Evolutionarily conserved amino acids within proteins characterize functional or structural region... more Evolutionarily conserved amino acids within proteins characterize functional or structural regions. Conversely, less conserved amino acids within these regions are generally areas of evolutionary divergence. A priori knowledge of biological function and species can help interpret the amino acid differences between sequences. However, this information is often erroneous or unavailable, hampering discovery with supervised algorithms. Also, most of the current unsupervised methods depend on full sequence similarity, which become inaccurate when proteins diverge (e.g. inversions, deletions, insertions). Due to these and other shortcomings, we developed a novel unsupervised algorithm which discovers highly conserved regions and uses two types of information measures: 1) data measures computed from input sequences; and 2) class measures computed using a priori class groupings in order to reveal subgroups (i.e. classes) or functional characteristics. Using known and putative sequences of t...
IEEE International Conference on Bioinformatics and Biomedicine Workshops, 2011
An essential step of protein function analysis is to discover patterns that represent functional ... more An essential step of protein function analysis is to discover patterns that represent functional regions in a set of protein family sequences. However, the same functional region of a protein family that occurs in different sequences may contain variations that resulted from biological substitutions, deletions, and insertions. Thus, a sequence pattern representing this functional region seldom repeats precisely at the
Background: Discovering sequence patterns with variation can unveil functions of a protein family... more Background: Discovering sequence patterns with variation can unveil functions of a protein family that are important for drug discovery. Exploring protein families using existing methods such as multiple sequence alignment is computationally expensive, thus pattern search, called motif finding in Bioinformatics, is used. However, at present, combinatorial algorithms result in large sets of solutions, and probabilistic models require a richer representation of the amino acid associations. To overcome these shortcomings, we present a method for ranking and compacting these solutions in a new representation referred to as Aligned Pattern Clusters (APCs). To tackle the problem of a large solution set, our method reveals a reduced set of candidate solutions without losing any information. To address the problem of representation, our method captures the amino acid associations and conservations of the aligned patterns. Our algorithm renders a set of APCs in which a set of patterns is discovered, pruned, aligned, and synthesized from the input sequences of a protein family. Results: Our algorithm identifies the binding or other functional segments and their embedded residues which are important drug targets from the cytochrome c and the ubiquitin protein families taken from Unitprot. The results are independently confirmed by pFam's multiple sequence alignment. For cytochrome c protein the number of resulting patterns with variations are reduced by 76.62% from the number of original patterns without variations. Furthermore, all of the top four candidate APCs correspond to the binding segments with one of each of their conserved amino acid as the binding residue. The discovered proximal APCs agree with pFam and PROSITE results. Surprisingly, the distal binding site discovered by our algorithm is not discovered by pFam nor PROSITE, but confirmed by the three-dimensional cytochrome c structure. When applied to the ubiquitin protein family, our results agree with pFam and reveals six of the seven Lysine binding residues as conserved aligned columns with entropy redundancy measure of 1.0. Conclusion: The discovery, ranking, reduction, and representation of a set of patterns is important to avert timeconsuming and expensive simulations and experimentations during proteomic study and drug discovery.
A basic task in protein analysis is to discover a set of sequence patterns that characterizes the... more A basic task in protein analysis is to discover a set of sequence patterns that characterizes the function of a protein family. To address this task, we introduce a synthesized pattern representation called Aligned Pattern (AP) Cluster to discover potential functional segments in protein sequences. We apply our algorithm to identify and display the binding segments for the Cytochrome C. and Ubiquitin protein families. The resulting AP Clusters correspond to protein binding segments that surround the binding residues. When compared to the results from the protein annotation databases, PROSITE and pFam, ours are more efficient in computation and comprehensive in quality. The significance of the AP Cluster is that it is able to capture subtle variations of the binding segments in protein families. It thus could help to reduce time-consuming simulations and experimentation in the protein analysis.
2016 International Joint Conference on Neural Networks (IJCNN), 2016
Content-Based Image Retrieval (CBIR) system which identifies similar medical images based on a qu... more Content-Based Image Retrieval (CBIR) system which identifies similar medical images based on a query image can assist clinicians for more accurate diagnosis. The recent CBIR research trend favors the construction and use of binary codes to represent images. Deep architectures could learn the non-linear relationship among image pixels adaptively, allowing the automatic learning of high-level features from raw pixels. However, most of them require class labels, which are expensive to obtain, particularly for medical images. The methods which do not need class labels utilize a deep autoencoder for binary hashing, but the code construction involves a specific training algorithm and an ad-hoc regularization technique. In this study, we explored using a deep de-noising autoencoder (DDA), with a new unsupervised training scheme using only backpropagation and dropout, to hash images into binary codes. We conducted experiments on more than 14,000 x-ray images. By using class labels only for evaluating the retrieval results, we constructed a 16-bit DDA and a 512-bit DDA independently. Comparing to other unsupervised methods, we succeeded to obtain the lowest total error by using the 512-bit codes for retrieval via exhaustive search, and speed up 9.27 times with the use of the 16-bit codes while keeping a comparable total error. We found that our new training scheme could reduce the total retrieval error significantly by 21.9%. To further boost the image retrieval performance, we developed Radon Autoencoder Barcode (RABC) which are learned from the Radon projections of images using a de-noising autoencoder. Experimental results demonstrated its superior performance in retrieval when it was combined with DDA binary codes.
Computer Vision, Graphics, and Image Processing, 1988
In digital image processing, thresholding is a well-known technique for image segmentation. Becau... more In digital image processing, thresholding is a well-known technique for image segmentation. Because of its wide applicability to other areas of the digital image processing, quite a number of thresholding methods have been proposed over the years. In this paper, we present a survey of thresholding techniques and update the earlier survey work by Weszka (Comput. Vision Graphics 62 Image Process 7, 1978, 259-265) and Fu and Mu (Pattern Recognit. 13, 1981, 3-16). We attempt to evaluate the performance of some automatic global thresholding methods using the criterion functions such as uniformity and shape measures. The evaluation is based on some real world images. 0 1988 Academic press, IX.
Machine Learning and Data Mining in Pattern Recognition, 2005
Several cost-sensitive boosting algorithms have been reported as effective methods in dealing wit... more Several cost-sensitive boosting algorithms have been reported as effective methods in dealing with class imbalance problem. Misclassification costs, which reflect the different level of class identification importance, are integrated into the weight update formula of AdaBoost algorithm. Yet, it has been shown that the weight update parameter of AdaBoost is induced so as the training error can be reduced most rapidly. This is the most crucial step of AdaBoost in converting a weak learning algorithm into a strong one. However, most reported cost-sensitive boosting algorithms ignore such a property. In this paper, we come up with three versions of cost-sensitive AdaBoost algorithms where the parameters for sample weight updating are induced. Then, their identification abilities on the small classes are tested on four "real world" medical data sets taken from UCI Machine Learning Database based on F-measure. Our experimental results show that one of our proposed cost-sensitive AdaBoost algorithms is superior in achieving the best identification ability on the small class among all reported cost-sensitive boosting algorithms.
IEEE Transactions on Pattern Analysis and Machine Intelligence, 1987
This paper presents a method of structuring the free space of a roving robot's environment into a... more This paper presents a method of structuring the free space of a roving robot's environment into a set of overlapping convex regions ideally suited to path planning and navigation tasks. The structure of the free space environment is maintained as a hypergraph with each convex region represented by a hyperedge identifying the boundary walls of the region. A new methodology reveals the structure of free space and constructs the hypergraph representation through a directed search for a set of fundamental circuits in an abstract graphical representation of the environment geometry.
EURASIP Journal on Bioinformatics and Systems Biology, 2012
Background: Much progress has been made in understanding the 3D structure of proteins using metho... more Background: Much progress has been made in understanding the 3D structure of proteins using methods such as NMR and X-ray crystallography. The resulting 3D structures are extremely informative, but do not always reveal which sites and residues within the structure are of special importance. Recently, there are indications that multipleresidue, sub-domain structural relationships within the larger 3D consensus structure of a protein can be inferred from the analysis of the multiple sequence alignment data of a protein family. These intra-dependent clusters of associated sites are used to indicate hierarchical inter-residue relationships within the 3D structure. To reveal the patterns of associations among individual amino acids or sub-domain components within the structure, we apply a k-modes attribute (aligned site) clustering algorithm to the ubiquitin and transthyretin families in order to discover associations among groups of sites within the multiple sequence alignment. We then observe what these associations imply within the 3D structure of these two protein families. Results: The k-modes site clustering algorithm we developed maximizes the intra-group interdependencies based on a normalized mutual information measure. The clusters formed correspond to sub-structural components or binding and interface locations. Applying this data-directed method to the ubiquitin and transthyretin protein family multiple sequence alignments as a test bed, we located numerous interesting associations of interdependent sites. These clusters were then arranged into cluster tree diagrams which revealed four structural sub-domains within the single domain structure of ubiquitin and a single large sub-domain within transthyretin associated with the interface among transthyretin monomers. In addition, several clusters of mutually interdependent sites were discovered for each protein family, each of which appear to play an important role in the molecular structure and/or function. Conclusions: Our results demonstrate that the method we present here using a k-modes site clustering algorithm based on interdependency evaluation among sites obtained from a sequence alignment of homologous proteins can provide significant insights into the complex, hierarchical inter-residue structural relationships within the 3D structure of a protein family.
Background Statistical data analysis, especially the advanced machine learning (ML) methods, have... more Background Statistical data analysis, especially the advanced machine learning (ML) methods, have attracted considerable interest and application in clinical practices. First, the interpretability of the diagnostic/prognostic results will bring confidence to doctors, patients and their relatives in therapeutics and clinical practice. Furthermore, from the clinical aspect, when the datasets are imbalanced in diagnostic categories, the ordinary ML methods might produce results overwhelmed by the majority classes diminishing prediction accuracy. Hence, it is desirable to have a method that could produce explicit transparent and interpretable results in decision-making, even for data with imbalanced groups.Methods In order to interpret the clinical patterns and conduct diagnostic prediction of patients, we present our new method, Pattern Discovery and Disentanglement for Clinical Data Analysis (cPDD), which is able to discover patterns (correlated traits/indicants) and use them to class...
2012 IEEE International Conference on Bioinformatics and Biomedicine Workshops, 2012
A basic task in protein analysis is to discover a set of sequence patterns that reflect the funct... more A basic task in protein analysis is to discover a set of sequence patterns that reflect the function of a protein family. This set of sequence patterns contains non-exact significant residue associations. Currently, the existing combinatorial methods are computationally expensive and probabilistic methods require richer representation of the amino acid associations. To undertake this task, we create a synthesized pattern representation called an Aligned Pattern (AP) Cluster that identifies the residue associations in the binding segment and the site variations in the aligned residues. In this paper, our algorithm identifies the binding segments for two protein families: the Cytochrome Complex and the Ubiquitin protein families. For each of the experiments, the AP Clusters obtained correspond to protein binding segments including a few beyond those identified by the other protein databases, PROSITE and pFam. Furthermore, the columns of aligned sites that exist only as a single value in the AP Clusters also correspond to the binding residues. Additional information retained by the AP Clusters can reveal the amino acid residues of interest, thus averting time-consuming simulations and experimentation.
Residue-residue close contact (R2R-C) data procured from three-dimensional protein-protein intera... more Residue-residue close contact (R2R-C) data procured from three-dimensional protein-protein interaction (PPI) experiments is currently used for predicting residue-residue interaction (R2R-I) in PPI. However, due to complex physiochemical environments, R2R-I incidences, facilitated by multiple factors, are usually entangled in the source environment and masked in the acquired data. Here we present a novel method, P2K (Pattern to Knowledge), to disentangle R2R-I patterns and render much succinct discriminative information expressed in different specific R2R-I statistical/functional spaces. Since such knowledge is not visible in the data acquired, we refer to it as deep knowledge. Leveraging the deep knowledge discovered to construct machine learning models for sequence-based R2R-I prediction, without trial-and-error combination of the features over external knowledge of sequences, our R2R-I predictor was validated for its effectiveness under stringent leave-one-complex-out-alone cross-...
Machine Learning has made impressive advances in many applications akin to human cognition for di... more Machine Learning has made impressive advances in many applications akin to human cognition for discernment. However, success has been limited in the areas of relational datasets, particularly for data with low volume, imbalanced groups, and mislabeled cases, with outputs that typically lack transparency and interpretability. The difficulties arise from the subtle overlapping and entanglement of functional and statistical relations at the source level. Hence, we have developed Pattern Discovery and Disentanglement System (PDD), which is able to discover explicit patterns from the data with various sizes, imbalanced groups, and screen out anomalies. We present herein four case studies on biomedical datasets to substantiate the efficacy of PDD. It improves prediction accuracy and facilitates transparent interpretation of discovered knowledge in an explicit representation framework PDD Knowledge Base that links the sources, the patterns, and individual patients. Hence, PDD promises broa...
A protein family has similar and diverse functions locally conserved as aligned sequence segments... more A protein family has similar and diverse functions locally conserved as aligned sequence segments. Further discovering their association patterns could reveal subtle family subgroup characteristics. Since(ARAs) in Aligned Pattern Clusters (APCs) are complex and intertwined due to entangled function, factors, and variance in the source environment, we have recently developed a novel method: Aligned Residue Association Discovery and Disentanglement (ARADD) to solve this problem. ARADD first obtains from an APC an ARA Frequency Matrix and converts it to an adjusted(SRV). It then disentangles the SRV into Principal Components (PCs) and Re-projects their vectors to a SRV to reveal succinct orthogonal AR groups. In this study, we applied ARADD to class A scavenger receptors (SR-A), a subclass of a diverse protein family binding to modified lipoproteins with diverse biological functionalities not explicitly known. Our experimental results demonstrated that ARADD can unveil subtle subgroups...
Background: A protein family has similar and diverse functions locally conserved. An aligned patt... more Background: A protein family has similar and diverse functions locally conserved. An aligned pattern cluster (APC) can reflect the conserved functionality. Discovering aligned residue associations (ARAs) in APCs can reveal subtle inner working characteristics of conserved regions of protein families. However, ARAs corresponding to different functionalities/subgroups/classes could be entangled because of subtle multiple entwined factors. Methods: To discover and disentangle patterns from mixed-mode datasets, such as APCs when the residues are replaced by their fundamental biochemical properties list, this paper presents a novel method, Extended Aligned Residual Association Discovery and Disentanglement (E-ARADD). E-ARADD discretizes the numerical dataset to transform the mixed-mode dataset into an event-value dataset, constructs an ARA Frequency Matrix and then converts it into an adjusted Statistical Residual (SR) Vector Space (SRV) capturing statistical deviation from randomness. By applying Principal Component (PC) Decomposition on SRV, PCs ranked by their variance are obtained. Finally, the disentangled ARAs are discovered when the projections on a PC is re-projected to a vector space with the same basis vectors of SRV. Results: Experiments on synthetic, cytochrome c and class A scavenger data have shown that E-ARADD can a) disentangle the entwined ARAs in APCs (with residues or biochemical properties), b) reveal subtle AR clusters relating to classes, subtle subgroups or specific functionalities. Conclusions: E-ARADD can discover and disentangle ARs and ARAs entangled in functionality and location of protein families to reveal functional subgroups and subgroup characteristics of biological conserved regions. Experimental results on synthetic data provides the proof-of-concept validation on the successful disentanglement that reveals class-associated ARAs with or without class labels as input. Experiments on cytochrome c data proved the efficacy of E-ARADD in handing both types of residue data. Our novel methodology is not only able to discover and disentangle ARs and ARAs in specific statistical/functional (PCs and RSRVs) spaces, but also their locations in the protein family functional domains. The success of E-ARADD shows its great potential to proteomic research, drug discovery and precision and personalized genetic medicine.
Discovering patterns from sequence data has significant impact in genomics, proteomics and busine... more Discovering patterns from sequence data has significant impact in genomics, proteomics and business. A problem commonly encountered is that the patterns discovered often contain many redundancies resulted from fake significant patterns induced by their strong ...
Evolutionarily conserved amino acids within proteins characterize functional or structural region... more Evolutionarily conserved amino acids within proteins characterize functional or structural regions. Conversely, less conserved amino acids within these regions are generally areas of evolutionary divergence. A priori knowledge of biological function and species can help interpret the amino acid differences between sequences. However, this information is often erroneous or unavailable, hampering discovery with supervised algorithms. Also, most of the current unsupervised methods depend on full sequence similarity, which become inaccurate when proteins diverge (e.g. inversions, deletions, insertions). Due to these and other shortcomings, we developed a novel unsupervised algorithm which discovers highly conserved regions and uses two types of information measures: 1) data measures computed from input sequences; and 2) class measures computed using a priori class groupings in order to reveal subgroups (i.e. classes) or functional characteristics. Using known and putative sequences of t...
IEEE International Conference on Bioinformatics and Biomedicine Workshops, 2011
An essential step of protein function analysis is to discover patterns that represent functional ... more An essential step of protein function analysis is to discover patterns that represent functional regions in a set of protein family sequences. However, the same functional region of a protein family that occurs in different sequences may contain variations that resulted from biological substitutions, deletions, and insertions. Thus, a sequence pattern representing this functional region seldom repeats precisely at the
Background: Discovering sequence patterns with variation can unveil functions of a protein family... more Background: Discovering sequence patterns with variation can unveil functions of a protein family that are important for drug discovery. Exploring protein families using existing methods such as multiple sequence alignment is computationally expensive, thus pattern search, called motif finding in Bioinformatics, is used. However, at present, combinatorial algorithms result in large sets of solutions, and probabilistic models require a richer representation of the amino acid associations. To overcome these shortcomings, we present a method for ranking and compacting these solutions in a new representation referred to as Aligned Pattern Clusters (APCs). To tackle the problem of a large solution set, our method reveals a reduced set of candidate solutions without losing any information. To address the problem of representation, our method captures the amino acid associations and conservations of the aligned patterns. Our algorithm renders a set of APCs in which a set of patterns is discovered, pruned, aligned, and synthesized from the input sequences of a protein family. Results: Our algorithm identifies the binding or other functional segments and their embedded residues which are important drug targets from the cytochrome c and the ubiquitin protein families taken from Unitprot. The results are independently confirmed by pFam's multiple sequence alignment. For cytochrome c protein the number of resulting patterns with variations are reduced by 76.62% from the number of original patterns without variations. Furthermore, all of the top four candidate APCs correspond to the binding segments with one of each of their conserved amino acid as the binding residue. The discovered proximal APCs agree with pFam and PROSITE results. Surprisingly, the distal binding site discovered by our algorithm is not discovered by pFam nor PROSITE, but confirmed by the three-dimensional cytochrome c structure. When applied to the ubiquitin protein family, our results agree with pFam and reveals six of the seven Lysine binding residues as conserved aligned columns with entropy redundancy measure of 1.0. Conclusion: The discovery, ranking, reduction, and representation of a set of patterns is important to avert timeconsuming and expensive simulations and experimentations during proteomic study and drug discovery.
A basic task in protein analysis is to discover a set of sequence patterns that characterizes the... more A basic task in protein analysis is to discover a set of sequence patterns that characterizes the function of a protein family. To address this task, we introduce a synthesized pattern representation called Aligned Pattern (AP) Cluster to discover potential functional segments in protein sequences. We apply our algorithm to identify and display the binding segments for the Cytochrome C. and Ubiquitin protein families. The resulting AP Clusters correspond to protein binding segments that surround the binding residues. When compared to the results from the protein annotation databases, PROSITE and pFam, ours are more efficient in computation and comprehensive in quality. The significance of the AP Cluster is that it is able to capture subtle variations of the binding segments in protein families. It thus could help to reduce time-consuming simulations and experimentation in the protein analysis.
2016 International Joint Conference on Neural Networks (IJCNN), 2016
Content-Based Image Retrieval (CBIR) system which identifies similar medical images based on a qu... more Content-Based Image Retrieval (CBIR) system which identifies similar medical images based on a query image can assist clinicians for more accurate diagnosis. The recent CBIR research trend favors the construction and use of binary codes to represent images. Deep architectures could learn the non-linear relationship among image pixels adaptively, allowing the automatic learning of high-level features from raw pixels. However, most of them require class labels, which are expensive to obtain, particularly for medical images. The methods which do not need class labels utilize a deep autoencoder for binary hashing, but the code construction involves a specific training algorithm and an ad-hoc regularization technique. In this study, we explored using a deep de-noising autoencoder (DDA), with a new unsupervised training scheme using only backpropagation and dropout, to hash images into binary codes. We conducted experiments on more than 14,000 x-ray images. By using class labels only for evaluating the retrieval results, we constructed a 16-bit DDA and a 512-bit DDA independently. Comparing to other unsupervised methods, we succeeded to obtain the lowest total error by using the 512-bit codes for retrieval via exhaustive search, and speed up 9.27 times with the use of the 16-bit codes while keeping a comparable total error. We found that our new training scheme could reduce the total retrieval error significantly by 21.9%. To further boost the image retrieval performance, we developed Radon Autoencoder Barcode (RABC) which are learned from the Radon projections of images using a de-noising autoencoder. Experimental results demonstrated its superior performance in retrieval when it was combined with DDA binary codes.
Computer Vision, Graphics, and Image Processing, 1988
In digital image processing, thresholding is a well-known technique for image segmentation. Becau... more In digital image processing, thresholding is a well-known technique for image segmentation. Because of its wide applicability to other areas of the digital image processing, quite a number of thresholding methods have been proposed over the years. In this paper, we present a survey of thresholding techniques and update the earlier survey work by Weszka (Comput. Vision Graphics 62 Image Process 7, 1978, 259-265) and Fu and Mu (Pattern Recognit. 13, 1981, 3-16). We attempt to evaluate the performance of some automatic global thresholding methods using the criterion functions such as uniformity and shape measures. The evaluation is based on some real world images. 0 1988 Academic press, IX.
Machine Learning and Data Mining in Pattern Recognition, 2005
Several cost-sensitive boosting algorithms have been reported as effective methods in dealing wit... more Several cost-sensitive boosting algorithms have been reported as effective methods in dealing with class imbalance problem. Misclassification costs, which reflect the different level of class identification importance, are integrated into the weight update formula of AdaBoost algorithm. Yet, it has been shown that the weight update parameter of AdaBoost is induced so as the training error can be reduced most rapidly. This is the most crucial step of AdaBoost in converting a weak learning algorithm into a strong one. However, most reported cost-sensitive boosting algorithms ignore such a property. In this paper, we come up with three versions of cost-sensitive AdaBoost algorithms where the parameters for sample weight updating are induced. Then, their identification abilities on the small classes are tested on four "real world" medical data sets taken from UCI Machine Learning Database based on F-measure. Our experimental results show that one of our proposed cost-sensitive AdaBoost algorithms is superior in achieving the best identification ability on the small class among all reported cost-sensitive boosting algorithms.
IEEE Transactions on Pattern Analysis and Machine Intelligence, 1987
This paper presents a method of structuring the free space of a roving robot's environment into a... more This paper presents a method of structuring the free space of a roving robot's environment into a set of overlapping convex regions ideally suited to path planning and navigation tasks. The structure of the free space environment is maintained as a hypergraph with each convex region represented by a hyperedge identifying the boundary walls of the region. A new methodology reveals the structure of free space and constructs the hypergraph representation through a directed search for a set of fundamental circuits in an abstract graphical representation of the environment geometry.
EURASIP Journal on Bioinformatics and Systems Biology, 2012
Background: Much progress has been made in understanding the 3D structure of proteins using metho... more Background: Much progress has been made in understanding the 3D structure of proteins using methods such as NMR and X-ray crystallography. The resulting 3D structures are extremely informative, but do not always reveal which sites and residues within the structure are of special importance. Recently, there are indications that multipleresidue, sub-domain structural relationships within the larger 3D consensus structure of a protein can be inferred from the analysis of the multiple sequence alignment data of a protein family. These intra-dependent clusters of associated sites are used to indicate hierarchical inter-residue relationships within the 3D structure. To reveal the patterns of associations among individual amino acids or sub-domain components within the structure, we apply a k-modes attribute (aligned site) clustering algorithm to the ubiquitin and transthyretin families in order to discover associations among groups of sites within the multiple sequence alignment. We then observe what these associations imply within the 3D structure of these two protein families. Results: The k-modes site clustering algorithm we developed maximizes the intra-group interdependencies based on a normalized mutual information measure. The clusters formed correspond to sub-structural components or binding and interface locations. Applying this data-directed method to the ubiquitin and transthyretin protein family multiple sequence alignments as a test bed, we located numerous interesting associations of interdependent sites. These clusters were then arranged into cluster tree diagrams which revealed four structural sub-domains within the single domain structure of ubiquitin and a single large sub-domain within transthyretin associated with the interface among transthyretin monomers. In addition, several clusters of mutually interdependent sites were discovered for each protein family, each of which appear to play an important role in the molecular structure and/or function. Conclusions: Our results demonstrate that the method we present here using a k-modes site clustering algorithm based on interdependency evaluation among sites obtained from a sequence alignment of homologous proteins can provide significant insights into the complex, hierarchical inter-residue structural relationships within the 3D structure of a protein family.
Background Statistical data analysis, especially the advanced machine learning (ML) methods, have... more Background Statistical data analysis, especially the advanced machine learning (ML) methods, have attracted considerable interest and application in clinical practices. First, the interpretability of the diagnostic/prognostic results will bring confidence to doctors, patients and their relatives in therapeutics and clinical practice. Furthermore, from the clinical aspect, when the datasets are imbalanced in diagnostic categories, the ordinary ML methods might produce results overwhelmed by the majority classes diminishing prediction accuracy. Hence, it is desirable to have a method that could produce explicit transparent and interpretable results in decision-making, even for data with imbalanced groups.Methods In order to interpret the clinical patterns and conduct diagnostic prediction of patients, we present our new method, Pattern Discovery and Disentanglement for Clinical Data Analysis (cPDD), which is able to discover patterns (correlated traits/indicants) and use them to class...
2012 IEEE International Conference on Bioinformatics and Biomedicine Workshops, 2012
A basic task in protein analysis is to discover a set of sequence patterns that reflect the funct... more A basic task in protein analysis is to discover a set of sequence patterns that reflect the function of a protein family. This set of sequence patterns contains non-exact significant residue associations. Currently, the existing combinatorial methods are computationally expensive and probabilistic methods require richer representation of the amino acid associations. To undertake this task, we create a synthesized pattern representation called an Aligned Pattern (AP) Cluster that identifies the residue associations in the binding segment and the site variations in the aligned residues. In this paper, our algorithm identifies the binding segments for two protein families: the Cytochrome Complex and the Ubiquitin protein families. For each of the experiments, the AP Clusters obtained correspond to protein binding segments including a few beyond those identified by the other protein databases, PROSITE and pFam. Furthermore, the columns of aligned sites that exist only as a single value in the AP Clusters also correspond to the binding residues. Additional information retained by the AP Clusters can reveal the amino acid residues of interest, thus averting time-consuming simulations and experimentation.
Residue-residue close contact (R2R-C) data procured from three-dimensional protein-protein intera... more Residue-residue close contact (R2R-C) data procured from three-dimensional protein-protein interaction (PPI) experiments is currently used for predicting residue-residue interaction (R2R-I) in PPI. However, due to complex physiochemical environments, R2R-I incidences, facilitated by multiple factors, are usually entangled in the source environment and masked in the acquired data. Here we present a novel method, P2K (Pattern to Knowledge), to disentangle R2R-I patterns and render much succinct discriminative information expressed in different specific R2R-I statistical/functional spaces. Since such knowledge is not visible in the data acquired, we refer to it as deep knowledge. Leveraging the deep knowledge discovered to construct machine learning models for sequence-based R2R-I prediction, without trial-and-error combination of the features over external knowledge of sequences, our R2R-I predictor was validated for its effectiveness under stringent leave-one-complex-out-alone cross-...
Machine Learning has made impressive advances in many applications akin to human cognition for di... more Machine Learning has made impressive advances in many applications akin to human cognition for discernment. However, success has been limited in the areas of relational datasets, particularly for data with low volume, imbalanced groups, and mislabeled cases, with outputs that typically lack transparency and interpretability. The difficulties arise from the subtle overlapping and entanglement of functional and statistical relations at the source level. Hence, we have developed Pattern Discovery and Disentanglement System (PDD), which is able to discover explicit patterns from the data with various sizes, imbalanced groups, and screen out anomalies. We present herein four case studies on biomedical datasets to substantiate the efficacy of PDD. It improves prediction accuracy and facilitates transparent interpretation of discovered knowledge in an explicit representation framework PDD Knowledge Base that links the sources, the patterns, and individual patients. Hence, PDD promises broa...
A protein family has similar and diverse functions locally conserved as aligned sequence segments... more A protein family has similar and diverse functions locally conserved as aligned sequence segments. Further discovering their association patterns could reveal subtle family subgroup characteristics. Since(ARAs) in Aligned Pattern Clusters (APCs) are complex and intertwined due to entangled function, factors, and variance in the source environment, we have recently developed a novel method: Aligned Residue Association Discovery and Disentanglement (ARADD) to solve this problem. ARADD first obtains from an APC an ARA Frequency Matrix and converts it to an adjusted(SRV). It then disentangles the SRV into Principal Components (PCs) and Re-projects their vectors to a SRV to reveal succinct orthogonal AR groups. In this study, we applied ARADD to class A scavenger receptors (SR-A), a subclass of a diverse protein family binding to modified lipoproteins with diverse biological functionalities not explicitly known. Our experimental results demonstrated that ARADD can unveil subtle subgroups...
Background: A protein family has similar and diverse functions locally conserved. An aligned patt... more Background: A protein family has similar and diverse functions locally conserved. An aligned pattern cluster (APC) can reflect the conserved functionality. Discovering aligned residue associations (ARAs) in APCs can reveal subtle inner working characteristics of conserved regions of protein families. However, ARAs corresponding to different functionalities/subgroups/classes could be entangled because of subtle multiple entwined factors. Methods: To discover and disentangle patterns from mixed-mode datasets, such as APCs when the residues are replaced by their fundamental biochemical properties list, this paper presents a novel method, Extended Aligned Residual Association Discovery and Disentanglement (E-ARADD). E-ARADD discretizes the numerical dataset to transform the mixed-mode dataset into an event-value dataset, constructs an ARA Frequency Matrix and then converts it into an adjusted Statistical Residual (SR) Vector Space (SRV) capturing statistical deviation from randomness. By applying Principal Component (PC) Decomposition on SRV, PCs ranked by their variance are obtained. Finally, the disentangled ARAs are discovered when the projections on a PC is re-projected to a vector space with the same basis vectors of SRV. Results: Experiments on synthetic, cytochrome c and class A scavenger data have shown that E-ARADD can a) disentangle the entwined ARAs in APCs (with residues or biochemical properties), b) reveal subtle AR clusters relating to classes, subtle subgroups or specific functionalities. Conclusions: E-ARADD can discover and disentangle ARs and ARAs entangled in functionality and location of protein families to reveal functional subgroups and subgroup characteristics of biological conserved regions. Experimental results on synthetic data provides the proof-of-concept validation on the successful disentanglement that reveals class-associated ARAs with or without class labels as input. Experiments on cytochrome c data proved the efficacy of E-ARADD in handing both types of residue data. Our novel methodology is not only able to discover and disentangle ARs and ARAs in specific statistical/functional (PCs and RSRVs) spaces, but also their locations in the protein family functional domains. The success of E-ARADD shows its great potential to proteomic research, drug discovery and precision and personalized genetic medicine.
Discovering patterns from sequence data has significant impact in genomics, proteomics and busine... more Discovering patterns from sequence data has significant impact in genomics, proteomics and business. A problem commonly encountered is that the patterns discovered often contain many redundancies resulted from fake significant patterns induced by their strong ...
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