2007 |
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| 6. | Delisi, Alexe Dalgin Ramaswamy G G S R C; Bhanot, G Data perturbation independent diagnosis and validation of breast-cancer subtypes using clustering and patterns Journal Article Cancer Informatics Online, 2 , pp. 243-274, 2007. Abstract | Links | BibTeX | Tags: breast cancer, clusters @article{Alexe2007, title = {Data perturbation independent diagnosis and validation of breast-cancer subtypes using clustering and patterns}, author = {G Alexe G S Dalgin R Ramaswamy C Delisi and G Bhanot}, url = {http://www.ncbi.nlm.nih.gov/pmc/articles/pmc2675483/}, year = {2007}, date = {2007-02-19}, journal = { Cancer Informatics Online}, volume = {2}, pages = {243-274}, abstract = {Molecular stratification of disease based on expression levels of sets of genes can help guide therapeutic decisions if such classifications can be shown to be stable against variations in sample source and data perturbation. Classifications inferred from one set of samples in one lab should be able to consistently stratify a different set of samples in another lab. We present a method for assessing such stability and apply it to the breast cancer (BCA) datasets of Sorlie et al. 2003 and Ma et al. 2003. We find that within the now commonly accepted BCA categories identified by Sorlie et al. Luminal A and Basal are robust, but Luminal B and ERBB2+ are not. In particular, 36% of the samples identified as Luminal B and 55% identified as ERBB2+ cannot be assigned an accurate category because the classification is sensitive to data perturbation. We identify a “core cluster” of samples for each category, and from these we determine “patterns” of gene expression that distinguish the core clusters from each other. We find that the best markers for Luminal A and Basal are (ESR1, LIV1, GATA-3) and (CCNE1, LAD1, KRT5), respectively. Pathways enriched in the patterns regulate apoptosis, tissue remodeling and the immune response. We use a different dataset (Ma et al. 2003) to test the accuracy with which samples can be allocated to the four disease subtypes. We find, as expected, that the classification of samples identified as Luminal A and Basal is robust but classification into the other two subtypes is not.}, keywords = {breast cancer, clusters}, pubstate = {published}, tppubtype = {article} } Molecular stratification of disease based on expression levels of sets of genes can help guide therapeutic decisions if such classifications can be shown to be stable against variations in sample source and data perturbation. Classifications inferred from one set of samples in one lab should be able to consistently stratify a different set of samples in another lab. We present a method for assessing such stability and apply it to the breast cancer (BCA) datasets of Sorlie et al. 2003 and Ma et al. 2003. We find that within the now commonly accepted BCA categories identified by Sorlie et al. Luminal A and Basal are robust, but Luminal B and ERBB2+ are not. In particular, 36% of the samples identified as Luminal B and 55% identified as ERBB2+ cannot be assigned an accurate category because the classification is sensitive to data perturbation. We identify a "core cluster" of samples for each category, and from these we determine "patterns" of gene expression that distinguish the core clusters from each other. We find that the best markers for Luminal A and Basal are (ESR1, LIV1, GATA-3) and (CCNE1, LAD1, KRT5), respectively. Pathways enriched in the patterns regulate apoptosis, tissue remodeling and the immune response. We use a different dataset (Ma et al. 2003) to test the accuracy with which samples can be allocated to the four disease subtypes. We find, as expected, that the classification of samples identified as Luminal A and Basal is robust but classification into the other two subtypes is not. |
2000 |
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| 5. | Hunjan, J S; Ramaswamy, R Melting of the glassy mixed cluster, Ar9Xe10 Journal Article Indian Journal of Chemistry A, 39 , 2000, ISSN: 0975-0975. Abstract | Links | BibTeX | Tags: clusters, phase transition @article{Hunjan2000, title = {Melting of the glassy mixed cluster, Ar9Xe10}, author = {J S Hunjan and R Ramaswamy }, url = {http://nopr.niscpr.res.in/handle/123456789/25846}, issn = {0975-0975}, year = {2000}, date = {2000-03-01}, journal = {Indian Journal of Chemistry A}, volume = {39}, abstract = { We have studied the adiabatic instantaneous normal modes (INMs) for a mixed 19-particle cluster, Ar9Xe10 as a function of temperature. In finite clusters, the INM frequencies, which are well-separated, do not mix as a consequence of the noncrossing rule. The frequencies of the lowest few modes of the system progressively soften as the temperature is increased, and prior to melting, the lowest few modes become unstable: these INM frequencies become imaginary. Eigenvectors corresponding to the lowest modes that appear to be involved in the actual melting process are identified.}, keywords = {clusters, phase transition}, pubstate = {published}, tppubtype = {article} } We have studied the adiabatic instantaneous normal modes (INMs) for a mixed 19-particle cluster, Ar9Xe10 as a function of temperature. In finite clusters, the INM frequencies, which are well-separated, do not mix as a consequence of the noncrossing rule. The frequencies of the lowest few modes of the system progressively soften as the temperature is increased, and prior to melting, the lowest few modes become unstable: these INM frequencies become imaginary. Eigenvectors corresponding to the lowest modes that appear to be involved in the actual melting process are identified. |
1996 |
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| 4. | Nayak, S; Ramaswamy, R Solid <==> liquid transition in model (HF)n clusters Journal Article Molecular Physics, 89 (3), pp. 809-817, 1996. Abstract | Links | BibTeX | Tags: clusters, phase transition @article{Nayak1996, title = {Solid <==> liquid transition in model (HF)n clusters}, author = {S Nayak and R Ramaswamy }, url = {https://doi.org/10.1080/002689796173705}, doi = {10.1080/002689796173705}, year = {1996}, date = {1996-03-01}, journal = {Molecular Physics}, volume = {89}, number = {3}, pages = {809-817}, abstract = { We study the stability, energetics and dynamics of small model hydrogen fluoride clusters (HF) n using isoergic molecular dynamics simulations. The largest Lyapunov exponent is computed over the energy range when the clusters melt, and is found to be more useful in defining the onset of melting than Lindemann’s index. We also examine the power spectrum of potential energy fluctuations of clusters in the liquid state, which show 1/f dependence over a smaller frequency range than rare-gas clusters of comparable size.}, keywords = {clusters, phase transition}, pubstate = {published}, tppubtype = {article} } We study the stability, energetics and dynamics of small model hydrogen fluoride clusters (HF) n using isoergic molecular dynamics simulations. The largest Lyapunov exponent is computed over the energy range when the clusters melt, and is found to be more useful in defining the onset of melting than Lindemann’s index. We also examine the power spectrum of potential energy fluctuations of clusters in the liquid state, which show 1/f dependence over a smaller frequency range than rare-gas clusters of comparable size. |
1994 |
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| 3. | Nayak, S K; Ramaswamy, R Melting of (Ar–Xe)13 clusters: Surface-core effects Journal Article Journal of Physical Chemistry, 98 (37), pp. 9260 – 9264, 1994, ISSN: 0022-3654. Abstract | Links | BibTeX | Tags: clusters @article{Nayak1994b, title = {Melting of (Ar–Xe)13 clusters: Surface-core effects}, author = {S K Nayak and R Ramaswamy}, url = {https://doi.org/10.1021/j100088a028}, doi = {10.1021/j100088a028}, issn = {0022-3654}, year = {1994}, date = {1994-09-01}, journal = {Journal of Physical Chemistry}, volume = {98}, number = {37}, pages = {9260 – 9264}, abstract = {Potential energy fluctuations in the liquid phase of small atomic clusters (e.g. Ar13) have been seen to have long-range temporal correlations. This is manifest in a power-law decay for the power spectrum, which has a characteristic 1 //dependence on the frequency,/ (More precisely, the dependence is 1 //*, with a = 1.) In order to understand the origin of this behavior, we study the melting of mixed rare-gas clusters Ar^Xe and Xe^Ar (via molecular dynamics simulations). Substitution of atomic impurities introduces widely differing time scales in the dynamics, and we show that long-lived memory-effects have their origins in hierarchical relaxation processes arising in the motion of the atoms from the surface to the core and vice versa. }, keywords = {clusters}, pubstate = {published}, tppubtype = {article} } Potential energy fluctuations in the liquid phase of small atomic clusters (e.g. Ar13) have been seen to have long-range temporal correlations. This is manifest in a power-law decay for the power spectrum, which has a characteristic 1 //dependence on the frequency,/ (More precisely, the dependence is 1 //*, with a = 1.) In order to understand the origin of this behavior, we study the melting of mixed rare-gas clusters Ar^Xe and Xe^Ar (via molecular dynamics simulations). Substitution of atomic impurities introduces widely differing time scales in the dynamics, and we show that long-lived memory-effects have their origins in hierarchical relaxation processes arising in the motion of the atoms from the surface to the core and vice versa. |
1992 |
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| 2. | M Sasai, Ohmine I; Ramaswamy, R Long time fluctuation of liquid water: 1/f spectrum of energy fluctuation in hydrogen bond network rearrangement dynamics Journal Article The Journal of Chemical Physics, 96 (4), pp. 3045–3053, 1992, ISSN: 0021-9606. Abstract | Links | BibTeX | Tags: clusters @article{Sasai1992, title = {Long time fluctuation of liquid water: 1/f spectrum of energy fluctuation in hydrogen bond network rearrangement dynamics }, author = {M Sasai, I Ohmine and R Ramaswamy}, url = {https://pubs.aip.org/aip/jcp/article-pdf/96/4/3045/11026320/3045\_1\_online.pdf}, doi = {10.1063/1.461950}, issn = {0021-9606}, year = {1992}, date = {1992-02-15}, journal = {The Journal of Chemical Physics}, volume = {96}, number = {4}, pages = {3045–3053}, abstract = {The power spectrum of the potential energy fluctuation of liquid water is examined and found to yield so‐called 1/f frequency dependence (f is frequency). This is in sharp contrast to spectra of simple liquids (e.g., liquid argon), which exhibit a near white spectrum. This indicates that there exists an extended multiplicity of hydrogen bond network relaxations in liquid water. A simple model of cellular dynamics is proposed to explain this frequency dependence. On the other hand, the cluster dynamics of argon also involves energy fluctuations of a 1/f type, resulting from various relaxation processes at core and surface.}, keywords = {clusters}, pubstate = {published}, tppubtype = {article} } The power spectrum of the potential energy fluctuation of liquid water is examined and found to yield so‐called 1/f frequency dependence (f is frequency). This is in sharp contrast to spectra of simple liquids (e.g., liquid argon), which exhibit a near white spectrum. This indicates that there exists an extended multiplicity of hydrogen bond network relaxations in liquid water. A simple model of cellular dynamics is proposed to explain this frequency dependence. On the other hand, the cluster dynamics of argon also involves energy fluctuations of a 1/f type, resulting from various relaxation processes at core and surface. |
0000 |
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| 1. | Alexe, G; Dalgin, G S; Ramaswamy, R; Delisi, C; Bhanot, G Data Perturbation Independent Diagnosis and Validation of Breast Cancer Subtypes Using Clustering and Patterns Journal Article Cancer Informatics, 2 , pp. 117693510600200, 0000, ISSN: 1176-9351. Abstract | Links | BibTeX | Tags: breast cancer, clusters, diagnosis, multi-gene biomarkers, patterns @article{Alexe2017, title = {Data Perturbation Independent Diagnosis and Validation of Breast Cancer Subtypes Using Clustering and Patterns}, author = {G Alexe and G S Dalgin and R Ramaswamy and C Delisi and G Bhanot}, url = {https://ramramaswamy.org/papers/110.pdf}, doi = {10.1177/117693510600200006}, issn = {1176-9351}, journal = {Cancer Informatics}, volume = {2}, pages = {117693510600200}, abstract = {Molecular stratification of disease based on expression levels of sets of genes can help guide therapeutic decisions if such classifications can be shown to be stable against variations in sample source and data perturbation. Classifications inferred from one set of samples in one lab should be able to consistently stratify a different set of samples in another lab. We present a method for assessing such stability and apply it to the breast cancer (BCA) datasets of Sorlie et al. 2003 and Ma et al. 2003. We find that within the now commonly accepted BCA categories identified by Sorlie et al. Luminal A and Basal are robust, but Luminal B and ERBB2+ are not. In particular, 36% of the samples identified as Luminal B and 55% identified as ERBB2+ cannot be assigned an accurate category because the classification is sensitive to data perturbation. We identify a “core cluster” of samples for each category, and from these we determine “patterns” of gene expression that distinguish the core clusters from each other. We find that the best markers for Luminal A and Basal are (ESR1, LIV1, GATA-3) and (CCNE1, LAD1, KRT5), respectively. Pathways enriched in the patterns regulate apoptosis, tissue remodeling and the immune response. We use a different dataset (Ma et al. 2003) to test the accuracy with which samples can be allocated to the four disease subtypes. We find, as expected, that the classification of samples identified as Luminal A and Basal is robust but classification into the other two subtypes is not.}, keywords = {breast cancer, clusters, diagnosis, multi-gene biomarkers, patterns}, pubstate = {published}, tppubtype = {article} } Molecular stratification of disease based on expression levels of sets of genes can help guide therapeutic decisions if such classifications can be shown to be stable against variations in sample source and data perturbation. Classifications inferred from one set of samples in one lab should be able to consistently stratify a different set of samples in another lab. We present a method for assessing such stability and apply it to the breast cancer (BCA) datasets of Sorlie et al. 2003 and Ma et al. 2003. We find that within the now commonly accepted BCA categories identified by Sorlie et al. Luminal A and Basal are robust, but Luminal B and ERBB2+ are not. In particular, 36% of the samples identified as Luminal B and 55% identified as ERBB2+ cannot be assigned an accurate category because the classification is sensitive to data perturbation. We identify a "core cluster" of samples for each category, and from these we determine "patterns" of gene expression that distinguish the core clusters from each other. We find that the best markers for Luminal A and Basal are (ESR1, LIV1, GATA-3) and (CCNE1, LAD1, KRT5), respectively. Pathways enriched in the patterns regulate apoptosis, tissue remodeling and the immune response. We use a different dataset (Ma et al. 2003) to test the accuracy with which samples can be allocated to the four disease subtypes. We find, as expected, that the classification of samples identified as Luminal A and Basal is robust but classification into the other two subtypes is not. |