Minimum Edit Distance Research Articles

Revisiting the complexity of and algorithms for the graph traversal edit distance and its variants

The graph traversal edit distance (GTED), introduced by Ebrahimpour Boroojeny et al. (2018), is an elegant distance measure defined as the minimum edit distance between strings reconstructed from Eulerian trails in two edge-labeled graphs. GTED can be used to infer evolutionary relationships between species by comparing de Bruijn graphs directly without the computationally costly and error-prone process of genome assembly. Ebrahimpour Boroojeny et al. (2018) propose two ILP formulations for GTED and claim that GTED is polynomially solvable because the linear programming relaxation of one of the ILPs always yields optimal integer solutions. The claim that GTED is polynomially solvable is contradictory to the complexity results of existing string-to-graph matching problems. We resolve this conflict in complexity results by proving that GTED is NP-complete and showing that the ILPs proposed by Ebrahimpour Boroojeny et al. do not solve GTED but instead solve for a lower bound of GTED and are not solvable in polynomial time. In addition, we provide the first two, correct ILP formulations of GTED and evaluate their empirical efficiency. These results provide solid algorithmic foundations for comparing genome graphs and point to the direction of heuristics. The source code to reproduce experimental results is available at https://github.com/Kingsford-Group/gtednewilp/.

1426. Integrating artificial intelligence and dynamic programming to identify microbial clusters through antimicrobial susceptibility sequence analysis

Abstract Background The spread of infectious diseases and antimicrobial resistance poses a significant threat, especially in developing countries where traditional DNA fingerprinting techniques are often not available. We propose a novel approach using dynamic programming algorithms to compare antimicrobial susceptibility sequences and identify clusters of related microbes. Methods We selected pathogens from specific infections and tested them with a range of antimicrobials, recording the test results as either sensitive (S), resistant (R), or unknown (U) to each drug. These results were combined into a sequence, or ATB string, for each pathogen (Fig. 1). To compare the sequences, we used dynamic programming (Fig. 2) to calculate the minimum edit distance between each pair of sequences. The edit distance reflects the number of atomic changes needed to transform one sequence into the other. In this case, atomic changes include insertion, deletion, substitution, and matching of individual test results (Fig. 3). We created a matrix of pairwise similarities by comparing all ATB strings. A cluster of similar strains was identified based on a cutoff in the edit distance matrix, such as a distance of less than 1. The method, which is available in the SACIH+ system (https://plus.sacihweb.com), was used to analyze the same strain of specific healthcare infections from a public hospital in Belo Horizonte, Brazil, diagnosed between Jan 2022 and Mar 2023.Figure 1.Antimicrobial testing and ATB string generation for selected pathogens. Antimicrobial testing and ATB string generation for selected pathogens.Figure 2.Sequence comparison using dynamic programming. Sequence comparison using dynamic programming. Results We analyzed S. aureus strains from surgical site infection - SSI (23 cases), pneumonia - PNEU (29 cases), and bloodstream infection - BSI (13 cases). Identified 4 SSI, 10 PNEU clusters (Fig. 4 and 5), and 4 BSI clusters using edit distance matrix. For K. pneumoniae strains from 21 BSI, 22 PNEU, and 34 urinary tract infection - UTI cases, found 5 clusters for PNEU and UTI, and 7 clusters for BSI (Fig. 6).Figure 3.Minimum edit distance calculation for sequence comparison. Minimum edit distance calculation for sequence comparison.Figure 4.Clustering of Staphylococcus aureus strains from 29 nosocomial pneumonia cases. Clustering of Staphylococcus aureus strains from 29 nosocomial pneumonia cases.Figure 5.Identification of a large cluster with 16 identical Staphylococcus aureus strains from 29 nosocomial pneumonia cases.Identification of a large cluster with 16 identical Staphylococcus aureus strains from 29 nosocomial pneumonia cases. Conclusion Our novel approach using dynamic programming to compare antimicrobial susceptibility sequences has shown promise in identifying clusters of related microorganisms, even without traditional DNA fingerprinting methods. The method represents a valuable tool for tracking the spread of infectious diseases, especially in settings where traditional methods are not available.Figure 6.Clustering of Klebsiella pneumoniae strains from 21 bloodstream infection cases.Clustering of Klebsiella pneumoniae strains from 21 bloodstream infection cases. Disclosures All Authors: No reported disclosures

CodeGen-Search: A Code Generation Model Incorporating Similar Sample Information

Code generation has a positive significance in supporting software development, reducing labor intensity, and improving development efficiency. Some scholars use similar code information to enhance the quality of code generation. However, to improve the efficiency and accuracy of programming in daily development tasks, developers often search for similar samples as references. They get the code’s syntactic structure and semantic information from similar samples to assist in programming development. Inspired by this, we argue that similar samples are helpful for code generation. This paper proposes a CodeGen-Search model to improve code generation quality by incorporating similar samples. To fully utilize the information of similar samples, the model adopts the “pre-training [Formula: see text] fine-tuning” pattern. The model uses a minimum edit distance algorithm to find some similar samples with natural language (NL), and uses different encoders to extract the features of the NL and the code in similar samples. Experimental results show that our model efficiently improves the quality of the generated code. Compared to the state-of-the-art model, the CodeGen-Search model improves the BLEU by 1.5%, the Rough by 0.8% on the HS dataset, and the StrAcc by 0.5% on the ATIS dataset.

Forward-backward Transliteration of Punjabi Gurmukhi Script Using N-gram Language Model

Transliterating the text of a language to a foreign script is called forward transliteration and transliterating the text back to the original script is called backward transliteration. In this work, we perform both forward as well as backward transliteration on Punjabi. We transliterate Punjabi person names from Gurmukhi script to English Roman script and from English Roman script back to Gurmukhi script using n-gram language model. We used more than one million parallel entities of person names in Gurmukhi and Roman script as the training corpus. We generated English to Punjabi and Punjabi to English n-grams databases from the corpus. To get better results, we tried to create as long n-grams as possible ranging from bi-gram to 30-gram. Our n-grams database contains more than 10 million n-grams, with each n-gram having multiple mappings of the other script. The most challenging part is to find the mapping for the given n-gram from the parallel name entity while creating n-grams databases. As per the orthography rules, the same combination of letters may have different pronunciation, depending upon its location in the word. Therefore, we categorized n-grams into starting, middle, and ending n-grams and used them accordingly in the transliteration process. The transliteration process works like the merge sort. We start searching the longest possible n-gram in the database and split the string recursively until the match is found. The transliterated strings are merged back to form the final output. In English to Punjabi transliteration, we achieved 96% accuracy using gold standard and 99.14% accuracy using minimum edit distance. In Punjabi to English transliteration, the result showed 96.85% and 99.35% accuracy for the gold standard and minimum edit distance, respectively.

Contrastive study of minimum edit distance and cosine similarity measures in the context of word suggestions for misspelled Marathi words

Contrastive study of minimum edit distance and cosine similarity measures in the context of word suggestions for misspelled Marathi words

Detecting change in the routine of the elderly

Recent studies shown that elderly persons have a well-defined, regular life routine, organized around their postures, habits, environment, and social relations. Monitoring the daily routine will allow to detect person behavior changes. In this paper, we propose two solutions for detecting changes in the elderly person behavior based on the daily living activities monitoring. For both proposed methods, the person’s activity is recorded with a depth sensor to preserve his anonymity. In a first step, the recorded images are classified according to the posture of the person (sitting, standing, lying down, absent and fall) by a modified ResNet-18 model. The first method is a binary decision problem and consists in classifying routine and non routine day according to the postures. Then a behavior index is built using the output of the classifier. The second method consists in describing the routine day by the temporal succession in postures. A similarity between the recorded day and the routine day postures is computed using the minimum edit distance which acts as a behavior index. The number of transformations reflects the change in the person’s behavior. Both methods have been tested on a database of depth images recorded in a nursing home over a 85 days period. Inferring the whole dataset gives an accuracy of 89.09% and a F1-score of 85.84% for posture estimation. Using Random Forest, an accuracy of 92% was retrieved for routine/non routine day classification. The two proposed indexes proved their reliability in monitoring behavior changes. The first approach proposes a behavioral index with a median magnitude error less that 0.01 compared to the ground truth. The second one provides a significant difference in the number of transformations between the postures in routine days (Interquartile range 25th-75th=[28, 48]) and non routine days (Interquartile range 25th-75th=[55, 62]).

Visualization of Speech Perception Analysis via Phoneme Alignment: A Pilot Study.

Objective: Speech tests assess the ability of people with hearing loss to comprehend speech with a hearing aid or cochlear implant. The tests are usually at the word or sentence level. However, few tests analyze errors at the phoneme level. So, there is a need for an automated program to visualize in real time the accuracy of phonemes in these tests.Method: The program reads in stimulus-response pairs and obtains their phonemic representations from an open-source digital pronouncing dictionary. The stimulus phonemes are aligned with the response phonemes via a modification of the Levenshtein Minimum Edit Distance algorithm. Alignment is achieved via dynamic programming with modified costs based on phonological features for insertion, deletions and substitutions. The accuracy for each phoneme is based on the F1-score. Accuracy is visualized with respect to place and manner (consonants) or height (vowels). Confusion matrices for the phonemes are used in an information transfer analysis of ten phonological features. A histogram of the information transfer for the features over a frequency-like range is presented as a phonemegram.Results: The program was applied to two datasets. One consisted of test data at the sentence and word levels. Stimulus-response sentence pairs from six volunteers with different degrees of hearing loss and modes of amplification were analyzed. Four volunteers listened to sentences from a mobile auditory training app while two listened to sentences from a clinical speech test. Stimulus-response word pairs from three lists were also analyzed. The other dataset consisted of published stimulus-response pairs from experiments of 31 participants with cochlear implants listening to 400 Basic English Lexicon sentences via different talkers at four different SNR levels. In all cases, visualization was obtained in real time. Analysis of 12,400 actual and random pairs showed that the program was robust to the nature of the pairs.Conclusion: It is possible to automate the alignment of phonemes extracted from stimulus-response pairs from speech tests in real time. The alignment then makes it possible to visualize the accuracy of responses via phonological features in two ways. Such visualization of phoneme alignment and accuracy could aid clinicians and scientists.

Bargaining power in informal trilogues: Intra-institutional preference cohesion and inter-institutional bargaining success

Informal trilogue meetings are the main legislative bargaining forum in the European Union, yet their dynamics remain largely understudied in a quantitative context. This article builds on the assumption that the negotiating delegations of the European Parliament and the Council play a two-level game whereby these actors can use their intra-institutional constraint to extract inter-institutional bargaining success. Negotiators can credibly claim that their hands are tied if the members of their parent institutions hold similar preferences and do not accept alternative proposals or if their institution is divided and negotiators need to defend a fragile compromise. Employing a measure of document similarity (minimum edit distance) between an institution's negotiation mandate and the trilogue outcome to measure bargaining success, the analysis supports the hypothesis for the European Parliament, but not for the Council.

Indonesian Spelling Error Detection and Type Identification Using Bigram Vector and Minimum Edit Distance Based Probabilities

Spelling error has become an error that is often found in this era which can be seen from the use of words that tend to follow trends or culture, especially in the younger generation. This study aims to develop and test a detection and identification model using a combination of Bigram Vector and Minimum Edit Distance Based Probabilities. Correct words from error words are obtained using candidates search and probability calculations that adopt the concept of minimum edit distance. The detection results then identified the error type into three types of errors, namely vowels, consonants and diphthongs from the error side on the tendency of the characters used as a result of phonemic rendering at the time of writing. The results of error detection and identification of error types obtained are quite good where most of the error test data can be detected and identified according to the type of error, although there are several detection errors by obtaining more than one correct word as a result of the same probability value of these words.

Reconstruction of a Symbolic Periodic Sequence from a Sequence with Noise

The problem of constructing a periodic sequence consisting of at least eight periods is considered, based on a given sequence obtained from an unknown periodic sequence, also containing at least eight periods, by introducing noise of deletion, replacement, and insertion of symbols. To construct a periodic sequence that approximates a given one, distorted by noise, it is first required to estimate the length of the repeating fragment (period). Further, the distorted original sequence is divided into successive sections of equal length; the length takes on integer values from 80 to 120 % of the period estimate. Each obtained section is compared with each of the remaining sections, a section is selected to build a periodic sequence that has the minimum edit distance (Levenshtein distance) to any of the remaining sections, minimization is carried out over all sections of a fixed length, and then along all lengths from 80 to 120 % of period estimates. For correct comparison of fragments of different lengths, we consider the ration between the edit distance and the length of the fragment. The length of a fragment that minimizes the ratio of the edit distance to another fragment of the same length to the fragment length is considered the period of the approximating periodic sequence, and the fragment itself, repeating the required number of times, forms an approximating sequence. The constructed sequence may contain an incomplete repeating fragment at the end. The quality of the approximation is estimated by the ratio of the edit distance from the original distorted sequence to the constructed periodic sequence of the same length and this length.

Pheniqs 2.0: accurate, high-performance Bayesian decoding and confidence estimation for combinatorial barcode indexing

BackgroundSystems biology increasingly relies on deep sequencing with combinatorial index tags to associate biological sequences with their sample, cell, or molecule of origin. Accurate data interpretation depends on the ability to classify sequences based on correct decoding of these combinatorial barcodes. The probability of correct decoding is influenced by both sequence quality and the number and arrangement of barcodes. The rising complexity of experimental designs calls for a probability model that accounts for both sequencing errors and random noise, generalizes to multiple combinatorial tags, and can handle any barcoding scheme. The needs for reproducibility and community benchmark standards demand a peer-reviewed tool that preserves decoding quality scores and provides tunable control over classification confidence that balances precision and recall. Moreover, continuous improvements in sequencing throughput require a fast, parallelized and scalable implementation.Results and discussionWe developed a flexible, robustly engineered software that performs probabilistic decoding and supports arbitrarily complex barcoding designs. Pheniqs computes the full posterior decoding error probability of observed barcodes by consulting basecalling quality scores and prior distributions, and reports sequences and confidence scores in Sequence Alignment/Map (SAM) fields. The product of posteriors for multiple independent barcodes provides an overall confidence score for each read. Pheniqs achieves greater accuracy than minimum edit distance or simple maximum likelihood estimation, and it scales linearly with core count to enable the classification of > 11 billion reads in 1 h 15 m using < 50 megabytes of memory. Pheniqs has been in production use for seven years in our genomics core facility.ConclusionWe introduce a computationally efficient software that implements both probabilistic and minimum distance decoders and show that decoding barcodes using posterior probabilities is more accurate than available methods. Pheniqs allows fine-tuning of decoding sensitivity using intuitive confidence thresholds and is extensible with alternative decoders and new error models. Any arbitrary arrangement of barcodes is easily configured, enabling computation of combinatorial confidence scores for any barcoding strategy. An optimized multithreaded implementation assures that Pheniqs is faster and scales better with complex barcode sets than existing tools. Support for POSIX streams and multiple sequencing formats enables easy integration with automated analysis pipelines.

A compressed string matching algorithm for face recognition with partial occlusion

There has been less attention towards the research on face recognition with partial occlusion. Facial accessories such as masks, sunglasses, and caps, etc., cause partial occlusion which results in a significant performance drop of the face recognition system. In this paper, a novel compressed string matching algorithm based on run-length encoding (CSM-RL) is proposed to solve the partial occlusion problem. In this, the face image is represented by a string sequence that is then compressed using run-length encoding. The proposed CSM-RL algorithm performs string matching between query face and gallery face string sequences by computing the edit distance between string sequences, finally, classifies query face based on the minimum edit distance. The proposed method does not require a classifier and has less time complexity, thus it is more suitable for real-world face recognition applications. The proposed method performs better than the state-of-the-art methods even limited sample images per person are available in the gallery. Extensive experimental results on benchmark face datasets such as AR and Extended Yale-B prove that the proposed algorithm exhibits significant performance improvement both in terms of speed and recognition accuracy for the recognition of partially occluded faces.

Explaining Propagators for String Edit Distance Constraints

The computation of string similarity measures has been thoroughly studied in the scientific literature and has applications in a wide variety of different areas. One of the most widely used measures is the so called string edit distance which captures the number of required edit operations to transform a string into another given string. Although polynomial time algorithms are known for calculating the edit distance between two strings, there also exist NP-hard problems from practical applications like scheduling or computational biology that constrain the minimum edit distance between arrays of decision variables. In this work, we propose a novel global constraint to formulate restrictions on the minimum edit distance for such problems. Furthermore, we describe a propagation algorithm and investigate an explanation strategy for an edit distance constraint propagator that can be incorporated into state of the art lazy clause generation solvers. Experimental results show that the proposed propagator is able to significantly improve the performance of existing exact methods regarding solution quality and computation speed for benchmark problems from the literature.

PyGTED: Python Application for Computing Graph Traversal Edit Distance.

Graph Traversal Edit Distance (GTED) is a measure of distance (or dissimilarity) between two graphs introduced. This measure is based on the minimum edit distance between two strings formed by the edge labels of respective Eulerian traversals of the two graphs. GTED was motivated by and provides the first mathematical formalism for sequence coassembly and de novo variation detection in bioinformatics. Many problems in applied machine learning deal with graphs (also called networks), including social networks, security, web data mining, protein function prediction, and genome informatics. The kernel paradigm beautifully decouples the learning algorithm from the underlying geometric space, which renders graph kernels important for the aforementioned applications. In this article, we introduce a tool, PyGTED to compute GTED. It implements the algorithm based on the polynomial time algorithm devised for it by the authors. Informally, the GTED is the minimum edit distance between two strings formed by the edge labels of respective Eulerian traversals of the two graphs.

Graph Traversal Edit Distance and Extensions.

Many problems in applied machine learning deal with graphs (also called networks), including social networks, security, web data mining, protein function prediction, and genome informatics. The kernel paradigm beautifully decouples the learning algorithm from the underlying geometric space, which renders graph kernels important for the aforementioned applications. In this article, we give a new graph kernel, which we call graph traversal edit distance (GTED). We introduce the GTED problem and give the first polynomial time algorithm for it. Informally, the GTED is the minimum edit distance between two strings formed by the edge labels of respective Eulerian traversals of the two graphs. Also, GTED is motivated by and provides the first mathematical formalism for sequence co-assembly and de novo variation detection in bioinformatics. We demonstrate that GTED admits a polynomial time algorithm using a linear program in the graph product space that is guaranteed to yield an integer solution. To the best of our knowledge, this is the first approach to this problem. We also give a linear programming relaxation algorithm for a lower bound on GTED. We use GTED as a graph kernel and evaluate it by computing the accuracy of a support vector machine (SVM) classifier on a few data sets in the literature. Our results suggest that our kernel outperforms many of the common graph kernels in the tested data sets. As a second set of experiments, we successfully cluster viral genomes using GTED on their assembly graphs obtained from de novo assembly of next-generation sequencing reads.

Application of a Genetic Algorithm for Finding Edit Distances between Process Models

Finding graph edit distances (determining the similarity of graph models) is an important task in various areas of computer science, such as image analysis, machine learning, and chemoinformatics. In recent years, due to the development of process mining techniques, it has become necessary to adapt the existing graph matching methods to be applied to the analysis of process models (annotated graphs) discovered from event logs of information systems. In particular, methods for finding the minimum graph edit distance can be used to reveal patterns (subprocesses) and to compare discovered process models. As was shown experimentally and theoretically substantiated, exact methods for finding the minimum edit distance between the discovered process models (and graphs in the general case) have a great time complexity and can be applied only to small-sized process models. In this paper, we estimate the accuracy and time performance characteristics of a genetic algorithm applied to find distances between process models discovered from the event logs. In particular, we find distances between BPMN (Business Process Model and Notation) models discovered from the event logs by using different synthesis algorithms. It is shown that the genetic algorithm proposed in the paper allows us to significantly reduce the computation time and produces results close to the optimal solutions (the minimum edit distances).

Improved dynamic match phone lattice search for Persian spoken term detection system in online and offline applications

Spoken term detection (STD) refers to discovering all occurrences of a given term in a set of speech utterances. One of the well-known approaches for the STD system is the phone lattice search (PLS) that produces a phone-based lattice of speech utterances. Since the accuracy of a phone recognizer affects the accuracy of the STD system, the PLS approach utilizes the minimum edit distance (MED) measure to compensate the phone recognizer errors. While this measure increases the detection rate, it also raises the false alarm rate. In this paper, we consider the PLS approach as the baseline. Then, we use Viterbi scoring and Jaro-Winkler similarity measure in order to decrease the false alarm rate. Since the proposed approach uses more techniques than the baseline approach, the search speed may decrease. To overcome this problem, we use lattice pruning and indexing techniques such as depth first search algorithm to increase the search speed in online and offline applications, respectively. We report the experimental results for monophone-based and triphone-based STD system. The results indicate that using triphone-based STD system improved the performance about 2% in comparison with monophone-based STD system. Moreover, when we used triphone-based models, the proposed approach including MED measure, Viterbi scores and Jaro-Winkler similarity measure improved the accuracy of the method with only MED measure, about 17%.

Detecting and correcting real-word errors in Tamil sentences

Spell checkers concern two types of errors namely non-word errors and real-word errors. Non-word errors can be of two categories: First one is that the word itself is invalid; the other is that the word is valid but not present in a valid lexicon. Real-word error means the word is valid but inappropriate in the context of the sentence. An approach to correcting real-word errors in Tamil language is proposed in this paper. A bigram probability model is constructed to determine appropriateness of the valid word in the context of the sentence using a 3GB volume of corpora of Tamil text. In case of lacking appropriateness, the word is marked as a real-word error and minimum edit distance technique is used to find lexically similar words, and the appropriateness of such words is measured by a word-level n-gram language probability model. A hash table with word-length as the key is used to speed up the search for words to check for the lexical similarity. Words of lengths of m-1 to m+1 are considered with m being the length of the word found to be ‘inappropriate’. Test results show that the suggestions generated by the system are with more than 98% accuracy as approved by a Scholar in Tamil.

Top-down tree edit-distance of regular tree languages

We study the edit-distance of regular tree languages. The edit-distance is a useful metric for measuring the similarity or dissimilarity between two objects. A regular tree language is a set of trees accepted by a finite-state tree automaton or described by a regular tree grammar. Given two regular tree languages L and R, we define the edit-distance d(L, R) between L and R to be the minimum edit-distance between a tree in L and a tree in R. Given tree automata for L and R, we design a polynomial time algorithm that computes d(L, R). We also present an efficient algorithm that identifies a special common string between two context-free grammars using the edit-distance between two tree languages.

A New Urban Typology Model Adapting Data Mining Analytics to Examine Dominant Trajectories of Neighborhood Change: A Case of Metro Detroit

This article develops an integrated methodology to investigate dominant trajectories of neighborhood change that are often confronted in urban studies. Currently, researchers are using k-means cluster analysis to establish diverse neighborhood typologies and principal component analysis (PCA) to identify socioeconomic interactions explaining the neighborhood typologies. Little attention has been given to longitudinal trajectories and dynamics of neighborhood evolution over a long period. Our new model adapts a newly developed dynamic sequential analysis (the weighted minimum edit distance algorithm) in big data analytics to sort and identify dominant trajectories of neighborhood change. Our model also innovatively synthesizes three statistical procedures—k-means, PCA, and analysis of variance—to derive the weight matrix, which naturally integrates the core characteristics of urban neighborhood changes into the sequential reordering. Using the census data in Metro Detroit over five census years (1970, 1980, 1990, 2000, and 2010), this model was tested to identify a unique city's demographic and socioeconomic transition pattern in the past forty years. This model successfully provided a thorough analysis of the neighborhood typologies and exhibited a much-enhanced performance in identifying long-term trajectories of urban evolution.