Journal: Clinical Epidemiology

Article Title: Fecal occult blood versus DNA testing: indirect comparison in a colorectal cancer screening population

doi: 10.2147/CLEP.S136565

Figure Lengend Snippet: Recruitment and characteristics of study population in the BLITZ sample evaluating FOB Gold compared to the MSDT study evaluating Cologuard 7

Article Snippet: In a recent study in a large screening population from the USA and Canada, a multitarget stool DNA test (MSDT) that combines quantitative molecular assays for KRAS mutations, aberrant NDRG4 and BMP3 methylation, and β-actin with a hemoglobin immunoassay was compared with a commercial FIT.

Techniques:

Journal: Clinical Epidemiology

Article Title: Fecal occult blood versus DNA testing: indirect comparison in a colorectal cancer screening population

doi: 10.2147/CLEP.S136565

Figure Lengend Snippet: Comparison of diagnostic performance of Cologuard in the MSDT study 7 and of FOB Gold in the BLITZ study

Article Snippet: In a recent study in a large screening population from the USA and Canada, a multitarget stool DNA test (MSDT) that combines quantitative molecular assays for KRAS mutations, aberrant NDRG4 and BMP3 methylation, and β-actin with a hemoglobin immunoassay was compared with a commercial FIT.

Techniques: Comparison, Diagnostic Assay

Journal: Clinical Epidemiology

Article Title: Fecal occult blood versus DNA testing: indirect comparison in a colorectal cancer screening population

doi: 10.2147/CLEP.S136565

Figure Lengend Snippet: Sensitivity for detecting CRC and APCL ≥1 cm and specificity of FOB Gold (using the original cutoff) compared to Cologuard in the MSDT study (data derived from Imperiale et al), with and without age adjustment of FOB Gold results to age distribution of MSDT study population: FIT-O, FOB Gold using original cutoff, no age adjustment; FIT-A, FOB Gold using original cutoff, age adjusted. Abbreviations: CRC, colorectal cancer; APCL, advanced precancerous lesion; MSDT, multitarget stool DNA test; FIT, fecal immunochemical test.

Article Snippet: In a recent study in a large screening population from the USA and Canada, a multitarget stool DNA test (MSDT) that combines quantitative molecular assays for KRAS mutations, aberrant NDRG4 and BMP3 methylation, and β-actin with a hemoglobin immunoassay was compared with a commercial FIT.

Techniques: Derivative Assay

Journal: Clinical Epidemiology

Article Title: Fecal occult blood versus DNA testing: indirect comparison in a colorectal cancer screening population

doi: 10.2147/CLEP.S136565

Figure Lengend Snippet: Comparison of site-specific sensitivity of Cologuard in the MSDT study 7 and of FOB Gold in the BLITZ study

Article Snippet: In a recent study in a large screening population from the USA and Canada, a multitarget stool DNA test (MSDT) that combines quantitative molecular assays for KRAS mutations, aberrant NDRG4 and BMP3 methylation, and β-actin with a hemoglobin immunoassay was compared with a commercial FIT.

Techniques: Comparison

Journal: Cold Spring Harbor Molecular Case Studies

Article Title: Personalized oncogenomic analysis of metastatic adenoid cystic carcinoma: using whole-genome sequencing to inform clinical decision-making

doi: 10.1101/mcs.a002626

Figure Lengend Snippet: POG analyses in all five adenoid cystic carcinoma cases yielded informative and actionable results

Article Snippet: Patient 3 exhibited aberrations in MET expression and was subsequently enrolled in a phase I clinical trial with MGCD265, a multitarget and ATP-competitive inhibitor of c-MET and VEGFR1/2/3 that has recently been studied in lung cancer models ( ).

Techniques: Biomarker Discovery, Over Expression

Journal: Algorithms for Molecular Biology : AMB

Article Title: Infrared: a declarative tree decomposition-powered framework for bioinformatics

doi: 10.1186/s13015-024-00258-2

Figure Lengend Snippet: RNA multitarget design. A Three target RNA secondary structures of length 100 as 2D plots (by VARNA ) and dot-bracket strings; taken from a multitarget design benchmark set . B Histograms of the features G C content (left), and the Turner energies (kcal/mol) of the three targets (right) in 5000 sequences sampled from the multitarget design model \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\mathcal {N} _{\text {design}}$$\end{document} N design at weight -5 for every feature. One can observe that (1) equal weights lead to different mean energies for the targets; (2) strong control of the G C weight is required to avoid extreme G C content for stable designs. To automate the calibration of weights (and target specific feature value combinations), we suggest multidimensional Boltzmann sampling in Section “Multidimensional Boltzmann sampling”

Article Snippet: The histograms from Fig. show the feature distributions resulting from large negative weights for all features in a multitarget RNA design example.

Techniques: Control, Sampling

Journal: Algorithms for Molecular Biology : AMB

Article Title: Infrared: a declarative tree decomposition-powered framework for bioinformatics

doi: 10.1186/s13015-024-00258-2

Figure Lengend Snippet: Multidimensional Boltzmann sampling applied to RNA design. For the example of Fig. , we target G C content 85% and respective energies E1=-40, E2=-40, E3=-30 for the target structures (with tolerances of 5% GC content and 0.5 kcal/mol energy). Infrared ’s multidimensional Boltzmann sampling (MDBS) strategy starts from uniform sampling (weights 0 for every feature). It iteratively generates Boltzmann samples and updates the weights to move the (estimated) expectation closer to the targets. A Accepted samples as well as root mean square distance (RMSD) to the targets during this procedure, which considered over 70,000 total samples to generate 100 targeted samples. B Kernel density estimate plots: distributions of features for uniform sampling (blue) and sampling at the end of the MDBS run (red), where distributions are shifted to the targets (dashed red lines)

Article Snippet: The histograms from Fig. show the feature distributions resulting from large negative weights for all features in a multitarget RNA design example.

Techniques: Sampling