Sequence mappability is an important task in genome resequencing. In the (
Protein–protein interaction (PPI) is vital for life processes, disease treatment, and drug discovery. The computational prediction of PPI is relatively inexpensive and efficient when compared to traditional wetlab experiments. Given a new protein, one may wish to find whether the protein has any PPI relationship with other existing proteins. Current computational PPI prediction methods usually compare the new protein to existing proteins one by one in a pairwise manner. This is time consuming.
In this work, we propose a more efficient model, called deep hash learning proteinandprotein interaction (DHLPPI), to predict allagainstall PPI relationships in a database of proteins. First, DHLPPI encodes a protein sequence into a binary hash code based on deep features extracted from the protein sequences using deep learning techniques. This encoding scheme enables us to turn the PPI discrimination problem into a much simpler searching problem. The binary hash code for a protein sequence can be regarded as a number. Thus, in the prescreening stage of DHLPPI, the string matching problem of comparing a protein sequence against a database with
The experimental results confirmed that DHLPPI is feasible and effective. Using a dataset with strictly negative PPI examples of four species, DHLPPI is shown to be superior or competitive when compared to the other stateoftheart methods in terms of precision, recall or F1 score. Furthermore, in the prediction stage, the proposed DHLPPI reduced the time complexity from
 Award ID(s):
 1816005
 Publication Date:
 NSFPAR ID:
 10368623
 Journal Name:
 BMC Bioinformatics
 Volume:
 23
 Issue:
 1
 ISSN:
 14712105
 Publisher:
 Springer Science + Business Media
 Sponsoring Org:
 National Science Foundation
More Like this

Abstract k ,m )mappability problem, for a given sequenceT of lengthn , the goal is to compute a table whosei th entry is the number of indices such that the length$$j \ne i$$ $j\ne i$m substrings ofT starting at positionsi andj have at mostk mismatches. Previous works on this problem focused on heuristics computing a rough approximation of the result or on the case of . We present several efficient algorithms for the general case of the problem. Our main result is an algorithm that, for$$k=1$$ $k=1$ , works in$$k=O(1)$$ $k=O\left(1\right)$ space and, with high probability, in$$O(n)$$ $O\left(n\right)$ time. Our algorithm requires a careful adaptation of the$$O(n \cdot \min \{m^k,\log ^k n\})$$ $O(n\xb7min\{{m}^{k},{log}^{k}n\})$k errata trees of Cole et al. [STOC 2004] to avoid multiple counting of pairs of substrings. Our technique can also be applied to solve the allpairs Hamming distance problem introduced by Crochemore et al. [WABI 2017]. We further develop time algorithms to compute$$O(n^2)$$ $O\left({n}^{2}\right)$all (k ,m )mappability tables for a fixedm and all or a fixed$$k\in \{0,\ldots ,m\}$$ $k\in \{0,\dots ,m\}$k and all . Finally, we show that, for$$m\in \{k,\ldots ,n\}$$ $m\in \{k,\dots ,n\}$ , the ($$k,m = \Theta (\log n)$$ $k,m=\Theta (logn)$k ,m )mappability problem cannot be solved in strongly subquadratic time unless the Strong Exponential Time Hypothesis fails. This is an improved and extended version of a paper presented at SPIRE 2018. 
Abstract We continue the program of proving circuit lower bounds via circuit satisfiability algorithms. So far, this program has yielded several concrete results, proving that functions in
and other complexity classes do not have small circuits (in the worst case and/or on average) from various circuit classes$\mathsf {Quasi}\text {}\mathsf {NP} = \mathsf {NTIME}[n^{(\log n)^{O(1)}}]$ $\mathrm{Quasi}\mathrm{NP}=\mathrm{NTIME}\left[{n}^{{\left(\mathrm{log}n\right)}^{O\left(1\right)}}\right]$ , by showing that$\mathcal { C}$ $C$ admits nontrivial satisfiability and/or$\mathcal { C}$ $C$# SAT algorithms which beat exhaustive search by a minor amount. In this paper, we present a new strong lower bound consequence of having a nontrivial# SAT algorithm for a circuit class . Say that a symmetric Boolean function${\mathcal C}$ $C$f (x _{1},…,x _{n}) issparse if it outputs 1 onO (1) values of . We show that for every sparse${\sum }_{i} x_{i}$ ${\sum}_{i}{x}_{i}$f , and for all “typical” , faster$\mathcal { C}$ $C$# SAT algorithms for circuits imply lower bounds against the circuit class$\mathcal { C}$ $C$ , which may be$f \circ \mathcal { C}$ $f\circ C$stronger than itself. In particular:$\mathcal { C}$ $C$# SAT algorithms forn ^{k}size circuits running in 2^{n}/$\mathcal { C}$ $C$n ^{k}time (for allk ) implyN E X P does not have circuits of polynomial size.$(f \circ \mathcal { C})$ $(f\circ C)$# SAT algorithms for size$2^{n^{{\varepsilon }}}$ ${2}^{{n}^{\epsilon}}$ circuits running in$\mathcal { C}$ $C$ time (for some$2^{nn^{{\varepsilon }}}$ ${2}^{n{n}^{\epsilon}}$ε > 0) implyQ u a s i N P does not have circuits of polynomial size.$(f \circ \mathcal { C})$ $(f\circ C)$Applying
# SAT algorithms from the literature, one immediate corollary of our results is thatQ u a s i N P does not haveE M A J ∘A C C ^{0}∘T H R circuits of polynomialmore » 
Abstract In a Merlin–Arthur proof system, the proof verifier (Arthur) accepts valid proofs (from Merlin) with probability 1, and rejects invalid proofs with probability arbitrarily close to 1. The running time of such a system is defined to be the length of Merlin’s proof plus the running time of Arthur. We provide new Merlin–Arthur proof systems for some key problems in finegrained complexity. In several cases our proof systems have optimal running time. Our main results include:
Certifying that a list of
n integers has no 3SUM solution can be done in Merlin–Arthur time . Previously, Carmosino et al. [ITCS 2016] showed that the problem has a nondeterministic algorithm running in$$\tilde{O}(n)$$ $\stackrel{~}{O}\left(n\right)$ time (that is, there is a proof system with proofs of length$$\tilde{O}(n^{1.5})$$ $\stackrel{~}{O}\left({n}^{1.5}\right)$ and a deterministic verifier running in$$\tilde{O}(n^{1.5})$$ $\stackrel{~}{O}\left({n}^{1.5}\right)$ time).$$\tilde{O}(n^{1.5})$$ $\stackrel{~}{O}\left({n}^{1.5}\right)$Counting the number of
k cliques with total edge weight equal to zero in ann node graph can be done in Merlin–Arthur time (where$${\tilde{O}}(n^{\lceil k/2\rceil })$$ $\stackrel{~}{O}\left({n}^{\lceil k/2\rceil}\right)$ ). For odd$$k\ge 3$$ $k\ge 3$k , this bound can be further improved for sparse graphs: for example, counting the number of zeroweight triangles in anm edge graph can be done in Merlin–Arthur time . Previous Merlin–Arthur protocols by Williams [CCC’16] and Björklund and Kaski [PODC’16] could only count$${\tilde{O}}(m)$$ $\stackrel{~}{O}\left(m\right)$k cliques in unweighted graphs, and had worse running times for smallk .Computing the AllPairsmore »
Certifying that an
n variablek CNF is unsatisfiable can be done in Merlin–Arthur time . We also observe an algebrization barrier for the previous$$2^{n/2  n/O(k)}$$ ${2}^{n/2n/O\left(k\right)}$ time Merlin–Arthur protocol of R. Williams [CCC’16] for$$2^{n/2}\cdot \textrm{poly}(n)$$ ${2}^{n/2}\xb7\text{poly}\left(n\right)$ SAT: in particular, his protocol algebrizes, and we observe there is no algebrizing protocol for$$\#$$ $\#$k UNSAT running in time. Therefore we have to exploit nonalgebrizing properties to obtain our new protocol.$$2^{n/2}/n^{\omega (1)}$$ ${2}^{n/2}/{n}^{\omega \left(1\right)}$ Due to the centrality of these problems in finegrained complexity, our results have consequences for many other problems of interest. For example, our work implies that certifying there is no Subset Sum solution toCertifying a Quantified Boolean Formula is true can be done in Merlin–Arthur time
. Previously, the only nontrivial result known along these lines was an Arthur–Merlin–Arthur protocol (where Merlin’s proof depends on some of Arthur’s coins) running in$$2^{4n/5}\cdot \textrm{poly}(n)$$ ${2}^{4n/5}\xb7\text{poly}\left(n\right)$ time.$$2^{2n/3}\cdot \textrm{poly}(n)$$ ${2}^{2n/3}\xb7\text{poly}\left(n\right)$n integers can be done in Merlin–Arthur time , improving on the previous best protocol by Nederlof [IPL 2017] which took$$2^{n/3}\cdot \textrm{poly}(n)$$ ${2}^{n/3}\xb7\text{poly}\left(n\right)$ time.$$2^{0.49991n}\cdot \textrm{poly}(n)$$ ${2}^{0.49991n}\xb7\text{poly}\left(n\right)$ 
Abstract Let
be an elliptically fibered$$X\rightarrow {{\mathbb {P}}}^1$$ $X\to {P}^{1}$K 3 surface, admitting a sequence of Ricciflat metrics collapsing the fibers. Let$$\omega _{i}$$ ${\omega}_{i}$V be a holomorphicSU (n ) bundle overX , stable with respect to . Given the corresponding sequence$$\omega _i$$ ${\omega}_{i}$ of Hermitian–Yang–Mills connections on$$\Xi _i$$ ${\Xi}_{i}$V , we prove that, ifE is a generic fiber, the restricted sequence converges to a flat connection$$\Xi _i_{E}$$ ${\Xi}_{i}{}_{E}$ . Furthermore, if the restriction$$A_0$$ ${A}_{0}$ is of the form$$V_E$$ ${V}_{E}$ for$$\oplus _{j=1}^n{\mathcal {O}}_E(q_j0)$$ ${\oplus}_{j=1}^{n}{O}_{E}({q}_{j}0)$n distinct points , then these points uniquely determine$$q_j\in E$$ ${q}_{j}\in E$ .$$A_0$$ ${A}_{0}$ 
Abstract As the use of spectral/
hp element methods, and highorder finite element methods in general, continues to spread, community efforts to create efficient, optimized algorithms associated with fundamental highorder operations have grown. Core tasks such as solution expansion evaluation at quadrature points, stiffness and mass matrix generation, and matrix assembly have received tremendous attention. With the expansion of the types of problems to which highorder methods are applied, and correspondingly the growth in types of numerical tasks accomplished through highorder methods, the number and types of these core operations broaden. This work focuses on solution expansion evaluation at arbitrary points within an element. This operation is core to many postprocessing applications such as evaluation of streamlines and pathlines, as well as to field projection techniques such as mortaring. We expand barycentric interpolation techniques developed on an interval to 2D (triangles and quadrilaterals) and 3D (tetrahedra, prisms, pyramids, and hexahedra) spectral/hp element methods. We provide efficient algorithms for their implementations, and demonstrate their effectiveness using the spectral/hp element libraryNektar++ by running a series of baseline evaluations against the ‘standard’ Lagrangian method, where an interpolation matrix is generated and matrixmultiplication applied to evaluate a point at a given location. We present results from a rigorous seriesmore »