Alhadi Bustamam

Clustering protein-protein interaction network of TP53 tumor suppressor protein using Markov clustering algorithm

The formation and proliferation of tumor cells occurs if a special protein that regulates cell division experience any changing on their function, gene expression or both of them. One of the tumor suppressor proteins that plays a significant role in controlling the cell cycle is the TP53 protein. In most of the genetic changes in the tumor, it found that mutant of TP53 is a high risk factor for cancer. Therefore, it is important to conduct studies on clustering protein-protein interactions (PPI) network of TP53. PPI networks are generally presented in the graph network with proteins as vertices and interactions as edges. Markov clustering (MCL) algorithm is a graph clustering method which based on a simulation of stochastic flow on a graph. In implementation, we applied MCL process using the Python programming language. The clustering datasets are the PPI of TP53 obtained from the STRING database. MCL algorithm consists of three main operations such as expansion, inflation, and prune. We conduct the clustering simulation using different parameter of expansion, inflation and the multiplier factor of identity matrix. As the results we found the MCL algorithm is proven to produce robust cluster with TP53 protein as a centroid for each clustering results.

Application of hierarchical clustering ordered partitioning and collapsing hybrid in Ebola Virus phylogenetic analysis

Gene clustering can be achieved through hierarchical or partition method. Both clustering methods can be combined by processing the partition and hierarchical phases alternately. This method is known as a hierarchical clustering ordered partitioning and collapsing hybrid (HOPACH) method. The Partitioning phase can be done by using PAM, SOM, or K-Means methods. The partition process is continued with the ordered process, and then it is corrected with agglomerative process, in order to have more accurate clustering results. Furthermore, the main clusters are determined by using MSS (Median Split Silhouette) value. We selected the clustering results which minimize the MSS value. In this work, we conduct the clustering on 136 Ebola Virus DNA sequences data from GenBank. The global alignment process is initially performed, followed by genetic distance calculation using Jukes-Cantor correction. In our implementation, we applied global alignment process and used the combination of HOPACH-PAM clustering using the R open source programming tool. In our results, we obtained maximum genetic distance is 0.6153407; meanwhile the minimum genetic distance is 0. Furthermore, genetic distance matrix can be used as a basis for sequences clustering and phylogenetic analysis. In our HOPACH-PAM clustering results, we obtained 10 main clusters with MSS value is 0.8873843. Ebola virus clusters can be identified by species and virus epidemic year.

Implementation of CUDA GPU-based parallel computing on Smith-Waterman algorithm to sequence database searches

In bioinformatics, one of the goldstandard algorithms to compute the optimal similarity score between sequences in a sequence database searches is Smith-Waterman algorithm that uses dynamic programming. This algorithm has a quadratic time complexity which requires a long computation time for large-sized data. In this issue, parallel computing is essential for sequence database searches in order to reduce the running time and to increase the performance. In this paper, we discuss the parallel implementation of Smith-Waterman algorithm in GPU using CUDA C programming language with NVCC compiler on Linux environment. Furthermore, we run the performance analysis using three parallelization models, including Inter-task Parallelization, Intra-task Parallelization, and a combination of both models. Based on the simulation results, a combination of both models has better performance than the others. In addition the parallelization using combination of both models achieves an average speed-up of 313x and an average efficiency with a factor of 0.93.

Texture and Gene Expression Analysis of the MRI Brain in Detection of Alzheimer’s Disease

Abstract Alzheimer’s disease is a type of dementia that can cause problems with human memory, thinking and behavior. This disease causes cell death and nerve tissue damage in the brain. The brain damage can be detected using brain volume, whole brain form, and genetic testing. In this research, we propose texture analysis of the brain and genomic analysis to detect Alzheimer’s disease. 3D MRI images were chosen to analyze the texture of the brain, and microarray data were chosen to analyze gene expression. We classified Alzheimer’s disease into three types: Alzheimer’s, Mild Cognitive Impairment (MCI), and Normal. In this study, texture analysis was carried out by using the Advanced Local Binary Pattern (ALBP) and the Gray Level Co-occurrence Matrix (GLCM). We also propose the bi-clustering method to analyze microarray data. The experimental results from texture analysis show that ALBP had better performance than GLCM in classification of Alzheimer’s disease. The ALBP method achieved an average value of accuracy of between 75% - 100% for binary classification of the whole brain data. Furthermore, Biclustering method with microarray data shows good performance gene expression, where this information show influence Alzheimer’s disease with total of bi-cluster is 6.

Implementation of parallel k-means algorithm for two-phase method biclustering in Carcinoma tumor gene expression data

Tumor is an abnormal growth of cells that serves no purpose. Carcinoma is a tumor that grows from the top of the cell membrane. In the field of molecular biology, the development of microarray technology is used in data store of disease genetic expression. For each of microarray gene, an amount of information is stored for each trait or condition. In gene expression data clustering can be done with a bicluster algorithm, that’s clustering method not only the objects to be clustered, but also the properties or condition of the object. This research proposed a two-phase method for finding a bicluster. In the first phase, a parallel k-means algorithm is applied to the gene expression data. Then, in the second phase, Cheng and Church biclustering algorithm as one of biclustering method is performed to find biclusters. In this study, we discuss the implementation of two-phase method using biclustering of Cheng and Church and parallel k-means algorithm in Carcinoma tumor gene expression data. From the experimen...

Implementation of spectral clustering with partitioning around medoids (PAM) algorithm on microarray data of carcinoma

Technology of microarray became one of the imperative tools in life science to observe the gene expression levels, one of which is the expression of the genes of people with carcinoma. Carcinoma is a cancer that forms in the epithelial tissue. These data can be analyzed such as the identification expressions hereditary gene and also build classifications that can be used to improve diagnosis of carcinoma. Microarray data usually served in large dimension that most methods require large computing time to do the grouping. Therefore, this study uses spectral clustering method which allows to work with any object for reduces dimension. Spectral clustering method is a method based on spectral decomposition of the matrix which is represented in the form of a graph. After the data dimensions are reduced, then the data are partitioned. One of the famous partition method is Partitioning Around Medoids (PAM) which is minimize the objective function with exchanges all the non-medoid points into medoid point iterativ...

Implementation of hybrid clustering based on partitioning around medoids algorithm and divisive analysis on human Papillomavirus DNA

Data clustering can be executed through partition or hierarchical method for many types of data including DNA sequences. Both clustering methods can be combined by processing partition algorithm in the first level and hierarchical in the second level, called hybrid clustering. In the partition phase some popular methods such as PAM, K-means, or Fuzzy c-means methods could be applied. In this study we selected partitioning around medoids (PAM) in our partition stage. Furthermore, following the partition algorithm, in hierarchical stage we applied divisive analysis algorithm (DIANA) in order to have more specific clusters and sub clusters structures. The number of main clusters is determined using Davies Bouldin Index (DBI) value. We choose the optimal number of clusters if the results minimize the DBI value. In this work, we conduct the clustering on 1252 HPV DNA sequences data from GenBank. The characteristic extraction is initially performed, followed by normalizing and genetic distance calculation using...

Detection of Alzheimer's disease using advanced local binary pattern from hippocampus and whole brain of MR images

Alzheimers disease as one type of dementia can cause problems to human memory, thinking and behavior. The brain damage can be detected using brain volume and whole brain form. The correlation between brain shrinkage and reduction of brain volume can affect to deformation texture. In this research, the enhancement texture approach was proposed, called advanced local binary pattern (ALBP) method. ALBP is introduced as a 2D and 3D feature extraction descriptor. In the ALBP, sign and magnitude value were introduced as an enhancement to the previous LBP method. Due to a great number of features are produced by ALBP, the principal component analysis (PCA) and factor analysis are used as feature selection method. Furthermore, SVM classifier is applied for multiclass classification including Alzheimers, mild cognitive impairment, and normal condition of whole brain and hippocampus. The experimental results from two scenarios (ALBP sign magnitude (2D) and ALBP sign magnitude using three orthogonal planes (3D) methods) show better accuracy and performance compare to previous method. Our proposed method achieved the average value of accuracy between 80% - 100% for both the whole brain and hippocampus data. In addition, uniform rotation invariant ALBP sign magnitude using three orthogonal planes as a 3D descriptor also outperforms other approaches with an average accuracy of 96.28% for multiclass classifications for whole brain image.

Collaboration and implementation of self organizing maps (SOM) partitioning algorithm in HOPACH clustering method

Since the discovery of DNA structure in the form of the double helix, there is a development of the complex interaction required, DNA clustering into clusters which have the same features or functions. Two clustering methods can be combined by doing the partitioning and hierarchical stage alternately known as HOPACH clustering. The partitioning stage can be done by using SOM Algorithm, PAM, and K-Means. SOM algorithm because it uses unsupervised learning method and efficient to be used for extensive data. The partitioning process is continued by ordering process and then performed collapsing with an agglomerative process so that the clustering result obtained will be more accurate. The determination of the main cluster done by calculating the homogeneous value of the clustering result uses MSS (Mean Split Silhouette). The determination criteria of the main cluster are choosing the smallest MSS value. 136 sequences of DNA EVD (Ebola Virus Disease) are obtained from NCBI GenBank by extraction of DNA sequence, normalization, and then calculating the genetic distance with Euclidean Distance. The extraction of DNA sequence, normalization, and the implementation of SOM partitioning algorithm in HOPACH clustering method use open source program R. On the result of implementation SOM partitioning algorithm in HOPACH clustering method retrieved 9 clusters with MSS value of 0.50280. The cluster obtained can be identified according to species and the first year of becoming an epidemic.

Application of hybrid clustering using parallel k-means algorithm and DIANA algorithm

DNA is one of the carrier of genetic information of living organisms. Encoding, sequencing, and clustering DNA sequences has become the key jobs and routine in the world of molecular biology, in particular on bioinformatics application. There are two type of clustering, hierarchical clustering and partitioning clustering. In this paper, we combined two type clustering i.e. K-Means (partitioning clustering) and DIANA (hierarchical clustering), therefore it called Hybrid clustering. Application of hybrid clustering using Parallel K-Means algorithm and DIANA algorithm used to clustering DNA sequences of Human Papillomavirus (HPV). The clustering process is started with Collecting DNA sequences of HPV are obtained from NCBI (National Centre for Biotechnology Information), then performing characteristics extraction of DNA sequences. The characteristics extraction result is store in a matrix form, then normalize this matrix using Min-Max normalization and calculate genetic distance using Euclidian Distance. Fur...