Yap Choon Kong

xHMMER3x2: Utilizing HMMER3's speed and HMMER2's sensitivity and specificity in the glocal alignment mode for improved large-scale protein domain annotation

Published date : 29 Nov 2016

BACKGROUND:
While the local-mode HMMER3 is notable for its massive speed improvement, the slower glocal-mode HMMER2 is more exact for domain annotation by enforcing full domain-to-sequence alignments. Since a unit of domain necessarily implies a unit of function, local-mode HMMER3 alone remains insufficient for precise function annotation tasks. In addition, the incomparable E-values for the same domain model by different HMMER builds create difficulty when checking for domain annotation consistency on a large-scale basis.

RESULTS:

type
Journal Paper
journal
Biology Direct 2016, 11:63, DOI: 10.1186/s13062-016-0163-0
Impact Factor
3.016

dissectHMMER: a HMMER-based score dissection framework that statistically evaluates fold-critical sequence segments for domain fold similarity

Published date : 01 Aug 2015

Background: Annotation transfer for function and structure within the sequence homology concept essentially requires protein sequence similarity for the secondary structural blocks forming the fold of a protein. A simplistic similarity approach in the case of non-globular segments (coiled coils, low complexity regions, transmembrane regions, long loops, etc.) is not justified and a pertinent source for mistaken homologies.

type
Journal Paper
journal
Biology Direct (2015) 10:39, doi: 10.1186/s13062-015-0068-3
Impact Factor
4.66

Automated Image Based Prominent Nucleoli Detection

Published date : 23 Jun 2015

Nucleolar changes in cancer cells are one of the cytologic features important to the tumor pathologist in cancer assessments of tissue biopsies. However, inter-observer variability and the manual approach to this work hamper the accuracy of the assessment by pathologists. In this paper, we propose a computational method for prominent nucleoli pattern detection. Materials and Methods: Thirty-five hematoxylin and eosin stained images were acquired from prostate cancer, breast cancer, renal clear cell cancer and renal papillary cell cancer tissues.

type
Journal Paper
journal
Journal of Pathology Informatics 2015, Vol. 6, Issue 1,doi: 10.4103/2153-3539.159232

Analyzing Cell and Tissure Morphologies Using Pattern Recognition Algorithms

Published date : 13 Feb 2015

This chapter concentrates on the development of biomedical image analysis. It begins with a discussion on three works for detecting objects in tissue and cellular images using image processing approaches. The chapter then presents a texture segmentation model for aiding diagnosis of premalignant endometrial disease. It also presents a method focusing on spot clustering for mutant detection in microscopy images of in vitro cultured keratinocytes. The chapter explains an ellipse detection method for cell and nucleus detection in microscopy image.

type
Book/Book Chapter
journal
Biomedical Image Understanding - Methods and Applications First Edition, 2015, Pg: 113-152, doi: 10.1002/9781118715321.ch4