Chen Bullock (crookkey0)

Experiments on different examples validate the effectiveness of the proposed algorithm on TZMG problems.This article studies the large-scale subspace clustering (LS²C) problem with millions of data points. Many popular subspace clustering methods cannot directly handle the LS²C problem although they have been considered to be state-of-the-art methods for small-scale data points. A simple reason is that these methods often choose all data points as a large dictionary to build huge coding models, which results in high time and space complexity. Alexidine In this article, we develop a learnable subspace clustering paradigm to efficiently solve the LS²C problem. The key concept is to learn a parametric function to partition the high-dimensional subspaces into their underlying low-dimensional subspaces instead of the computationally demanding classical coding models. Moreover, we propose a unified, robust, predictive coding machine (RPCM) to learn the parametric function, which can be solved by an alternating minimization algorithm. Besides, we provide a bounded contraction analysis of the parametric function. To the best of our knowledge, this article is the first work to efficiently cluster millions of data points among the subspace clustering methods. Experiments on million-scale data sets verify that our paradigm outperforms the related state-of-the-art methods in both efficiency and effectiveness.Over the past decade, the demand for automated protein function prediction has increased due to the volume of newly sequenced proteins. In this paper, we address the function prediction task by developing an ensemble system automatically assigning Gene Ontology (GO) terms to the given input protein sequence. We develop an ensemble system which combines the GO predictions made by random forest (RF) and neural network (NN) classifiers. Both RF and NN models rely on features derived from BLAST sequence alignments, taxonomy and protein signature analysis tools. In addition, we report on experiments with a NN model that directly analyzes the amino acid sequence as its sole input, using a convolutional layer. The Swiss-Prot database is used as the training and evaluation data. In the CAFA3 evaluation, which relies on experimental verification of the functional predictions, our submitted ensemble model demonstrates competitive performance ranking among top-10 best-performing systems out of over 100 submitted systems. In this paper, we evaluate and further improve the CAFA3-submitted system. Our machine learning models together with the data pre-processing and feature generation tools are publicly available as an open source software at https//github.com/TurkuNLP/CAFA3.SARS-CoV-2 encodes the Mac1 domain within the large non-structural-protein 3, which has an ADP-ribosylhydrolase activity conserved in other coronaviruses. ADP-ribosylhydrolase activity of Mac1 makes it an essential virulence factor for the pathogenicity of CoV. They have a regulatory role in counteracting host-mediated antiviral ADP-ribosylation, which is unique part of host response towards viral infections. Mac1 shows highly conserved residues in the binding pocket for the mono and poly ADP-ribose. Therefore, SARS-CoV-2 Mac1 enzyme is considered as an ideal drug target and inhibitors developed against them can possess a broad antiviral activity against CoV. Considering this, the ADP-Ribose-1"-phosphate bound closed form of Mac1 domain is considered for screening with large database of ZINC. XP docking and QPLD provides strong potential lead compounds, that perfectly fits inside the binding pocket. Quantum mechanical studies expose that, substrate and leads have similar electron donor ability in the head regions, that allocates tight binding inside the substrate-binding pocket. Molecular dynamics study confirms the substrate and new lead molecules presence of electron donor and acceptor makes the interactions tight inside the binding pocket. Overall binding phenomenon show