Clustering of Protein Conformations Using Parallelized Dimensionality Reduction

Joshi, Arpita; Haspel, Nurit

doi:10.12720/jait.10.4.142-147

Cited by 9 publications

(3 citation statements)

References 16 publications

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“…Despite its advantage in efficient representation of molecular data [31,32], Isomap is computationally expensive, especially with very large, multi-dimensional datasets. To overcome this, we use a version of the Mode-III of Isomap [55]. Improvements over Isomap are presented in [33,34].…”

Section: Feature Reductionmentioning

confidence: 99%

“…The next step is to obtain an embedding of the conformations in the reduced dataset in a lower dimension. We used a parallelized version of the Isomap algorithm that produces the same results and works much faster [55]. The algorithm can be used to produce an embedding in as many dimensions as the attributes of the dataset, we pick the first three dimensions to work with because they capture over 80% of the variance inherent in the data.…”

Section: Low-dimensional Embeddingmentioning

confidence: 99%

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Characterizing Protein Conformational Spaces using Efficient Data Reduction and Algebraic Topology

Joshi

Haspel

2022

J. Hum. Earth Future

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Datasets representing the conformational landscapes of protein structures are high-dimensional and hence present computational challenges. Efficient and effective dimensionality reduction of these datasets is therefore paramount to our ability to analyze the conformational landscapes of proteins and extract important information regarding protein folding, conformational changes, and binding. Representing the structures with fewer attributes that capture the most variance in the data makes for a quicker and more precise analysis of these structures. In this study, we make use of dimensionality reduction methods for reducing the number of instances and for feature reduction. The reduced dataset that is obtained is then subjected to topological and quantitative analysis. In this step, we perform hierarchical clustering to obtain different sets of conformation clusters that may correspond to intermediate structures. The structures represented by these conformations are then analyzed by studying their high-dimensional topological properties to identify truly distinct conformations and holes in the conformational space that may represent high energy barriers. Our results show that the clusters closely follow known experimental results about intermediate structures as well as binding and folding events. Doi: 10.28991/HEF-SP2022-01-01 Full Text: PDF

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Section: Feature Reductionmentioning

confidence: 99%

Section: Low-dimensional Embeddingmentioning

confidence: 99%

Characterizing Protein Conformational Spaces using Efficient Data Reduction and Algebraic Topology

Joshi

Haspel

2022

J. Hum. Earth Future

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“…These attributes can be normalized and reduced using various feature reduction techniques like PCA itself. Studies have shown that not all of these conformations need to be worked with to understand macro-molecular dynamics [8][9][10][11][12]. Our algorithm reduces the number of these conformations in a way that reduces the vast conformation space of proteins.…”

Section: Introductionmentioning

confidence: 99%

A Novel Data Instance Reduction Technique using Linear Feature Reduction

Joshi¹,

Haspel²

2020

AIS

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Representation of structurally significant data is indispensable to modern research. The need for dimensionality reduction finds its foray in varied genres viz-a-viz, Structural Bioinformatics, Machine Learning, Robotics, Artificial Intelligence, to name a few. The number of points required to effectively capture the essence of a structure is an intuitive decision. Feature reduction methods like Principal Component Analysis (PCA) have already been explored and proven to be an aid in classification and regression. In this work we present a novel approach that first performs PCA on a data set for reduction of features and then attempts to reduce the number of points itself to get rid of the points that have nothing or very little new to offer. The algorithm was tested on various kinds of data (points representing a spiral, protein coordinates, the Iris dataset prevalent in Machine Learning, face image) and the results agree with the quantitative tests applied. In each case, it turns out that a lot of data instances need not be stored to make any kind of decision. Matlab and R simulations were used to assess the structures with reduced data points. The time complexity of the algorithm is linear in the degrees of freedom of the data if the data is in a natural order.

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LapEFCM: overlapping community detection using laplacian eigenmaps and fuzzy C-means clustering

Hasan

Kamal

2022

Int. j. inf. tecnol.

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Clustering of Protein Conformations Using Parallelized Dimensionality Reduction

Cited by 9 publications

References 16 publications

Characterizing Protein Conformational Spaces using Efficient Data Reduction and Algebraic Topology

Characterizing Protein Conformational Spaces using Efficient Data Reduction and Algebraic Topology

A Novel Data Instance Reduction Technique using Linear Feature Reduction

LapEFCM: overlapping community detection using laplacian eigenmaps and fuzzy C-means clustering

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