Perbandingan Metode Clustering Menggunakan Metode Single Linkage dan K - Means pada Pengelompokan Dokumen

Penyebaran berita saat ini semakin tersebar luas semenjak perkembangan dunia internet yang semakin pesat. Perkembangan dunia internet membuat berita yang tersebar semakin beragam dan berjumlah sangat besar. Pembaca berita akan kesulitan untuk memperoleh berita yang diinginkan jika berita tersebut tidak terkelompok dengan baik. Dan jika harus dikelompokkan secara manual membutuhkan waktu yang sangat lama. Oleh sebab itu, Clustering menjadi solusi untuk mengatasi masalah tersebut. Clustering akan mengelompokkan dokumen berita berdasarkan tingkat kemiripan dari dokumen tersebut. Metode Single Linkage merupakan metode pengelompokan hierarchical clustering. Metode Single Linkage mengelompokkan dokumen didasarkan pada jarak terdekat antar dokumen. Komputasi Single Linkage merupakan komputasi yang mahal dan kompleks. Sedangkan metode K-means merupakan metode pengelompokan partitioned clustering. Metode K-means mengelompokkan dokumen didasarkan pada jarak terdekat dengan centroid-nya. K-Means merupakan metode pengelompokan yang sederhana dan dapat digunakan dengan mudah. Tetapi pada jenis data tertentu, K-means tidak dapat memberikan segementasi data dengan baik, sehingga kelompok yang terbentuk tidak murni data yang sama. Metode pengujian yang digunakan untuk mengukur kualitas cluster adalah Silhouette Coefficient dan Purity. Berdasarkan hasil pengujian yang dilakukan, dapat disimpulkan, bahwa metode Single Linkage memiliki performansi yang lebih baik dibandingkan dengan metode K-means. Nilai silhouette coefficient Single Linkage selalu lebih unggul dibandingkan dengan K-Means. Pertambahan jumlah dokumen membuat nilai silhouette coefficient single linkage semakin kecil sedangkan K-means terkadang menghasilkan nilai yang negatif. Untuk nilai purity, Single Linkage selalu bernilai 1 sedangkan K-Means tidak pernah bernilai 1. Hasil pertambahan jumlah cluster dan jumlah dokumen memberikan pengaruh terhadap nilai silhouette coefficient dan purity. Hal ini berarti single linkage selalu menghasilkan dokumen yang sama, sedangkan K-means masih bercampur dengan dokumen yang lain

Ashwagandha Derived Withanone Targets TPX2-Aurora A Complex: Computational and Experimental Evidence to its Anticancer Activity

Cancer is largely marked by genetic instability. Specific inhibition of individual proteins or signalling pathways that regulate genetic stability during cell division thus hold a great potential for cancer therapy. The Aurora A kinase is a Ser/Thr kinase that plays a critical role during mitosis and cytokinesis and is found upregulated in several cancer types. It is functionally regulated by its interactions with TPX2, a candidate oncogene. Aurora A inhibitors have been proposed as anticancer drugs that work by blocking its ATP binding site. This site is common to other kinases and hence these inhibitors lack specificity for Aurora A inhibition in particular, thus advocating the need of some alternative inhibition route. Previously, we identified TPX2 as a cellular target for withanone that selectively kill cancer cells. By computational approach, we found here that withanone binds to TPX2-Aurora A complex. In experiment, withanone treatment to cancer cells indeed resulted in dissociation of TPX2-Aurora A complex and disruption of mitotic spindle apparatus proposing this as a mechanism of the anticancer activity of withanone. From docking analysis, non-formation/disruption of the active TPX2-Aurora A association complex could be discerned. Our MD simulation results suggesting the thermodynamic and structural stability of TPX2-Aurora A in complex with withanone further substantiates the binding. We report a computational rationale of the ability of naturally occurring withanone to alter the kinase signalling pathway in an ATP-independent manner and experimental evidence in which withanone cause inactivation of the TPX2-Aurora A complex. The study demonstrated that TPX2-Aurora A complex is a target of withanone, a potential natural anticancer drug

Analysis of MD trajectories.

<p>(A) Plot of root mean square deviation (RMSD) of Cα of TPX2-Aurora A (protein) and TPX2/Aurora A /withanone (complex). RMSDs were calculated using the initial structures as templates. For protein (red) the reference is the PDB structure and for complex (blue) the reference is the initial docked structure. The trajectories were captured every 2.5 ps until the simulation time reached 6000 ps. (B) Plot of total energy of TPX2-Aurora A and TPX2/Aurora A/withanone (complex). The energy trajectories of both the protein (red) and the complex (blue) are stable over the entire length of simulation time with the energy of the complex always lower than that of the protein.</p

mRNA expression of Aurora A and TPX2 as analysed by RT-PCR is shown.

<p>Blood and fibroblasts represent the normal samples whereas the others are the cancer cell lines as indicated (A). Quantitation of the signals obtained by RT-PCR shown in B signified increase both in TPX2 and Aurora A in cancer cell lines.</p

Docking representations of Withanone.

<p>(A) Ligand docked into the receptor cavity (B) Docked Ligand inside the pocket of receptor mesh.</p

Interactions of docked withanone with rigid Aurora A/TPX2 receptor.

<p>(A) H-Bond interactions of the docked ligand with the macromolecule residues (B) Docked withanone disrupting the crucial non-covalent interactions already present in the undocked protein (C) Withanone forming hydrophobic interactions with the receptor.</p

Clustering results obtained from flexible docking of withanone into TPX2-Aurora A complex.

a<p>Number of GA runs are shown in parentheses.</p>b<p>Clustering is done with RMS tolerance of 5.0 Å.</p

Interactions of docked withanone with flexible receptor.

<p>(A) The docked withanone forming intermolecular interactions (B) Withanone forming extensive H-bonds with the residues of flexible receptor.</p