Methods for simultaneously identifying coherent local clusters with smooth global patterns in gene expression profiles

<p>Abstract</p> <p>Background</p> <p>The hierarchical clustering tree (HCT) with a dendrogram <abbrgrp><abbr bid="B1">1</abbr></abbrgrp> and the singular value decomposition (SVD) with a dimension-reduced representative map <abbrgrp><abbr bid="B2">2</abbr></abbrgrp> are popular methods for two-way sorting the gene-by-array matrix map employed in gene expression profiling. While HCT dendrograms tend to optimize local coherent clustering patterns, SVD leading eigenvectors usually identify better global grouping and transitional structures.</p> <p>Results</p> <p>This study proposes a flipping mechanism for a conventional agglomerative HCT using a rank-two ellipse (R2E, an improved SVD algorithm for sorting purpose) seriation by Chen <abbrgrp><abbr bid="B3">3</abbr></abbrgrp> as an external reference. While HCTs always produce permutations with good local behaviour, the rank-two ellipse seriation gives the best global grouping patterns and smooth transitional trends. The resulting algorithm automatically integrates the desirable properties of each method so that users have access to a clustering and visualization environment for gene expression profiles that preserves coherent local clusters and identifies global grouping trends.</p> <p>Conclusion</p> <p>We demonstrate, through four examples, that the proposed method not only possesses better numerical and statistical properties, it also provides more meaningful biomedical insights than other sorting algorithms. We suggest that sorted proximity matrices for genes and arrays, in addition to the gene-by-array expression matrix, can greatly aid in the search for comprehensive understanding of gene expression structures. Software for the proposed methods can be obtained at <url>http://gap.stat.sinica.edu.tw/Software/GAP</url>.</p

Protemot: prediction of protein binding sites with automatically extracted geometrical templates

Geometrical analysis of protein tertiary substructures has been an effective approach employed to predict protein binding sites. This article presents the Protemot web server that carries out prediction of protein binding sites based on the structural templates automatically extracted from the crystal structures of protein–ligand complexes in the PDB (Protein Data Bank). The automatic extraction mechanism is essential for creating and maintaining a comprehensive template library that timely accommodates to the new release of PDB as the number of entries continues to grow rapidly. The design of Protemot is also distinctive by the mechanism employed to expedite the analysis process that matches the tertiary substructures on the contour of the query protein with the templates in the library. This expediting mechanism is essential for providing reasonable response time to the user as the number of entries in the template library continues to grow rapidly due to rapid growth of the number of entries in PDB. This article also reports the experiments conducted to evaluate the prediction power delivered by the Protemot web server. Experimental results show that Protemot can deliver a superior prediction power than a web server based on a manually curated template library with insufficient quantity of entries. Availability:

Scutellaria baicalensis decreases ritonavir-induced nausea

BACKGROUND: Protease inhibitors, particularly ritonavir, causes significant gastrointestinal disturbances such as nausea, even at low doses. This ritonavir-induced nausea could be related to its oxidative stress in the gut. Alleviation of drug-induced nausea is important in effectively increasing patients' compliance and improving their quality of life. Conventional anti-emetic drugs can only partially abate the symptoms in these patients, and their cost has also been a concern. Rats respond to nausea-producing emetic stimuli by increasing consumption of non-nutritive substances like kaolin or clay, a phenomenon known as pica. In this study, we used this rat pica model to evaluate the effects of Scutellaria baicalensis, a commonly used oriental herbal medicine, on ritonavir-induced nausea. RESULTS: Rats treated with 20 mg/kg ritonavir significant caused increases of kaolin consumption at 24 to 48 hr (P < 0.01). Pretreatment with 0.3 and 3 mg/kg Scutellaria baicalensis extract significantly decreased ritonavir-induced kaolin intake in a dose-related manner (P < 0.01). Compared to vehicle treatment, the extract completely prevented ritonavir-induced kaolin consumption at dose 3 mg/kg. The area under the curves (AUC) for kaolin intake from time 0 to 120 hr for vehicle only, ritonavir only, SbE 0.3 mg/kg plus ritonavir, and SbE 3 mg/kg plus ritonavir were 27.3 g•hr, 146.7 g•hr, 123.2 g•hr, and 32.7 g•hr, respectively. The reduction in area under the curves of kaolin intake from time 0 to 120 hr between ritonavir only and SbE 0.3 mg/kg plus ritonavir, ritonavir only and SbE 3 mg/kg plus ritonavir were 16.0% and 77.7%, respectively. CONCLUSION: Scutellaria baicalensis significantly attenuated ritonavir-induced pica, and demonstrated a potential in treating ritonavir-induced nausea

SegSort: Segmentation by Discriminative Sorting of Segments

Almost all existing deep learning approaches for semantic segmentation tackle this task as a pixel-wise classification problem. Yet humans understand a scene not in terms of pixels, but by decomposing it into perceptual groups and structures that are the basic building blocks of recognition. This motivates us to propose an end-to-end pixel-wise metric learning approach that mimics this process. In our approach, the optimal visual representation determines the right segmentation within individual images and associates segments with the same semantic classes across images. The core visual learning problem is therefore to maximize the similarity within segments and minimize the similarity between segments. Given a model trained this way, inference is performed consistently by extracting pixel-wise embeddings and clustering, with the semantic label determined by the majority vote of its nearest neighbors from an annotated set. As a result, we present the SegSort, as a first attempt using deep learning for unsupervised semantic segmentation, achieving $76\%$ performance of its supervised counterpart. When supervision is available, SegSort shows consistent improvements over conventional approaches based on pixel-wise softmax training. Additionally, our approach produces more precise boundaries and consistent region predictions. The proposed SegSort further produces an interpretable result, as each choice of label can be easily understood from the retrieved nearest segments.Comment: In ICCV 2019. Webpage & Code: https://jyhjinghwang.github.io/projects/segsort.htm