Ruzzo–Tompa algorithm: Difference between revisions

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The '''Ruzzo-TompaRuzzo–Tompa algorithm''' or the '''RT algorithm'''<ref name=":0">{{Cite journal |last1=Spouge |first1=John L. |last2=Ramírez |first2=Leonardo Mariño |last3=Sheetlin |first3=Sergey L. |date=2014 |title=Searching for repeats, as an example of using the generalised Ruzzo-Tompa algorithm to find optimal subsequences with gaps |journal=International Journal of Bioinformatics Research and Applications |language=en |volume=10 |issue=4/5 |pages=384–408 |doi=10.1504/IJBRA.2014.062991 |issn=1744-5485 |pmc=4135518 |pmid=24989859}}</ref> is a linear[[Time complexity#Linear time|linear-time]] [[algorithm]] for finding all non-overlapping, contiguous, maximal scoring subsequences in a sequence of real numbers.<ref name="ruzzo_tompa">{{cite journal|last1=Ruzzo|first1=Walter L.|last2=Martin|first2=Tompa|title=A Linearlinear Timetime Algorithmalgorithm for Findingfinding Allall Maximalmaximal Scoringscoring Subsequencessubsequences|journal=Proceedings. International Conference on Intelligent Systems for Molecular Biology|date=1999|pages=234-241234–241|pmid=10786306|isbn=9781577350835|url=https://dl.acm.org/citation.cfm?id=660812|ref=ruzzo-tompa}}</ref> The Ruzzo–Tompa algorithm was proposed by Walter L. Ruzzo and Martin Tompa.<ref>{{Cite web |title=A Linear Time Algorithm for Finding All Maximal Scoring Subsequences |url=https://homes.cs.washington.edu/~ruzzo/papers/maxseq.pdf}}</ref> This algorithm is an improvement over previously known quadratic time algorithms.<ref name=":0" /> The maximum scoring subsequence from the set produced by the algorithm is also a solution to the [[Maximummaximum subarray problem]].

The Ruzzo–Tompa algorithm has applications in [[bioinformatics]],<ref name="karlin" /> [[web scraping]],<ref name="pasternack">{{cite book|last1=Pasternack|first1=Jeff|last2=Roth|first2=Dan|title=Proceedings of the 18th international conference on World wide web |chapter=Extracting article text from the web with maximum subsequence segmentation |date=2009|pages=971–980|doi=10.1145/1526709.1526840|isbn=9781605584874|s2cid=346124}}</ref> and [[information retrieval]].<ref name="liang">{{cite book|last1=Liang|first1=Shangsong|last2=Ren|first2=Zhaochun|last3=Weerkamp|first3=Wouter|last4=Meij|first4=Edgar|last5=de Rijke|first5=Maarten|title=Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management |chapter=Time-Aware Rank Aggregation for Microblog Search |date=2014|pages=989–998|doi=10.1145/2661829.2661905|isbn=9781450325981|citeseerx=10.1.1.681.6828|s2cid=14287901}}</ref>

The problem of find disjoint maximal subsequences is of practical importance in the analysis of [[DNA]]. Maximal subsequences algorithms have been used in the identification of transmembrane segments and the evaluation of [[sequence homology]]<ref name="ruzzo_tompa">{{cite journal|last1=Karlin|first1=S|last2=Altschul|first2=SF|title=Applications and statistics for multiple high-scoring segments in molecular sequences|journal=Proceedings of the National Academy of Sciences of the United States of America|date=Jun 15, 1993|volume=90|issue=12|pages=5873-5877|pmid=8390686}}</ref>

==Applications==

==Problem Definition==

===Bioinformatics===

The Ruzzo–Tompa algorithm has been used in [[Bioinformatics]] tools to study biological data. The problem of findfinding disjoint maximal subsequences is of practical importance in the analysis of [[DNA]]. Maximal subsequences algorithms have been used in the identification of transmembrane segments and the evaluation of [[sequence homology]].<ref name="ruzzo_tompakarlin">{{cite journal|last1=Karlin|first1=S|last2=Altschul|first2=SF|title=Applications and statistics for multiple high-scoring segments in molecular sequences|journal=Proceedings of the National Academy of Sciences of the United States of America|date=Jun 15, 1993|volume=90|issue=12|pages=5873-58775873–5877|pmid=8390686|pmc=46825|doi=10.1073/pnas.90.12.5873|bibcode=1993PNAS...90.5873K|doi-access=free}}</ref>

The algorithm is used in [[sequence alignment]] which is used as a method of identifying similar [[DNA]], [[RNA]], or [[protein]] sequences.<ref>{{Cite book |last1=Spouge |first1=John L. |last2=Mariño-Ramírez |first2=Leonardo |last3=Sheetlin |first3=Sergey L. |title=2012 IEEE 2nd International Conference on Computational Advances in Bio and medical Sciences (ICCABS) |chapter=The ruzzo-tompa algorithm can find the maximal paths in weighted, directed graphs on a one-dimensional lattice |year=2012 |pages=1–6 |doi=10.1109/ICCABS.2012.6182645|isbn=978-1-4673-1321-6 |s2cid=14584619 }}</ref> Accounting for the ordering of pairs of high-scoring subsequences in two sequences creates better sequence alignments. This is because the biological model suggests that separate high-scoring subsequence pairs arise from insertions or deletions within a matching region. Requiring consistent ordering of high-scoring subsequence pairs increases their statistical significance.<ref name="karlin" />

==Algorithm==

===Web scraping===

The Ruzzo–Tompa algorithm is used in [[Web scraping]] to extract information from web pages. Pasternack and Roth proposed a method for extracting important blocks of text from HTML documents. The web pages are first [[Lexical analysis#Tokenization|tokenized]] and the score for each token is found using local, token-level classifiers.<ref>{{Cite web |date=2021-07-30 |title=Web Scraping: Everything You Need To Know |url=https://datamam.com/web-scraping/ |access-date=2023-02-16 |website=Datamam |language=en-US}}</ref> A modified version of the Ruzzo–Tompa algorithm is then used to find the k highest-valued subsequences of tokens. These subsequences are then used as predictions of important blocks of text in the article.<ref name="pasternack" />

===Information retrieval===

The Ruzzo–Tompa algorithm has been used in [[Information retrieval]] search algorithms. Liang et al. proposed a [[data fusion]] method to combine the search results of several microblog search algorithms. In their method, the Ruzzo–Tompa algorithm is used to detect [[Bursting|bursts]] of information.<ref name="liang" />

==Problem Definitiondefinition==

The problem of finding all maximal subsequences is defined as follows: Given a list of real numbered scores <math>x_1,x_2,\ldots,x_n</math>, find the list of contiguous subsequences that gives the greatest total score, where the score of each subsequence <math>S_{i,j} = \sum_{i\leq k\leq j} x_k</math>. The subsequences must be disjoint (non-overlapping) and have a positive score.<ref>{{Cite book |last1=Spouge |first1=John L. |last2=Mariño-Ramírez |first2=Leonardo |last3=Sheetlin |first3=Sergey L. |title=2012 IEEE 2nd International Conference on Computational Advances in Bio and medical Sciences (ICCABS) |chapter=The ruzzo-tompa algorithm can find the maximal paths in weighted, directed graphs on a one-dimensional lattice |year=2012 |pages=1–6 |doi=10.1109/ICCABS.2012.6182645|isbn=978-1-4673-1321-6 |s2cid=14584619 }}</ref>

==Other algorithms==

There are several approaches to solving the all maximal scoring subsequences problem. A natural approach is to use existing, linear time algorithms to find the maximum subsequence (see [[maximum subarray problem]]) and then recursively find the maximal subsequences to the left and right of the maximum subsequence. The analysis of this algorithm is similar to that of [[Quicksort]]: The maximum subsequence could be small in comparison to the rest of sequence, leading to a running time of <math>O(n^2)</math> in the worst case.

==Algorithm==

The standard implementation of the Ruzzo-Tompa algorithm runs in <math>O(n)</math> time and uses <math>O(n)</math> space, where <math>n</math> is the length of the list of scores. The algorithm uses [[dynamic programming]] to progressively build the final solution by incrementally solving progressively larger subsets of the problem. The description of the algorithm provided by Ruzzo and Tompa is as follows:

[[File:Animation of Ruzzo-Tompa Algorithm.ogv|300px|thumb|This animation shows the Ruzzo–Tompa algorithm running with an input sequence of 11 integers each represented by a line segment in the graph. Segments with bold lines represent maximal segments found so far. The animation shows the state of <math>I, R </math> and <math>L</math> at each step. Below that it shows the current state the algorithm which correspond to steps 1–4 in the [[#Algorithm|Algorithm]] section of this page. The red highlight shows the algorithm finding a value for <math>j</math> in steps 1 and 3. If the value of <math>j</math> satisfies the inequalities in those steps the highlight turns green.

: Read the scores left to right and maintain the cumulative sum of the scores read. Maintain an ordered list <math>I_1,I_2,...,I_j</math> of disjoint subsequences. For each subsequence <math>I_j</math>, record the cumulative total <math>L_j</math> of all scores up to but not including the leftmost score of <math>I_j</math>, and the total <math>R_j</math> up to and including the rightmost score of <math>I_j</math>.

At the end of the animation, the maximal subsequences will be bolded and displayed in <math>I</math>.<ref name=":0" />

: The lists are initially empty. Scores are read from left to right and are processed as follows. Nonpositive scores are require no special processing, so the next score is read. A positive score is incorporated into a new sub-sequence <math>I_k</math> of length one that is then integrated into the list by the following process.

]]

The standard implementation of the Ruzzo-TompaRuzzo–Tompa algorithm runs in <math>O(n)</math> time and uses <math>''O''(''n'')</math> space, where <math>''n</math>'' is the length of the list of scores. The algorithm uses [[dynamic programming]] to progressively build the final solution by incrementally solving progressively larger subsets of the problem. The description of the algorithm provided by Ruzzo and Tompa is as follows:

: Read the scores left to right and maintain the cumulative sum of the scores read. Maintain an ordered list <math>I_1,I_2,...\ldots,I_j</math> of disjoint subsequences. For each subsequence <math>I_j</math>, record the cumulative total <math>L_j</math> of all scores up to but not including the leftmost score of <math>I_j</math>, and the total <math>R_j</math> up to and including the rightmost score of <math>I_j</math>.

: The lists are initially empty. Scores are read from left to right and are processed as follows. Nonpositive scores are require no special processing, so the next score is read. A positive score is incorporated into a new sub-sequence <math>I_k</math> of length one that is then integrated into the list by the following process.:

# The list <math>I</math> is searched from right to left for the maximum value of <math>j</math> satisfying <math>L_j<L_k</math>

# If there is no such <math>j</math>, then add <math>I_k</math> to the end of the list.

# If there is such a <math>j</math>, and <math>R_j \geq R_k</math>, then add <math>I_k</math> to the end of the list.

# Otherwise (i.e., there is such a j, but <math>R_j < R_k</math>), extend the subsequence <math>I_k</math> to the left to encompass everything up to and including the leftmost score in <math>I_j</math>. Delete subsequences <math>I_j,I_j+1,...\ldots,I_k-1</math> from the list, and append <math>I_k</math> to the end of the list. Reconsider the newly extended subsequence <math>I_k</math> (now renumbered <math>I_j</math>) as in step 1.

:Once the end of the input is reached, all subsequences remaining on the list <math>I</math> are maximal.<ref name="ruzzo_tompa">< /ref>

The following [[Python (programming language)|Python]] code implements the Ruzzo-TompaRuzzo–Tompa algorithm:

As example of the algorithm running, consider the input score list <math>L=[4,-5,3,-2,1,2]</math>. On input <math>4</math> in step 1, no satisfying <math>j</math> is found so step 2 applies and <math>[4]</math> is appended to <math>I</math>, so <math>I=[[4]], R=[4], L=[0]</math>. The input <math>-5</math> is skipped and the input <math>3</math> is read. In step 1, no satisfying <math>j</math> is found so <math>[3]</math> is appended to <math>I</math>, so <math>I=[[4],[3]], R=[4,2], L=[0,-1]</math>. The input <math>-2</math> is skipped and the input <math>1</math> is read. In step 1, a value of <math>j=1</math> is found to satisfy <math>L[j]<L[k]</math>, so step 3 applies. In step 3, <math>R[j]\ngeq R[k]</math>, so <math>[1] is appended to <math>I</math>, and now <math>I=[[4],[3],[1]], R=[4,2,1], L=[0,-1,0]</math>.

<sourcesyntaxhighlight lang="python" line="1">

The following [[Python (programming language)|Python]] code implements the Ruzzo-Tompa algorithm:

def RuzzoTomparuzzo_tompa(scores):

"""Ruzzo–Tompa algorithm."""

<source lang="python" line="1">

totalk = 0;

def RuzzoTompa(scores):

ktotal = 0

total = 0;

# Allocating arrays of size n

I, L, R, Lidx = [[0] * len(scores) for _ in range(4)]

for i, s in enumerate(scores):

total += s

if s > 0:

# store I[k] by (start,end) indices of scores

I[k] = (i, i + 1)

Lidx[k] = i

L[k] = total - s

R[k] = total

while( True):

maxj = None

for j in range(k - 1, -1, -1):

if L[j] < L[k]:

maxj = j

break;

if maxj !=is not None and R[maxj] < R[k]:

I[maxj] = (Lidx[maxj], i + 1)

R[maxj] = total

k = maxj

else:

k += 1

break;

# Getting maximal subsequences using stored indices

return [scores[I[l][0] : I[l][1]] for l in range(k)]

</syntaxhighlight>

</source>

== See also ==

* [[Maximum subarray problem]]

* [[Quicksort]]

== References ==

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== Further reading ==

* {{cite journal | last1=Ali | first1=Syed Arslan | last2=Raza | first2=Basit | last3=Malik | first3=Ahmad Kamran | last4=Shahid | first4=Ahmad Raza | last5=Faheem | first5=Muhammad | last6=Alquhayz | first6=Hani | last7=Kumar | first7=Yogan Jaya | title=An Optimally Configured and Improved Deep Belief Network (OCI-DBN) Approach for Heart Disease Prediction Based on Ruzzo–Tompa and Stacked Genetic Algorithm | journal=IEEE Access | publisher=Institute of Electrical and Electronics Engineers (IEEE) | volume=8 | year=2020 | issn=2169-3536 | doi=10.1109/access.2020.2985646 | pages=65947–65958| bibcode=2020IEEEA...865947A | s2cid=215817246 | doi-access=free }}

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