Seeker: alignment-free identification of bacteriophage genomes by deep learning

Hessian Free Deep Learning

Optimization techniques used in Machine Learning play an important role in the training of the Neural Network in regression and classification tasks. Predominantly, first order optimization methods such as Gradient Descent have been used in the training of Neural Networks, since second order methods, such as Newton’s method, are computationally infeasible. However, second order methods show muc...

An Alignment-Free Distance Measure for Closely Related Genomes

Phylogeny reconstruction on a genome scale remains computationally challenging even for closely related organisms. Here we propose an alignmentfree pairwise distance measure, Kr, for genomes separated by less than approximately 0.5 mismatches/nucleotide. We have implemented the computation of Kr based on enhanced suffix arrays in the program kr, which is freely available from guanine.evolbio.mp...

Alignment of whole genomes.

A new system for aligning whole genome sequences is described. Using an efficient data structure called a suffix tree, the system is able to rapidly align sequences containing millions of nucleotides. Its use is demonstrated on two strains of Mycoplasma tuberculosis, on two less similar species of Mycoplasma bacteria and on two syntenic sequences from human chromosome 12 and mouse chromosome 6....

Studies on Bacteriophage and Metabolic Identification of Brucella Strains

Deep Spatial Feature Reconstruction for Partial Person Re-identification: Alignment-Free Approach

Partial person re-identification (re-id) is a challenging problem, where only several partial observations (images) of people are available for matching. However, few studies have provided flexible solutions to identifying a person in an image containing arbitrary part of the body. In this paper, we propose a fast and accurate matching method to address this problem. The proposed method leverag...