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  1. Article: Identifying Effective Biomarkers for Accurate Pancreatic Cancer Prognosis Using Statistical Machine Learning.

    Abu-Khudir, Rasha / Hafsa, Noor / Badr, Badr E

    Diagnostics (Basel, Switzerland)

    2023  Volume 13, Issue 19

    Abstract: Pancreatic cancer (PC) has one of the lowest survival rates among all major types of cancer. Consequently, it is one of the leading causes of mortality worldwide. Serum biomarkers historically correlate well with the early prognosis of post-surgical ... ...

    Abstract Pancreatic cancer (PC) has one of the lowest survival rates among all major types of cancer. Consequently, it is one of the leading causes of mortality worldwide. Serum biomarkers historically correlate well with the early prognosis of post-surgical complications of PC. However, attempts to identify an effective biomarker panel for the successful prognosis of PC were almost non-existent in the current literature. The current study investigated the roles of various serum biomarkers including carbohydrate antigen 19-9 (CA19-9), chemokine (C-X-C motif) ligand 8 (CXCL-8), procalcitonin (PCT), and other relevant clinical data for identifying PC progression, classified into sepsis, recurrence, and other post-surgical complications, among PC patients. The most relevant biochemical and clinical markers for PC prognosis were identified using a random-forest-powered feature elimination method. Using this informative biomarker panel, the selected machine-learning (ML) classification models demonstrated highly accurate results for classifying PC patients into three complication groups on independent test data. The superiority of the combined biomarker panel (Max AUC-ROC = 100%) was further established over using CA19-9 features exclusively (Max AUC-ROC = 75%) for the task of classifying PC progression. This novel study demonstrates the effectiveness of the combined biomarker panel in successfully diagnosing PC progression and other relevant complications among Egyptian PC survivors.
    Language English
    Publishing date 2023-09-29
    Publishing country Switzerland
    Document type Journal Article
    ZDB-ID 2662336-5
    ISSN 2075-4418
    ISSN 2075-4418
    DOI 10.3390/diagnostics13193091
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Article: Accurate prediction of pressure losses using machine learning for the pipeline transportation of emulsions.

    Hafsa, Noor / Rushd, Sayeed / Alzoubi, Hadeel / Al-Faiad, Majdi

    Heliyon

    2023  Volume 10, Issue 1, Page(s) e23591

    Abstract: One of the significant challenges to designing an emulsion transportation system is predicting frictional pressure losses with confidence. The state-of-the-art method for enhancing reliability in prediction is to employ artificial intelligence (AI) based ...

    Abstract One of the significant challenges to designing an emulsion transportation system is predicting frictional pressure losses with confidence. The state-of-the-art method for enhancing reliability in prediction is to employ artificial intelligence (AI) based on various machine learning (ML) tools. Six traditional and tree-based ML algorithms were analyzed for the prediction in the current study. A rigorous feature importance study using RFECV method and relevant statistical analysis was conducted to identify the parameters that significantly contributed to the prediction. Among 16 input variables, the fluid velocity, mass flow rate, and pipe diameter were evaluated as the top predictors to estimate the frictional pressure losses. The significance of the contributing parameters was further validated by estimation error trend analyses. A comprehensive assessment of the regression models demonstrated an ensemble of the top three regressors to excel over all other ML and theoretical models. The ensemble regressor showcased exceptional performance, as evidenced by its high R
    Language English
    Publishing date 2023-12-16
    Publishing country England
    Document type Journal Article
    ZDB-ID 2835763-2
    ISSN 2405-8440
    ISSN 2405-8440
    DOI 10.1016/j.heliyon.2023.e23591
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  3. Article ; Online: CSI 2.0: a significantly improved version of the Chemical Shift Index.

    Hafsa, Noor E / Wishart, David S

    Journal of biomolecular NMR

    2014  Volume 60, Issue 2-3, Page(s) 131–146

    Abstract: Protein chemical shifts have long been used by NMR spectroscopists to assist with secondary structure assignment and to provide useful distance and torsion angle constraint data for structure determination. One of the most widely used methods for ... ...

    Abstract Protein chemical shifts have long been used by NMR spectroscopists to assist with secondary structure assignment and to provide useful distance and torsion angle constraint data for structure determination. One of the most widely used methods for secondary structure identification is called the Chemical Shift Index (CSI). The CSI method uses a simple digital chemical shift filter to locate secondary structures along the protein chain using backbone (13)C and (1)H chemical shifts. While the CSI method is simple to use and easy to implement, it is only about 75-80% accurate. Here we describe a significantly improved version of the CSI (2.0) that uses machine-learning techniques to combine all six backbone chemical shifts ((13)Cα, (13)Cβ, (13)C, (15)N, (1)HN, (1)Hα) with sequence-derived features to perform far more accurate secondary structure identification. Our tests indicate that CSI 2.0 achieved an average identification accuracy (Q3) of 90.56% for a training set of 181 proteins in a repeated tenfold cross-validation and 89.35% for a test set of 59 proteins. This represents a significant improvement over other state-of-the-art chemical shift-based methods. In particular, the level of performance of CSI 2.0 is equal to that of standard methods, such as DSSP and STRIDE, used to identify secondary structures via 3D coordinate data. This suggests that CSI 2.0 could be used both in providing accurate NMR constraint data in the early stages of protein structure determination as well as in defining secondary structure locations in the final protein model(s). A CSI 2.0 web server (http://csi.wishartlab.com) is available for submitting the input queries for secondary structure identification.
    MeSH term(s) Carbon Isotopes ; Databases, Protein ; Nitrogen Isotopes ; Nuclear Magnetic Resonance, Biomolecular ; Probability ; Protein Structure, Secondary ; Proteins/chemistry ; Protons ; Software ; Support Vector Machine
    Chemical Substances Carbon Isotopes ; Nitrogen Isotopes ; Proteins ; Protons
    Language English
    Publishing date 2014-10-02
    Publishing country Netherlands
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 1081696-3
    ISSN 1573-5001 ; 0925-2738
    ISSN (online) 1573-5001
    ISSN 0925-2738
    DOI 10.1007/s10858-014-9863-x
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    In stock of ZB MED Cologne/Königswinter

    Zs.A 4132: 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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  4. Article ; Online: Rapid and reliable protein structure determination via chemical shift threading.

    Hafsa, Noor E / Berjanskii, Mark V / Arndt, David / Wishart, David S

    Journal of biomolecular NMR

    2017  Volume 70, Issue 1, Page(s) 33–51

    Abstract: Protein structure determination using nuclear magnetic resonance (NMR) spectroscopy can be both time-consuming and labor intensive. Here we demonstrate how chemical shift threading can permit rapid, robust, and accurate protein structure determination ... ...

    Abstract Protein structure determination using nuclear magnetic resonance (NMR) spectroscopy can be both time-consuming and labor intensive. Here we demonstrate how chemical shift threading can permit rapid, robust, and accurate protein structure determination using only chemical shift data. Threading is a relatively old bioinformatics technique that uses a combination of sequence information and predicted (or experimentally acquired) low-resolution structural data to generate high-resolution 3D protein structures. The key motivations behind using NMR chemical shifts for protein threading lie in the fact that they are easy to measure, they are available prior to 3D structure determination, and they contain vital structural information. The method we have developed uses not only sequence and chemical shift similarity but also chemical shift-derived secondary structure, shift-derived super-secondary structure, and shift-derived accessible surface area to generate a high quality protein structure regardless of the sequence similarity (or lack thereof) to a known structure already in the PDB. The method (called E-Thrifty) was found to be very fast (often < 10 min/structure) and to significantly outperform other shift-based or threading-based structure determination methods (in terms of top template model accuracy)-with an average TM-score performance of 0.68 (vs. 0.50-0.62 for other methods). Coupled with recent developments in chemical shift refinement, these results suggest that protein structure determination, using only NMR chemical shifts, is becoming increasingly practical and reliable. E-Thrifty is available as a web server at http://ethrifty.ca .
    MeSH term(s) Amino Acid Sequence ; Nuclear Magnetic Resonance, Biomolecular/methods ; Protein Conformation ; Protein Structure, Secondary ; Proteins/chemistry ; Time Factors
    Chemical Substances Proteins
    Language English
    Publishing date 2017-12-01
    Publishing country Netherlands
    Document type Journal Article
    ZDB-ID 1081696-3
    ISSN 1573-5001 ; 0925-2738
    ISSN (online) 1573-5001
    ISSN 0925-2738
    DOI 10.1007/s10858-017-0154-1
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    In stock of ZB MED Cologne/Königswinter

    Zs.A 4132: Show issues Location:
    Je nach Verfügbarkeit (siehe Angabe bei Bestand)
    bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular
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    ab Jg. 2022: Lesesaal (EG)

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  5. Article ; Online: CSI 3.0: a web server for identifying secondary and super-secondary structure in proteins using NMR chemical shifts.

    Hafsa, Noor E / Arndt, David / Wishart, David S

    Nucleic acids research

    2015  Volume 43, Issue W1, Page(s) W370–7

    Abstract: The Chemical Shift Index or CSI 3.0 (http://csi3.wishartlab.com) is a web server designed to accurately identify the location of secondary and super-secondary structures in protein chains using only nuclear magnetic resonance (NMR) backbone chemical ... ...

    Abstract The Chemical Shift Index or CSI 3.0 (http://csi3.wishartlab.com) is a web server designed to accurately identify the location of secondary and super-secondary structures in protein chains using only nuclear magnetic resonance (NMR) backbone chemical shifts and their corresponding protein sequence data. Unlike earlier versions of CSI, which only identified three types of secondary structure (helix, β-strand and coil), CSI 3.0 now identifies total of 11 types of secondary and super-secondary structures, including helices, β-strands, coil regions, five common β-turns (type I, II, I', II' and VIII), β hairpins as well as interior and edge β-strands. CSI 3.0 accepts experimental NMR chemical shift data in multiple formats (NMR Star 2.1, NMR Star 3.1 and SHIFTY) and generates colorful CSI plots (bar graphs) and secondary/super-secondary structure assignments. The output can be readily used as constraints for structure determination and refinement or the images may be used for presentations and publications. CSI 3.0 uses a pipeline of several well-tested, previously published programs to identify the secondary and super-secondary structures in protein chains. Comparisons with secondary and super-secondary structure assignments made via standard coordinate analysis programs such as DSSP, STRIDE and VADAR on high-resolution protein structures solved by X-ray and NMR show >90% agreement between those made with CSI 3.0.
    MeSH term(s) Algorithms ; Internet ; Nuclear Magnetic Resonance, Biomolecular ; Protein Structure, Secondary ; Software
    Language English
    Publishing date 2015-07-01
    Publishing country England
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 186809-3
    ISSN 1362-4962 ; 1362-4954 ; 0301-5610 ; 0305-1048
    ISSN (online) 1362-4962 ; 1362-4954
    ISSN 0301-5610 ; 0305-1048
    DOI 10.1093/nar/gkv494
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    In stock of ZB MED Cologne/Königswinter

    Zs.A 1067: Show issues Location:
    Je nach Verfügbarkeit (siehe Angabe bei Bestand)
    bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular
    Jg. 1995 - 2021: Lesesall (1.OG)
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  6. Article ; Online: Accessible surface area from NMR chemical shifts.

    Hafsa, Noor E / Arndt, David / Wishart, David S

    Journal of biomolecular NMR

    2015  Volume 62, Issue 3, Page(s) 387–401

    Abstract: Accessible surface area (ASA) is the surface area of an atom, amino acid or biomolecule that is exposed to solvent. The calculation of a molecule's ASA requires three-dimensional coordinate data and the use of a "rolling ball" algorithm to both define ... ...

    Abstract Accessible surface area (ASA) is the surface area of an atom, amino acid or biomolecule that is exposed to solvent. The calculation of a molecule's ASA requires three-dimensional coordinate data and the use of a "rolling ball" algorithm to both define and calculate the ASA. For polymers such as proteins, the ASA for individual amino acids is closely related to the hydrophobicity of the amino acid as well as its local secondary and tertiary structure. For proteins, ASA is a structural descriptor that can often be as informative as secondary structure. Consequently there has been considerable effort over the past two decades to try to predict ASA from protein sequence data and to use ASA information (derived from chemical modification studies) as a structure constraint. Recently it has become evident that protein chemical shifts are also sensitive to ASA. Given the potential utility of ASA estimates as structural constraints for NMR we decided to explore this relationship further. Using machine learning techniques (specifically a boosted tree regression model) we developed an algorithm called "ShiftASA" that combines chemical-shift and sequence derived features to accurately estimate per-residue fractional ASA values of water-soluble proteins. This method showed a correlation coefficient between predicted and experimental values of 0.79 when evaluated on a set of 65 independent test proteins, which was an 8.2 % improvement over the next best performing (sequence-only) method. On a separate test set of 92 proteins, ShiftASA reported a mean correlation coefficient of 0.82, which was 12.3 % better than the next best performing method. ShiftASA is available as a web server ( http://shiftasa.wishartlab.com ) for submitting input queries for fractional ASA calculation.
    MeSH term(s) Algorithms ; Internet ; Machine Learning ; Nuclear Magnetic Resonance, Biomolecular/methods ; Proteins/chemistry ; Software ; Surface Properties
    Chemical Substances Proteins
    Language English
    Publishing date 2015-07
    Publishing country Netherlands
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 1081696-3
    ISSN 1573-5001 ; 0925-2738
    ISSN (online) 1573-5001
    ISSN 0925-2738
    DOI 10.1007/s10858-015-9957-0
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    In stock of ZB MED Cologne/Königswinter

    Zs.A 4132: 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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  7. Article ; Online: Muscle percentage index as a marker of disease severity in golden retriever muscular dystrophy.

    Eresen, Aydin / Hafsa, Noor E / Alic, Lejla / Birch, Sharla M / Griffin, John F / Kornegay, Joe N / Ji, Jim X

    Muscle & nerve

    2019  Volume 60, Issue 5, Page(s) 621–628

    Abstract: Introduction: Golden retriever muscular dystrophy (GRMD) is a spontaneous X-linked canine model of Duchenne muscular dystrophy that resembles the human condition. Muscle percentage index (MPI) is proposed as an imaging biomarker of disease severity in ... ...

    Abstract Introduction: Golden retriever muscular dystrophy (GRMD) is a spontaneous X-linked canine model of Duchenne muscular dystrophy that resembles the human condition. Muscle percentage index (MPI) is proposed as an imaging biomarker of disease severity in GRMD.
    Methods: To assess MPI, we used MRI data acquired from nine GRMD samples using a 4.7 T small-bore scanner. A machine learning approach was used with eight raw quantitative mapping of MRI data images (T1m, T2m, two Dixon maps, and four diffusion tensor imaging maps), three types of texture descriptors (local binary pattern, gray-level co-occurrence matrix, gray-level run-length matrix), and a gradient descriptor (histogram of oriented gradients).
    Results: The confusion matrix, averaged over all samples, showed 93.5% of muscle pixels classified correctly. The classification, optimized in a leave-one-out cross-validation, provided an average accuracy of 80% with a discrepancy in overestimation for young (8%) and old (20%) dogs.
    Discussion: MPI could be useful for quantifying GRMD severity, but careful interpretation is needed for severe cases.
    MeSH term(s) Animals ; Disease Models, Animal ; Dogs ; Magnetic Resonance Imaging ; Muscle, Skeletal/diagnostic imaging ; Muscle, Skeletal/pathology ; Muscular Dystrophy, Animal/diagnostic imaging ; Muscular Dystrophy, Animal/pathology ; Muscular Dystrophy, Duchenne/diagnostic imaging ; Muscular Dystrophy, Duchenne/pathology ; Severity of Illness Index
    Language English
    Publishing date 2019-08-28
    Publishing country United States
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 438353-9
    ISSN 1097-4598 ; 0148-639X
    ISSN (online) 1097-4598
    ISSN 0148-639X
    DOI 10.1002/mus.26657
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    Zs.A 1449: Show issues Location:
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    Zs.MO 551: Show issues

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