The Experts below are selected from a list of 20658 Experts worldwide ranked by ideXlab platform
Suryanarayanarao Ramakumar - One of the best experts on this subject based on the ideXlab platform.
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detailed protein sequence alignment based on spectral Similarity Score sss
BMC Bioinformatics, 2005Co-Authors: Kshitiz Gupta, Dina Thomas, S V Vidya, K V Venkatesh, Suryanarayanarao RamakumarAbstract:Background The chemical property and biological function of a protein is a direct consequence of its primary structure. Several algorithms have been developed which determine alignment and Similarity of primary protein sequences. However, character based Similarity cannot provide insight into the structural aspects of a protein. We present a method based on spectral Similarity to compare subsequences of amino acids that behave similarly but are not aligned well by considering amino acids as mere characters. This approach finds a Similarity Score between sequences based on any given attribute, like hydrophobicity of amino acids, on the basis of spectral information after partial conversion to the frequency domain.
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Detailed protein sequence alignment based on Spectral Similarity Score (SSS)
BMC Bioinformatics, 2005Co-Authors: Kshitiz Gupta, Dina Thomas, S V Vidya, K V Venkatesh, Suryanarayanarao RamakumarAbstract:Background The chemical property and biological function of a protein is a direct consequence of its primary structure. Several algorithms have been developed which determine alignment and Similarity of primary protein sequences. However, character based Similarity cannot provide insight into the structural aspects of a protein. We present a method based on spectral Similarity to compare subsequences of amino acids that behave similarly but are not aligned well by considering amino acids as mere characters. This approach finds a Similarity Score between sequences based on any given attribute, like hydrophobicity of amino acids, on the basis of spectral information after partial conversion to the frequency domain. Results Distance matrices of various branches of the human kinome, that is the full complement of human kinases, were developed that matched the phylogenetic tree of the human kinome establishing the efficacy of the global alignment of the algorithm. PKCd and PKCe kinases share close biological properties and structural similarities but do not give high Scores with character based alignments. Detailed comparison established close similarities between subsequences that do not have any significant character identity. We compared their known 3D structures to establish that the algorithm is able to pick subsequences that are not considered similar by character based matching algorithms but share structural similarities. Similarly many subsequences with low character identity were picked between xyna-theau and xyna-clotm F/10 xylanases. Comparison of 3D structures of the subsequences confirmed the claim of Similarity in structure. Conclusion An algorithm is developed which is inspired by successful application of spectral Similarity applied to music sequences. The method captures subsequences that do not align by traditional character based alignment tools but give rise to similar secondary and tertiary structures. The Spectral Similarity Score (SSS) is an extension to the conventional Similarity methods and results indicate that it holds a strong potential for analysis of various biological sequences and structural variations in proteins.
Kshitiz Gupta - One of the best experts on this subject based on the ideXlab platform.
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detailed protein sequence alignment based on spectral Similarity Score sss
BMC Bioinformatics, 2005Co-Authors: Kshitiz Gupta, Dina Thomas, S V Vidya, K V Venkatesh, Suryanarayanarao RamakumarAbstract:Background The chemical property and biological function of a protein is a direct consequence of its primary structure. Several algorithms have been developed which determine alignment and Similarity of primary protein sequences. However, character based Similarity cannot provide insight into the structural aspects of a protein. We present a method based on spectral Similarity to compare subsequences of amino acids that behave similarly but are not aligned well by considering amino acids as mere characters. This approach finds a Similarity Score between sequences based on any given attribute, like hydrophobicity of amino acids, on the basis of spectral information after partial conversion to the frequency domain.
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Detailed protein sequence alignment based on Spectral Similarity Score (SSS)
BMC Bioinformatics, 2005Co-Authors: Kshitiz Gupta, Dina Thomas, S V Vidya, K V Venkatesh, Suryanarayanarao RamakumarAbstract:Background The chemical property and biological function of a protein is a direct consequence of its primary structure. Several algorithms have been developed which determine alignment and Similarity of primary protein sequences. However, character based Similarity cannot provide insight into the structural aspects of a protein. We present a method based on spectral Similarity to compare subsequences of amino acids that behave similarly but are not aligned well by considering amino acids as mere characters. This approach finds a Similarity Score between sequences based on any given attribute, like hydrophobicity of amino acids, on the basis of spectral information after partial conversion to the frequency domain. Results Distance matrices of various branches of the human kinome, that is the full complement of human kinases, were developed that matched the phylogenetic tree of the human kinome establishing the efficacy of the global alignment of the algorithm. PKCd and PKCe kinases share close biological properties and structural similarities but do not give high Scores with character based alignments. Detailed comparison established close similarities between subsequences that do not have any significant character identity. We compared their known 3D structures to establish that the algorithm is able to pick subsequences that are not considered similar by character based matching algorithms but share structural similarities. Similarly many subsequences with low character identity were picked between xyna-theau and xyna-clotm F/10 xylanases. Comparison of 3D structures of the subsequences confirmed the claim of Similarity in structure. Conclusion An algorithm is developed which is inspired by successful application of spectral Similarity applied to music sequences. The method captures subsequences that do not align by traditional character based alignment tools but give rise to similar secondary and tertiary structures. The Spectral Similarity Score (SSS) is an extension to the conventional Similarity methods and results indicate that it holds a strong potential for analysis of various biological sequences and structural variations in proteins.
Han Hu - One of the best experts on this subject based on the ideXlab platform.
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spatial temporal relation networks for multi object tracking
International Conference on Computer Vision, 2019Co-Authors: Jiarui Xu, Zheng Zhang, Han HuAbstract:Recent progress in multiple object tracking (MOT) has shown that a robust Similarity Score is a key to the success of trackers. A good Similarity Score is expected to reflect multiple cues, e.g. appearance, location, and topology, over a long period of time. However, these cues are heterogeneous, making them hard to be combined in a unified network. As a result, existing methods usually encode them in separate networks or require a complex training approach. In this paper, we present a unified framework for Similarity measurement based on spatial-temporal relation network which could simultaneously encode various cues and perform reasoning across both spatial and temporal domains. We also study the feature representation of a tracklet-object pair in depth, showing a proper design of the pair features can well empower the trackers. The resulting approach is named spatial-temporal relation networks (STRN). It runs in a feed-forward way and can be trained in an end-to-end manner. The state-of-the-art accuracy was achieved on all of the MOT15$\sim$17 benchmarks using public detection and online settings.
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spatial temporal relation networks for multi object tracking
arXiv: Computer Vision and Pattern Recognition, 2019Co-Authors: Jiarui Xu, Zheng Zhang, Han HuAbstract:Recent progress in multiple object tracking (MOT) has shown that a robust Similarity Score is key to the success of trackers. A good Similarity Score is expected to reflect multiple cues, e.g. appearance, location, and topology, over a long period of time. However, these cues are heterogeneous, making them hard to be combined in a unified network. As a result, existing methods usually encode them in separate networks or require a complex training approach. In this paper, we present a unified framework for Similarity measurement which could simultaneously encode various cues and perform reasoning across both spatial and temporal domains. We also study the feature representation of a tracklet-object pair in depth, showing a proper design of the pair features can well empower the trackers. The resulting approach is named spatial-temporal relation networks (STRN). It runs in a feed-forward way and can be trained in an end-to-end manner. The state-of-the-art accuracy was achieved on all of the MOT15-17 benchmarks using public detection and online settings.
K V Venkatesh - One of the best experts on this subject based on the ideXlab platform.
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detailed protein sequence alignment based on spectral Similarity Score sss
BMC Bioinformatics, 2005Co-Authors: Kshitiz Gupta, Dina Thomas, S V Vidya, K V Venkatesh, Suryanarayanarao RamakumarAbstract:Background The chemical property and biological function of a protein is a direct consequence of its primary structure. Several algorithms have been developed which determine alignment and Similarity of primary protein sequences. However, character based Similarity cannot provide insight into the structural aspects of a protein. We present a method based on spectral Similarity to compare subsequences of amino acids that behave similarly but are not aligned well by considering amino acids as mere characters. This approach finds a Similarity Score between sequences based on any given attribute, like hydrophobicity of amino acids, on the basis of spectral information after partial conversion to the frequency domain.
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Detailed protein sequence alignment based on Spectral Similarity Score (SSS)
BMC Bioinformatics, 2005Co-Authors: Kshitiz Gupta, Dina Thomas, S V Vidya, K V Venkatesh, Suryanarayanarao RamakumarAbstract:Background The chemical property and biological function of a protein is a direct consequence of its primary structure. Several algorithms have been developed which determine alignment and Similarity of primary protein sequences. However, character based Similarity cannot provide insight into the structural aspects of a protein. We present a method based on spectral Similarity to compare subsequences of amino acids that behave similarly but are not aligned well by considering amino acids as mere characters. This approach finds a Similarity Score between sequences based on any given attribute, like hydrophobicity of amino acids, on the basis of spectral information after partial conversion to the frequency domain. Results Distance matrices of various branches of the human kinome, that is the full complement of human kinases, were developed that matched the phylogenetic tree of the human kinome establishing the efficacy of the global alignment of the algorithm. PKCd and PKCe kinases share close biological properties and structural similarities but do not give high Scores with character based alignments. Detailed comparison established close similarities between subsequences that do not have any significant character identity. We compared their known 3D structures to establish that the algorithm is able to pick subsequences that are not considered similar by character based matching algorithms but share structural similarities. Similarly many subsequences with low character identity were picked between xyna-theau and xyna-clotm F/10 xylanases. Comparison of 3D structures of the subsequences confirmed the claim of Similarity in structure. Conclusion An algorithm is developed which is inspired by successful application of spectral Similarity applied to music sequences. The method captures subsequences that do not align by traditional character based alignment tools but give rise to similar secondary and tertiary structures. The Spectral Similarity Score (SSS) is an extension to the conventional Similarity methods and results indicate that it holds a strong potential for analysis of various biological sequences and structural variations in proteins.
S V Vidya - One of the best experts on this subject based on the ideXlab platform.
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detailed protein sequence alignment based on spectral Similarity Score sss
BMC Bioinformatics, 2005Co-Authors: Kshitiz Gupta, Dina Thomas, S V Vidya, K V Venkatesh, Suryanarayanarao RamakumarAbstract:Background The chemical property and biological function of a protein is a direct consequence of its primary structure. Several algorithms have been developed which determine alignment and Similarity of primary protein sequences. However, character based Similarity cannot provide insight into the structural aspects of a protein. We present a method based on spectral Similarity to compare subsequences of amino acids that behave similarly but are not aligned well by considering amino acids as mere characters. This approach finds a Similarity Score between sequences based on any given attribute, like hydrophobicity of amino acids, on the basis of spectral information after partial conversion to the frequency domain.
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Detailed protein sequence alignment based on Spectral Similarity Score (SSS)
BMC Bioinformatics, 2005Co-Authors: Kshitiz Gupta, Dina Thomas, S V Vidya, K V Venkatesh, Suryanarayanarao RamakumarAbstract:Background The chemical property and biological function of a protein is a direct consequence of its primary structure. Several algorithms have been developed which determine alignment and Similarity of primary protein sequences. However, character based Similarity cannot provide insight into the structural aspects of a protein. We present a method based on spectral Similarity to compare subsequences of amino acids that behave similarly but are not aligned well by considering amino acids as mere characters. This approach finds a Similarity Score between sequences based on any given attribute, like hydrophobicity of amino acids, on the basis of spectral information after partial conversion to the frequency domain. Results Distance matrices of various branches of the human kinome, that is the full complement of human kinases, were developed that matched the phylogenetic tree of the human kinome establishing the efficacy of the global alignment of the algorithm. PKCd and PKCe kinases share close biological properties and structural similarities but do not give high Scores with character based alignments. Detailed comparison established close similarities between subsequences that do not have any significant character identity. We compared their known 3D structures to establish that the algorithm is able to pick subsequences that are not considered similar by character based matching algorithms but share structural similarities. Similarly many subsequences with low character identity were picked between xyna-theau and xyna-clotm F/10 xylanases. Comparison of 3D structures of the subsequences confirmed the claim of Similarity in structure. Conclusion An algorithm is developed which is inspired by successful application of spectral Similarity applied to music sequences. The method captures subsequences that do not align by traditional character based alignment tools but give rise to similar secondary and tertiary structures. The Spectral Similarity Score (SSS) is an extension to the conventional Similarity methods and results indicate that it holds a strong potential for analysis of various biological sequences and structural variations in proteins.
