Phyloproteomics is a novel analytical tool that solves the issue of comparability between proteomic analyses, utilizes a total spectrum-parsing algorithm, and produces biologically meaningful classification of specimens. Phyloproteomics employs two algorithms: a new parsing algorithm (UNIPAL) and a phylogenetic algorithm (MIX). By outgroup comparison, the parsing algorithm identifies novel or vanished MS peaks and peaks signifying up or down regulated proteins and scores them as derived or ancestral. The phylogenetic algorithm uses the latter scores to produce a biologically meaningful classification of the specimens.
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http://dx.doi.org/10.1021/pr0504485 | DOI Listing |
Nat Commun
December 2024
GMU-GIBH Joint School of Life Sciences, The Guangdong-Hong Kong-Macao Joint Laboratory for Cell Fate Regulation and Diseases, Guangzhou Laboratory, Guangzhou Medical University, Guangzhou, China.
Cell type deconvolution methods can impute cell proportions from bulk transcriptomics data, revealing changes in disease progression or organ development. But benchmarking studies often use simulated bulk data from the same source as the reference, which limits its application scenarios. This study examines batch effects in deconvolution and introduces SCCAF-D, a computational workflow that ensures a Pearson Correlation Coefficient above 0.
View Article and Find Full Text PDFEClinicalMedicine
January 2025
Canadian Cancer Trials Group, Queen's University, Kingston, ON, Canada.
Background: Dual inhibition of cytotoxic T-lymphocyte associated protein 4 (CTLA-4) and programmed death ligand 1 (PD-L1) has been shown to be an effective treatment strategy in many cancers. We sought to determine the objective response rate of combination durvalumab (D) plus tremelimumab (TM) in parallel cohorts of patients with carefully selected rare cancer types in which these agents had not previously been evaluated in phase II trials and for which there was clinical or biological rationale for dual immune checkpoint inhibitor therapy to be active.
Methods: We designed a multi-centre, non-blinded, open-label phase II basket trial with each of the following 8 rare cancers considered a separate phase II trial: salivary carcinoma, carcinoma of unknown primary (CUP) with tumour infiltrating lymphocytes and/or expressing PD-L1, mucosal melanoma, acral melanoma, osteosarcoma, undifferentiated pleomorphic sarcoma, clear cell carcinoma of the ovary (CCCO) or squamous cell carcinoma of the anal canal (SCCA).
Sci Total Environ
December 2024
Restoration Research Team (Fishes/Amphibians & Reptiles), Research Center for Endangered Species, National Institute of Ecology, Yeongyang 36531, Republic of Korea. Electronic address:
Because of their noninvasive nature and high detection sensitivity, eDNA-based aquatic ecosystem surveys are useful for monitoring rare, elusive indicator species. Advancements in statistical techniques have expanded their use beyond simple population tracking to predict potential habitats based on the environmental conditions of sites detected eDNA. This study used species-specific molecular marker and targeted qPCR techniques to assess the distribution and habitat requirements of the endangered Gobiobotia naktongensis, a flagship fish species in Korean sandy river systems with increased public interest related to habitat restoration evaluations associated with dam construction.
View Article and Find Full Text PDFBioData Min
December 2024
School of Computing, Queen's University, 557 Goodwin Hall, 21-25 Union St, Kingston, K7L 2N8, Ontario, Canada.
Background: Epistasis, the phenomenon where the effect of one gene (or variant) is masked or modified by one or more other genes, significantly contributes to the phenotypic variance of complex traits. Traditionally, epistasis has been modeled using the Cartesian epistatic model, a multiplicative approach based on standard statistical regression. However, a recent study investigating epistasis in obesity-related traits has identified potential limitations of the Cartesian epistatic model, revealing that it likely only detects a fraction of the genetic interactions occurring in natural systems.
View Article and Find Full Text PDFMath Biosci
December 2024
Department of Biology, Duke University, Durham, NC, USA.
Synchronized behavior among individuals, broadly defined, is a ubiquitous feature of populations. Understanding mechanisms of (de)synchronization demands meaningful, interpretable, computable quantifications of synchrony, relevant to measurements that can be made of complex, dynamic populations. Despite the importance to analyzing and modeling populations, existing notions of synchrony often lack rigorous definitions, may be specialized to a particular experimental system and/or measurement, or may have undesirable properties that limit their utility.
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