-ID	2062954-0
ISSN	1474-1784 ; 1474-1776
ISSN (online)	1474-1784
ISSN	1474-1776
DOI	10.1038/s41573-023-00638-0
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Article ; Online: PrePPI: A Structure Informed Proteome-wide Database of Protein–Protein Interactions

Petrey, Donald / Zhao, Haiqing / Trudeau, Stephen J / Murray, Diana / Honig, Barry

Journal of Molecular Biology. 2023 July, v. 435, no. 14 p.168052-

2023

Abstract	We present an updated version of the Predicting Protein-Protein Interactions (PrePPI) webserver which predicts PPIs on a proteome-wide scale. PrePPI combines structural and non-structural evidence within a Bayesian framework to compute a likelihood ratio (LR) for essentially every possible pair of proteins in a proteome; the current database is for the human interactome. The structural modeling (SM) component is derived from template-based modeling and its application on a proteome-wide scale is enabled by a unique scoring function used to evaluate a putative complex. The updated version of PrePPI leverages AlphaFold structures that are parsed into individual domains. As has been demonstrated in earlier applications, PrePPI performs extremely well as measured by receiver operating characteristic curves derived from testing on E. coli and human protein–protein interaction (PPI) databases. A PrePPI database of ∼1.3 million human PPIs can be queried with a webserver application that comprises multiple functionalities for examining query proteins, template complexes, 3D models for predicted complexes, and related features (https://honiglab.c2b2.columbia.edu/PrePPI). PrePPI is a state-of-the-art resource that offers an unprecedented structure-informed view of the human interactome.
Keywords	Bayesian theory ; Escherichia coli ; databases ; humans ; molecular biology ; protein-protein interactions ; proteome ; database ; alphafold models ; structural modeling ; non-structural evidence
Language	English
Dates of publication	2023-07
Publishing place	Elsevier Ltd
Document type	Article ; Online
ZDB-ID	80229-3
ISSN	1089-8638 ; 0022-2836
ISSN (online)	1089-8638
ISSN	0022-2836
DOI	10.1016/j.jmb.2023.168052
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Article: Analysis of muscle, hip, and subcutaneous fat in osteoporosis patients with varying degrees of fracture risk using 3T Chemical Shift Encoded MRI.

Martel, Dimitri / Honig, Stephen / Monga, Anmol / Chang, Gregory

Bone reports

2020 Volume 12, Page(s) 100259

Abstract: Osteoporosis (OP) is a major disease that affects 200 million people worldwide. Fatty acid metabolism plays an important role in bone health and plays an important role in bone quality and remodeling. Increased bone marrow fat quantity has been shown to ... ...

Abstract	Osteoporosis (OP) is a major disease that affects 200 million people worldwide. Fatty acid metabolism plays an important role in bone health and plays an important role in bone quality and remodeling. Increased bone marrow fat quantity has been shown to be associated with a decrease in bone mineral density (BMD), which is used to predict fracture risk. Chemical-Shift Encoded magnetic resonance imaging (CSE-MRI) allows noninvasive and quantitative assessment of adipose tissues (AT). The aim of our study was to assess hip or proximal femoral bone marrow adipose tissue (BMAT), thigh muscle (MUS), and subcutaneous adipose tissue (SAT) in 128 OP subjects matched for age, BMD, weight and height with different degrees of fracture risk assessed through the FRAX score (low, moderate and high). Our results showed an increase in BMAT and in MUS in high compared to low fracture risk patients. We also assessed the relationship between fracture risk as assessed by FRAX and AT quantities. Overall, the results of this study suggest that assessment of adipose tissue via 3T CSE-MRI provides insight into the pathophysiology fracture risk by showing differences in the bone marrow and muscle fat content in subjects with similarly osteoporotic BMD as assessed by DXA, but with varying degrees of fracture risk as assessed by FRAX.
Language	English
Publishing date	2020-03-24
Publishing country	United States
Document type	Journal Article
ZDB-ID	2821774-3
ISSN	2352-1872
ISSN	2352-1872
DOI	10.1016/j.bonr.2020.100259
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Article ; Online: Osteoporosis - new treatments and updates.

Honig, Stephen

Bulletin of the NYU hospital for joint diseases

2011 Volume 69, Issue 3, Page(s) 253–256

MeSH term(s)	Bone Density Conservation Agents/adverse effects ; Bone Density Conservation Agents/therapeutic use ; Fractures, Bone/etiology ; Fractures, Bone/prevention & control ; Humans ; Osteoporosis/complications ; Osteoporosis/diagnosis ; Osteoporosis/drug therapy ; Treatment Outcome
Chemical Substances	Bone Density Conservation Agents
Language	English
Publishing date	2011
Publishing country	United States
Document type	Journal Article ; Review
ZDB-ID	390411-8
ISSN	1936-9727 ; 2328-5273 ; 1936-9719 ; 0018-5647 ; 0883-9344 ; 2328-4633
ISSN (online)	1936-9727 ; 2328-5273
ISSN	1936-9719 ; 0018-5647 ; 0883-9344 ; 2328-4633
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Osteoporosis - new treatments and updates.

Honig, Stephen

Bulletin of the NYU hospital for joint diseases

2010 Volume 68, Issue 3, Page(s) 166–170

Abstract: With the aging of the population, low bone mass states will be an increasing clinical issue for both men and women. More than 2 million osteoporotic fractures occur annually in the United States and it is estimated that nearly half of American Caucasian ... ...

Abstract	With the aging of the population, low bone mass states will be an increasing clinical issue for both men and women. More than 2 million osteoporotic fractures occur annually in the United States and it is estimated that nearly half of American Caucasian women over the age of 50 will experience at least one fragility fracture in their lifetime. Identifying subjects at increased risk for fracture and defining rational treatment strategies that balance risks with therapeutic benefits promises to be a major focus in the decade ahead. This review focuses on several areas of active interest in osteoporosis including the use of fracture assessment tools that help quantify time sensitive fracture risks by using patient specific data, advances in bone imaging and the relationship between structure and strength, new and future drug treatments for osteoporosis, and several unusual adverse clinical syndromes linked to the use of bisphosphonates. The topics chosen for this review highlight the increased understanding of bone structure and aging and how this knowledge can help clinicians in their treatment of osteoporosis.
MeSH term(s)	Age Factors ; Aged ; Bone Density Conservation Agents/adverse effects ; Bone Density Conservation Agents/therapeutic use ; Female ; Fractures, Bone/etiology ; Fractures, Bone/prevention & control ; Humans ; Male ; Middle Aged ; Osteoporosis/complications ; Osteoporosis/diagnosis ; Osteoporosis/drug therapy ; Risk Assessment ; Risk Factors ; Sex Factors ; Treatment Outcome
Chemical Substances	Bone Density Conservation Agents
Language	English
Publishing date	2010
Publishing country	United States
Document type	Journal Article ; Review
ZDB-ID	390411-8
ISSN	1936-9727 ; 2328-5273 ; 1936-9719 ; 0018-5647 ; 0883-9344 ; 2328-4633
ISSN (online)	1936-9727 ; 2328-5273
ISSN	1936-9719 ; 0018-5647 ; 0883-9344 ; 2328-4633
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Artificial intelligence, osteoporosis and fragility fractures.

Ferizi, Uran / Honig, Stephen / Chang, Gregory

Current opinion in rheumatology

2019 Volume 31, Issue 4, Page(s) 368–375

Abstract: Purpose of review: Artificial intelligence tools have found new applications in medical diagnosis. These tools have the potential to capture underlying trends and patterns, otherwise impossible with previous modeling capabilities. Machine learning and ... ...

Abstract	Purpose of review: Artificial intelligence tools have found new applications in medical diagnosis. These tools have the potential to capture underlying trends and patterns, otherwise impossible with previous modeling capabilities. Machine learning and deep learning models have found a role in osteoporosis, both to model the risk of fragility fracture, and to help with the identification and segmentation of images. Recent findings: Here we survey the latest research in the artificial intelligence application to the prediction of osteoporosis that has been published between January 2017 and March 2019. Around half of the articles that are covered here predict (by classification or regression) an indicator of osteoporosis, such as bone mass or fragility fractures; the other half of studies use tools for automatic segmentation of the images of patients with or at risk of osteoporosis. The data for these studies include diverse signal sources: acoustics, MRI, CT, and of course, X-rays. Summary: New methods for automatic image segmentation, and prediction of fracture risk show promising clinical value. Though these recent developments have had a successful initial application to osteoporosis research, their development is still under improvement, such as accounting for positive/negative class bias. We urge care when reporting accuracy metrics, and when comparing such metrics between different studies.
MeSH term(s)	Artificial Intelligence ; Bone Density ; Frailty ; Humans ; Magnetic Resonance Imaging/methods ; Osteoporotic Fractures/diagnosis ; Osteoporotic Fractures/metabolism ; Tomography, X-Ray Computed/methods
Language	English
Publishing date	2019-06-24
Publishing country	United States
Document type	Journal Article ; Review
ZDB-ID	1045317-9
ISSN	1531-6963 ; 1040-8711
ISSN (online)	1531-6963
ISSN	1040-8711
DOI	10.1097/BOR.0000000000000607
Database	MEDical Literature Analysis and Retrieval System OnLINE

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