A Scalable Method for Readable Tree Layouts.

Large tree structures are ubiquitous and real-world relational datasets often have information associated with nodes (e.g., labels or other attributes) and edges (e.

Using convolutional neural networks for tick image recognition - a preliminary exploration.

Smartphone cameras and digital devices are increasingly used in the capture of tick images by the public as citizen scientists, and rapid advances in deep learning and computer vision has enabled brand new image recognition models to be trained. However, there is currently no web-based or mobile application that supports automated classification of tick images. The purpose of this study was to compare the accuracy of a deep learning model pre-trained with millions of annotated images in Imagenet, against a shallow custom-build convolutional neural network (CNN) model for the classification of common hard ticks present in anthropic areas from northeastern USA.

PhISCS: a combinatorial approach for subperfect tumor phylogeny reconstruction via integrative use of single-cell and bulk sequencing data.

Available computational methods for tumor phylogeny inference via single-cell sequencing (SCS) data typically aim to identify the most likely satisfying the (ISA). However, the limitations of SCS technologies including frequent allele dropout and variable sequence coverage may prohibit a perfect phylogeny. In addition, ISA violations are commonly observed in tumor phylogenies due to the loss of heterozygosity, deletions, and convergent evolution.

A multi-labeled tree dissimilarity measure for comparing "clonal trees" of tumor progression.

We introduce a new dissimilarity measure between a pair of "clonal trees", each representing the progression and mutational heterogeneity of a tumor sample, constructed by the use of single cell or bulk high throughput sequencing data. In a clonal tree, each vertex represents a specific tumor clone, and is labeled with one or more mutations in a way that each mutation is assigned to the oldest clone that harbors it. Given two clonal trees, our multi-labeled tree dissimilarity (MLTD) measure is defined as the minimum number of mutation/label deletions, (empty) leaf deletions, and vertex (clonal) expansions, applied in any order, to convert each of the two trees to the maximum common tree.

Antigenic: An improved prediction model of protective antigens.

An antigen is a protein capable of triggering an effective immune system response. Protective antigens are the ones that can invoke specific and enhanced adaptive immune response to subsequent exposure to the specific pathogen or related organisms. Such proteins are therefore of immense importance in vaccine preparation and drug design.

isGPT: An optimized model to identify sub-Golgi protein types using SVM and Random Forest based feature selection.

The Golgi Apparatus (GA) is a key organelle for protein synthesis within the eukaryotic cell. The main task of GA is to modify and sort proteins for transport throughout the cell. Proteins permeate through the GA on the ER (Endoplasmic Reticulum) facing side (cis side) and depart on the other side (trans side).

CRISPRpred: A flexible and efficient tool for sgRNAs on-target activity prediction in CRISPR/Cas9 systems.

The CRISPR/Cas9-sgRNA system has recently become a popular tool for genome editing and a very hot topic in the field of medical research. In this system, Cas9 protein is directed to a desired location for gene engineering and cleaves target DNA sequence which is complementary to a 20-nucleotide guide sequence found within the sgRNA. A lot of experimental efforts, ranging from in vivo selection to in silico modeling, have been made for efficient designing of sgRNAs in CRISPR/Cas9 system.