Results 61 to 70 of about 2,194,803 (325)

The Inconsistency of the Algorithms of Jaro–Winkler and Needleman–Wunsch Applied to DNA Chain Similarity Results

Mathematics
There are many different algorithms for calculating the distances between DNA chains. Different algorithms for determining such distances give different results.
Boris Melnikov
doaj   +1 more source

Clustering the objective interestingness measures based on tendency of variation in statistical implications

EAI Endorsed Transactions on Context-aware Systems and Applications, 2016
In recent years, the research cluster of objective interestingness measures has rapidly developed in order to assist users to choose the appropriate measure for their application.
Nghia Quoc Phan, Vinh Cong Phan, Hung Huu Huynh, Hiep Xuan Huynh   +3 more
doaj   +1 more source

On spectral radius and Nordhaus-Gaddum type inequalities of the generalized distance matrix of graphs

Karpatsʹkì Matematičnì Publìkacìï, 2022
If $Tr(G)$ and $D(G)$ are respectively the diagonal matrix of vertex transmission degrees and distance matrix of a connected graph $G$, the generalized distance matrix $D_{\alpha}(G)$ is defined as $D_{\alpha}(G)=\alpha ~Tr(G)+(1-\alpha)~D(G)$, where $0 ...
M. Merajuddin, S. Bhatnagar, S. Pirzada
doaj   +1 more source

In vitro models of cancer‐associated fibroblast heterogeneity uncover subtype‐specific effects of CRISPR perturbations

Molecular Oncology, EarlyView.
Development of therapies targeting cancer‐associated fibroblasts (CAFs) necessitates preclinical model systems that faithfully represent CAF–tumor biology. We established an in vitro coculture system of patient‐derived pancreatic CAFs and tumor cell lines and demonstrated its recapitulation of primary CAF–tumor biology with single‐cell transcriptomics ...
Elysia Saputra, Shamsudheen Karuthedath Vellarikkal, Lixia Li, Hong Sun, Khoa Nguyen, Amber Montano, Suchitra Natarajan, Federica Piccioni, Alex Michael Tamburino, Xin Yu, Aleksandra Katarzyna Olow   +10 more
wiley   +1 more source

The second immanant of some combinatorial matrices [PDF]

Transactions on Combinatorics, 2015
Let $A = (a_{i,j})_{1 leq i,j leq n}$ be an $n times n$ matrix where $n geq 2$. Let $dt(A)$, its second immanant be the immanant corresponding to the partition $lambda_2 = 2,1^{n-2}$.
R. B. Bapat, Sivaramakrishnan Sivasubramanian   +1 more
doaj  

Extremal Unicyclic Graphs With Minimal Distance Spectral Radius

Discussiones Mathematicae Graph Theory, 2014
The distance spectral radius ρ(G) of a graph G is the largest eigenvalue of the distance matrix D(G). Let U (n,m) be the class of unicyclic graphs of order n with given matching number m (m ≠ 3).
Lu Hongyan, Luo Jing, Zhu Zhongxun
doaj   +1 more source

K-Matrix: A Novel Change-Pattern Mining Method for SAR Image Time Series

Remote Sensing, 2019
In this paper, we present a novel method for change-pattern mining in Synthetic Aperture Radar (SAR) image time series based on a distance matrix clustering algorithm, called K-Matrix.
Dong Peng, Ting Pan, Wen Yang, Heng-Chao Li   +3 more
doaj   +1 more source

Study of Air Pressure on Arc Ion Plating on Epoxy Resin Matrix Composites at Meter Range Working Distance [PDF]

, 2023
Chao Meng, Zhandong Ma, Hongming Xue, Shoujing Yue, Zhen Gao, Xing Liu   +5 more
openalex   +1 more source

Strength through diversity: how cancers thrive when clones cooperate

Molecular Oncology, EarlyView.
Intratumor heterogeneity can offer direct benefits to the tumor through cooperation between different clones. In this review, Kuiken et al. discuss existing evidence for clonal cooperativity to identify overarching principles, and highlight how novel technological developments could address remaining open questions.
Marije C. Kuiken, Maartje Witsen, Emile E. Voest, Krijn K. Dijkstra   +3 more
wiley   +1 more source

Leibniz seminorms for "Matrix algebras converge to the sphere" [PDF]

, 2010
In an earlier paper of mine relating vector bundles and Gromov-Hausdorff distance for ordinary compact metric spaces, it was crucial that the Lipschitz seminorms from the metrics satisfy a strong Leibniz property.
Rieffel, Marc A.
core