Biomedical subjects

Chien-Yu Chen

Publications and source records attributed to Chien-Yu Chen.

7 recordsLinked to original sources

PubMed2026

Graph-KIR: graph-based KIR copy number estimation and allele calling using short-read sequencing data.

MOTIVATION: The Killer-cell Immunoglobulin-like Receptor (KIR) is a highly polymorphic region in the human genome, associated with autoimmune diseases and organ transplantation. The sequences of KIR genes are highly similar among star alleles as well as in between individual genes, with the copy number of each KIR gene typically ranging from 0 to 4. In this study, we introduce Graph-KIR, a tool designed to estimate gene copy numbers and predict full-resolution (7-digit, encompassing both coding and non-coding sequence variations) from a whole genome sequencing (WGS) sample. RESULTS: Graph-KIR is capable of independently typing KIR alleles per sample with no reliance on the distribution of any framework gene in a cohort. In a set of 100 simulated samples, Graph-KIR demonstrated 99.2% accuracy in copy number estimation and high F1-score of allele typing: 91.79% at 7-digit resolution, 97.37% at 5-digit resolution, and 97.11% at 3-digit resolution. Graph-KIR outperforms existing tools such as Geny (96.39% F1-score), PING's WGS version (92.77% F1-score), and T1K (90.44% F1-score) at 5-digit resolution. By analyzing the results on 44 HPRC samples, Graph-KIR achieves better F1-score than Geny and PING at 7-digit resolution. The release of Graph-KIR adds another valuable tool to assist users in accurately estimating copy numbers and calling alleles of KIR genes from WGS samples. AVAILABILITY AND IMPLEMENTATION: The Graph-KIR and paper-related pipeline codes are available at https://github.com/linnil1/KIR_graph.

Receptors, KIR↗

PubMed2006

MAGIIC-PRO: detecting functional signatures by efficient discovery of long patterns in protein sequences.

This paper presents a web service named MAGIIC-PRO, which aims to discover functional signatures of a query protein by sequential pattern mining. Automatic discovery of patterns from unaligned biological sequences is an important problem in molecular biology. MAGIIC-PRO is different from several previously established methods performing similar tasks in two major ways. The first remarkable feature of MAGIIC-PRO is its efficiency in delivering long patterns. With incorporating a new type of gap constraints and some of the state-of-the-art data mining techniques, MAGIIC-PRO usually identifies satisfied patterns within an acceptable response time. The efficiency of MAGIIC-PRO enables the users to quickly discover functional signatures of which the residues are not from only one region of the protein sequences or are only conserved in few members of a protein family. The second remarkable feature of MAGIIC-PRO is its effort in refining the mining results. Considering large flexible gaps improves the completeness of the derived functional signatures. The users can be directly guided to the patterns with as many blocks as that are conserved simultaneously. In this paper, we show by experiments that MAGIIC-PRO is efficient and effective in identifying ligand-binding sites and hot regions in protein-protein interactions directly from sequences. The web service is available at http://biominer.bime.ntu.edu.tw/magiicpro and a mirror site at http://biominer.cse.yzu.edu.tw/magiicpro.

Binding Sites↗

PubMed2006

Protein disorder prediction by condensed PSSM considering propensity for order or disorder.

BACKGROUND: More and more disordered regions have been discovered in protein sequences, and many of them are found to be functionally significant. Previous studies reveal that disordered regions of a protein can be predicted by its primary structure, the amino acid sequence. One observation that has been widely accepted is that ordered regions usually have compositional bias toward hydrophobic amino acids, and disordered regions are toward charged amino acids. Recent studies further show that employing evolutionary information such as position specific scoring matrices (PSSMs) improves the prediction accuracy of protein disorder. As more and more machine learning techniques have been introduced to protein disorder detection, extracting more useful features with biological insights attracts more attention. RESULTS: This paper first studies the effect of a condensed position specific scoring matrix with respect to physicochemical properties (PSSMP) on the prediction accuracy, where the PSSMP is derived by merging several amino acid columns of a PSSM belonging to a certain property into a single column. Next, we decompose each conventional physicochemical property of amino acids into two disjoint groups which have a propensity for order and disorder respectively, and show by experiments that some of the new properties perform better than their parent properties in predicting protein disorder. In order to get an effective and compact feature set on this problem, we propose a hybrid feature selection method that inherits the efficiency of uni-variant analysis and the effectiveness of the stepwise feature selection that explores combinations of multiple features. The experimental results show that the selected feature set improves the performance of a classifier built with Radial Basis Function Networks (RBFN) in comparison with the feature set constructed with PSSMs or PSSMPs that adopt simply the conventional physicochemical properties. CONCLUSION: Distinguishing disordered regions from ordered regions in protein sequences facilitates the exploration of protein structures and functions. Results based on independent testing data reveal that the proposed predicting model DisPSSMP performs the best among several of the existing packages doing similar tasks, without either under-predicting or over-predicting the disordered regions. Furthermore, the selected properties are demonstrated to be useful in finding discriminating patterns for order/disorder classification.

Amino Acid Sequence↗

PubMed2005

Data classification with radial basis function networks based on a novel kernel density estimation algorithm.

This paper presents a novel learning algorithm for efficient construction of the radial basis function (RBF) networks that can deliver the same level of accuracy as the support vector machines (SVMs) in data classification applications. The proposed learning algorithm works by constructing one RBF subnetwork to approximate the probability density function of each class of objects in the training data set. With respect to algorithm design, the main distinction of the proposed learning algorithm is the novel kernel density estimation algorithm that features an average time complexity of O(n log n), where n is the number of samples in the training data set. One important advantage of the proposed learning algorithm, in comparison with the SVM, is that the proposed learning algorithm generally takes far less time to construct a data classifier with an optimized parameter setting. This feature is of significance for many contemporary applications, in particular, for those applications in which new objects are continuously added into an already large database. Another desirable feature of the proposed learning algorithm is that the RBF networks constructed are capable of carrying out data classification with more than two classes of objects in one single run. In other words, unlike with the SVM, there is no need to resort to mechanisms such as one-against-one or one-against-all for handling datasets with more than two classes of objects. The comparison with SVM is of particular interest, because it has been shown in a number of recent studies that SVM generally are able to deliver higher classification accuracy than the other existing data classification algorithms. As the proposed learning algorithm is instance-based, the data reduction issue is also addressed in this paper. One interesting observation in this regard is that, for all three data sets used in data reduction experiments, the number of training samples remaining after a naive data reduction mechanism is applied is quite close to the number of support vectors identified by the SVM software. This paper also compares the performance of the RBF networks constructed with the proposed learning algorithm and those constructed with a conventional cluster-based learning algorithm. The most interesting observation learned is that, with respect to data classification, the distributions of training samples near the boundaries between different classes of objects carry more crucial information than the distributions of samples in the inner parts of the clusters.

Algorithms↗

PubMed2004

ProteMiner-SSM: a web server for efficient analysis of similar protein tertiary substructures.

Analysis of protein-ligand interactions is a fundamental issue in drug design. As the detailed and accurate analysis of protein-ligand interactions involves calculation of binding free energy based on thermodynamics and even quantum mechanics, which is highly expensive in terms of computing time, conformational and structural analysis of proteins and ligands has been widely employed as a screening process in computer-aided drug design. In this paper, a web server called ProteMiner-SSM designed for efficient analysis of similar protein tertiary substructures is presented. In one experiment reported in this paper, the web server has been exploited to obtain some clues about a biochemical hypothesis. The main distinction in the software design of the web server is the filtering process incorporated to expedite the analysis. The filtering process extracts the residues located in the caves of the protein tertiary structure for analysis and operates with O(nlogn) time complexity, where n is the number of residues in the protein. In comparison, the alpha-hull algorithm, which is a widely used algorithm in computer graphics for identifying those instances that are on the contour of a three-dimensional object, features O(n2) time complexity. Experimental results show that the filtering process presented in this paper is able to speed up the analysis by a factor ranging from 3.15 to 9.37 times. The ProteMiner-SSM web server can be found at http://proteminer.csie.ntu.edu.tw/. There is a mirror site at http://p4.sbl.bc.sinica.edu.tw/proteminer/.

Binding Sites↗

PubMed2004

Incremental generation of summarized clustering hierarchy for protein family analysis.

MOTIVATION: Protein sequence clustering has been widely exploited to facilitate in-depth analysis of protein functions and families. For some applications of protein sequence clustering, it is highly desirable that a hierarchical structure, also referred to as dendrogram, which shows how proteins are clustered at various levels, is generated. However, as the sizes of contemporary protein databases continue to grow at rapid rates, it is of great interest to develop some summarization mechanisms so that the users can browse the dendrogram and/or search for the desired information more effectively. RESULTS: In this paper, the design of a novel incremental clustering algorithm aimed at generating summarized dendrograms for analysis of protein databases is described. The proposed incremental clustering algorithm employs a statistics-based model to summarize the distributions of the similarity scores among the proteins in the database and to control formation of clusters. Experimental results reveal that, due to the summarization mechanism incorporated, the proposed incremental clustering algorithm offers the users highly concise dendrograms for analysis of protein clusters with biological significance. Another distinction of the proposed algorithm is its incremental nature. As the sizes of the contemporary protein databases continue to grow at fast rates, due to the concern of efficiency, it is desirable that cluster analysis of a protein database can be carried out incrementally, when the protein database is updated. Experimental results with the Swiss-Prot protein database reveal that the time complexity for carrying out incremental clustering with k new proteins added into the database containing n proteins is O(n2betalogn), where beta congruent with 0.865, provided that k << n. AVAILABILITY: The Linux executable is available on the following supplementary page.

Algorithms↗

PubMed2004

Differentiation of ketamine effects on renal nerve activity and renal blood flow in rats.

BACKGROUND: A biphasic pattern in the effects of increasing dose of ketamine on mean arterial blood pressure (MBP) and renal sympathetic nerve activities (RSNA) was shown in previous study. We hypothesized that if renal vascular resistance (RVR) and renal blood flow (RBF) are mainly controlled by RSNA, they will show a similar biphasic pattern under increasing doses of ketamine. METHODS: 16 female Wistar rats anesthetized with urethane were studied for ketamine at 0.5, 1, 2 mg/kg, i.v. at 30 min intervals. Multifiber RSNA recording was studied in 8 rats and RBF (by electromagnetic flowmeter) and RVR were studied in the other 8 rats with intact renal nerves. RESULTS: Our results showed that although incremental doses of ketamine brought about a biphasic pattern in MBP and RSNA, the decrease in RBF and the increase in RVR went a dose-related way. CONCLUSIONS: We concluded that there was a differentiation of ketamine effects on renal nerve activity and renal blood flow in rats. The changes of RBF and RVR can not only be attributed to the effects of sympathetic nerve activities.

Animals↗