Infrequent Patterns Research Articles

Joint Imbalance Adaptation for Radiology Report Generation.

Radiology report generation, translating radiological images into precise and clinically relevant description, may face the data imbalance challenge - medical tokens appear less frequently than regular tokens; and normal entries are significantly more than abnormal ones. However, very few studies consider the imbalance issues, not even with conjugate imbalance factors. In this study, we propose a Joint Imbalance Adaptation (JIMA) model to promote task robustness by leveraging token and label imbalance. JIMA predicts entity distributions from images and generates reports based on these distributions and image features. We employ a hard-to-easy learning strategy that mitigates overfitting to frequent labels and tokens, thereby encouraging the model to focus more on rare labels and clinical tokens. JIMA shows notable improvements (16.75% - 50.50% on average) across evaluation metrics on IU X-ray and MIMIC-CXR datasets. Our ablation analysis proves that JIMA's enhanced handling of infrequent tokens and abnormal labels counts the major contribution. Human evaluation and case study experiments further validate that JIMA can generate more clinically accurate reports. Data imbalance (e.g., infrequent tokens and abnormal labels) leads to the underperformance of radiology report generation. Our curriculum learning strategy successfully reduce data imbalance impacts by reducing overfitting on frequent patterns and underfitting on infrequent patterns. While data imbalance remains challenging, our approach opens new directions for the generation task.

Unveiling Rare Patterns: Anomaly Detection in CCTV Footage for Safeguarding Home Premises

Abstract Protecting residential properties through CCTV surveillance has become a crucial aspect of contemporary living. In the age of data-driven security, detecting unusual and infrequent patterns within CCTV footage is of significant importance. Conventional anomaly detection methods often need help to handle the intricacies and distinctiveness of home environments, where family members and potential intruders co-exist. To address these challenges, this research incorporates extensive datasets, various machine-learning techniques, and thorough evaluation measures to pinpoint rare patterns that might signal threats to residential premises. These findings provide valuable insights for homeowners, security professionals, and developers working on surveillance systems.

Infrequent patterns in cerebrospinal fluid isofocusing test: Clinical significance and contribution of IgG index and Reiber diagram to their interpretation

Cerebrospinal fluid (CSF) isoelectrofocusing (IEF) is considered as the gold standard for detecting an intrathecal synthesis of IgG, which is a hallmark of multiple sclerosis (MS). This corresponds to the presence of CSF-restricted IgG oligoclonal bands (OCB) (typically type 2 pattern). Moreover, this technique can also detect a systemic immune reaction with passive transfer of IgG (type 3 and 4 patterns) for which the clinical relevance is less understood. The aim of our study was to determine the frequency and disease associations of IEF type 3 and 4 patterns and to investigate the potential usefulness of including quantitative data (IgG index and Reiber Diagram) in interpreting such IEF profiles.Among 544 patients who underwent CSF IEF (Hydragel CSF isofocusing kit, Sebia®, France) in our Laboratory during a six-year-period, those who presented type 3 or 4 patterns were selected. Clinical data and results of other immunological tests were analyzed for 27 patients followed in the Neurological Department.Frequencies of type 3 and type 4 patterns were relatively low (2.3 % and 3.4 % respectively). Among patients with type 3 pattern included in our study (n = 10), 5 were diagnosed with MS. For the 5 other patients, the diagnosis was a clinically isolated syndrome (CIS) (2 cases), a probable auto-immune encephalitis (2 cases) and a possible genetic neurodegenerative disease (1 case). MS patients had an IgG index >0.7 and fell into area 4 of Reiber diagram while non-MS patients had an IgG index <0.7 and fell into area 1, except the last case. Regarding type 4 pattern (n = 17), the diagnoses were as follows: MS (3), CIS (4), Neuromyelitis optica spectrum disorders with positive anti-AQP4 antibodies (3) and anti-NMDAR autoimmune encephalitis (1). The remaining cases had central nervous system impairment related to vascular, metabolic or tumoral etiologies (3) or peripheral nervous system impairment (3). In this group (type 4 pattern), IgG index was <0.7 in 15/17 cases. Interpretation using Reiber diagram showed an abnormal blood-brain barrier for 8/17 patients.Type 3 and 4 IEF patterns are infrequently observed in routine neurology practice. It is important for the diagnostic laboratory professional as well as for the neurologist to understand their clinical relevance. Our findings highlight the contribution of quantitative evaluation of CSF (IgG index, Reiber diagram) for the interpretation of such situations. Despite the small size of our study population, our results emphasize the importance of reporting the exact type of IEF pattern and not only the positivity or not of OCB.

Constraints on Acceleration in Bilingual Development: Evidence from Word Segmentation by Spanish Learning Infants.

We have previously shown that bilingual Spanish and English-learning infants can segment English iambs, two-syllable words with final stress (e.g., guiTAR), earlier than their monolingual peers. This is consistent with accelerated development in bilinguals and was attributed to bilingual infants' increased exposure to iambs through Spanish; about 10% of English content words start with an unstressed syllable, compared to 40% in Spanish. Here, we evaluated whether increased exposure to a stress pattern alone is sufficient to account for acceleration in bilingual infants. In English, 90% of content words start with a stressed syllable (e.g., KINGdom), compared to 60% in Spanish. However, we found no evidence for accelerated segmentation of Spanish trochees by Spanish-English bilingual infants compared to their monolingual Spanish-learning peers. Based on this finding, we argue that merely increased exposure to a linguistic feature in one language does not result in accelerated development in the other. Instead, only the acquisition of infrequent patterns in one language may be accelerated due to the additive effects of the other language.

Emerging Scientific Topic Discovery by Analyzing Reliable Patterns of Infrequent Synonymous Biterms

Emerging scientific topics are those topics that the number of related articles was small in the past but has grown dramatically in recent years. Automatic discovery of emerging scientific topics has become increasingly necessary because of the exponentially increasing of research papers. Such discovery enables broad applications, such as optimizing resource allocations for promising research areas, predicting future technology trends, finding knowledge gaps and new concepts, and recommending personalized research directions. In this paper, we provide a framework of emerging topic discovery methods using Infrequent Synonymous Biterm (ISB), which automatically extracts the dedicated knowledge from the infrequent patterns of synonymous biterms in a corpus (e.g., paper titles); each term in a synonymous biterm represents a collaborating supertopic, whose collaboration originates an emerging topic. In particular, we propose an Analyzing Reliable Patterns of Infrequent Synonymous Biterms (ARPISB) method, which guarantees the quality of the result emerging topics by adaptively giving larger weights to more reliable ISB. Extensive experiments on five subfields' scholarly papers demonstrate the significant and robust improvement of the accuracy of emerging scientific topic discovery.

Efficient weighted sequential pattern mining

In real-life applications, data mining task involves extracting valuable but hidden information from massive data. How to effectively find out interesting patterns from large databases is a current topic. Sequential pattern mining is the most popular approach in data mining domain. Traditional sequential pattern mining research generally focuses on discovering frequent sequential patterns. However, the account of occurrence times of patterns does not adequately indicate their importance. For instance, frequent patterns (e.g., pencil and eraser) are not profitable, whereas infrequent patterns (e.g., extreme weather) are high-risk. To extract more useful information, researchers study a weighted sequential pattern mining task. In this paper, an efficient algorithm for weighted sequential pattern mining task, called EWSPM, is proposed. Two new strict upper bounds, namely MWEbound and MSRIWbound, are designed based on the concepts of maximum weight estimation (simplified as MWE) and maximum sumation of remaining item weights (simplified as MSRIW), respectively. These upper bounds achieve better pruning effects and reduce the size of search space during the mining process, which significantly shortens execution time. In addition, a database-projection method is employed to optimize memory usage. It addresses potential memory explosion issues in a certain degree. Finally, we also conducted extensive experiments on nine datasets (including real and synthetic). The experimental results demonstrate that the EWSPM algorithm is capable of mining all interesting patterns efficiently, with the smallest size of search space. Additionally, the novel algorithm also exhibits superior performance in terms of execution time and memory consumption.

A new tree-based approach to mine sequential patterns

Generic sequential pattern mining problem aims to mine the set of sequential patterns from a sequential database that satisfies a minimum support or occurrence threshold constraint. The main challenges that affect the efficiency of a solution lie in reducing the pattern search space, early detecting the infrequent patterns, representing the database in an efficient format, etc. Also, additional challenges get included when the problem environment transitions from static to incremental database leading to not to re-mine but efficiently tracking the effect of the incremental portion over the complete updated database. In this article, we introduce a new tree-based solution to the sequential pattern mining problem, including two sets of novel solutions for static and incremental sequential databases. We propose two new structures, SP-Tree and IncSP-Tree, and design two efficient algorithms, Tree-Miner and IncTree-Miner to mine the complete set of sequential patterns from static and incremental databases respectively. The proposed novel structures provide an efficient manner to store the complete sequential database maintaining “build-once-mine-many” property and giving scope to perform interactive mining. Additionally, we also design a new breath-first based support counting technique to efficiently identify the infrequent patterns at early stages and a new heuristic pruning strategy to reduce pattern search space. We also design a new pattern storage structure BPFSP-Tree to store the frequent patterns during successive iterations in incremental mining to reduce the number of database scans and to remove the infrequent patterns efficiently. A novel structure named Sequence Summarizer is also introduced to efficiently calculate and update the co-occurrence information of the items, especially in an incremental environment. Experimental results from various real-life and synthetic datasets demonstrate the efficiency of our work in comparison with the related state-of-the-art approaches.

A - 183 Novel Performance Validity Indicators for Frequently Used Memory Tests Using Infrequent Patterns of Memory Performance.

Determining performance validity is an imperative part of neuropsychological assessment in concussion management. Several existing embedded performance validity tests (PVTs) have been developed for popular measures of memory, Hopkins Verbal Learning Test Revised (HVLT-R) and Brief Visuospatial Memory Test Revised (BVMT-R). However, many PVTs utilize cognitive performance measures to classify performance invalidity, which increases the likelihood of false positives, especially in clinical populations. To reduce this possibility, a novel embedded PVT was developed utilizing infrequent patterns of performance in a primarily amnestic, dementia population. Neuropsychological data for 50 adult outpatients (Mean Age = 73, Range = 50-97) who met criteria for Major Neurocognitive Disorder (MND) were reviewed. Infrequent patterns of performance (IPOP) were identified on the HVLT-R and BVMT-R, including inconsistency between recall and recognition, unusually high intrusions, exceedingly poor learning, and lack of primary effect. Scaled scores were developed from the IPOP scores (0 = 0-74%, 1 = 75-94%, 2 = 95-99%). Total IPOP scaled scores were compared to existing embedded PVTs, which utilized recognition discrimination and percent retention scores, to examine classification rates. Failure rates on the embedded PVTs for the HVLT-R were 16-42% and BVMT-R 38-58% whereas elevated IPOP scaled scores were identified in less than 15% of the sample. High frequencies of embedded PVT failures were found in a clinical sample of patients with MND, which is concerning for false positive misclassification. Infrequent patterns of memory performances identified in a dementia population were less frequent and could potentially be utilized to examine performance validity in persons suspected of producing invalid memory performances.

A - 184 Validity of the Infrequent Patterns of Performance Embedded Performance Validity Indicators in a Mixed Clinical Sample.

Novel performance validity tests (PVTs) for the Hopkins Verbal Learning Test-Revised (HVLT-R) and Brief Visuospatial Memory Test-Revised (BVMT-R), derived from infrequent patterns of performance (IPOP), were developed to examine invalid memory performance while reducing false positive misclassification. To examine their classification accuracy, these indicators were compared to several existing embedded PVTs in a mixed clinical sample. A chart review was completed for 75 adult outpatients (Mean Age = 52, Range = 19-85) referred to an academic medical center outpatient clinic for neuropsychological evaluation. Twenty-eight percent of the sample failed the Medical Symptom Validity Test (MSVT), which was used as the criterion to indicate invalid performance. Several established embedded validity indicators were calculated from HVLT-R and BVMT-R scores, including the recognition discrimination index (RDI) and percent retention (PR), as well as the Reliable Digit Span (RDS) validity indicator. Classification statistics, including sensitivity, specific, likelihood ratios, predictive values, and overall accuracy, were calculated and compared. The HVLT-R IPOP had a higher overall classification accuracy (72%) compared to the RDI indicator (68%) while maintaining excellent specificity (97%). The BVMT-R IPOP had identical classification accuracy (75%) compared to the RDI and PR indicators with very high specificity (96%). Both indicators were more accurate than RDS (72%). The HVLT-R and BVMT-R IPOP performance validity indicators produced adequate classification statistics when compared to existing embedded PVTs. Both indicators maintained excellent specificity, which is crucial in reducing false positive classification. Future research will focus on further examining the validity of these indicators in other clinical groups and known groups, including medico-legal populations.

“It doesn’t make any sense to even try”: the disruptive impact of COVID-19’s first wave on people with chronic pain using medical cannabis in New York

BackgroundThe COVID-19 pandemic disrupted health care but it is unknown how it impacted the lives of people using medical cannabis for chronic pain.ObjectiveTo understand the experiences of individuals from the Bronx, NY, who had chronic pain and were certified to use medical cannabis during the first wave of the COVID-19 pandemic.MethodsWe conducted 1:1 semi-structured qualitative telephone interviews from March through May 2020 with a convenience sample of 14 individuals enrolled in a longitudinal cohort study. We purposively recruited participants with both frequent and infrequent patterns of cannabis use. Interviews addressed the impact of the COVID-19 pandemic on daily life, symptoms, medical cannabis purchase, and use. We conducted a thematic analysis, with a codebook approach, to identify and describe prominent themes.ResultsParticipants’ median age was 49 years, nine were female, four were Hispanic, four were non-Hispanic White, and four were non-Hispanic Black. We identified three themes: (1) disrupted access to health services, (2) disrupted access to medical cannabis due to the pandemic, and (3) mixed impact of chronic pain on social isolation and mental health. Due to increased barriers to health care in general and to medical cannabis specifically, participants reduced medical cannabis use, stopped use, or substituted medical cannabis with unregulated cannabis. Living with chronic pain both prepared participants for the pandemic and made the pandemic more difficult.ConclusionThe COVID-19 pandemic amplified pre-existing challenges and barriers to care, including to medical cannabis, among people with chronic pain. Understanding pandemic-era barriers may inform policies in ongoing and future public health emergencies.

Mining Weighted Sequential Patterns Based on Prefix-Tree and Prism Encoding

The purpose of mining sequential patterns problem with weighted constraints is to find high-valued patterns, including infrequent patterns but having items which appear in the pattern of high importance in the sequence database (SD). Therefore, weighted sequential pattern mining will collect a set of more complete patterns with items of low support but of high importance. This paper proposes a new algorithm called WSPM_PreTree to find highly weighted sequential patterns. To collect a set of complete sequential patterns with the stricter weighted constraints of sequential patterns, the proposed algorithm uses both the minimum support constraint and the actual values of items appearing in the SD. To increase the performance of the finding weighted sequential patterns process, the algorithm uses the parent–child relationship on the prefix tree structure to create candidates and combines the weighted mean of the sequential 1-patterns that is calculated from the actual value of items in the SD as conditions to find the weighted sequential patterns. Experimental results show that the proposed algorithm is more efficient than sequential patterns mining with weight constraint (SPMW) algorithm [Ref. 20 ] in the runtime.

VEPRECO: Vertical databases with pre-pruning strategies and common candidate selection policies to fasten sequential pattern mining

Sequential pattern mining (SPM) discovers, from event transactions recorded along time, patterns of events fulfilling a sequential order. In this work, we introduce a new efficient sequential pattern mining algorithm called VEPRECO. VEPRECO proposes three main contributions that fasten the mining process: a vertical representation of patterns, pre-pruning strategies to avoid checking infrequent patterns, and common candidate selection policies that reduce the number of iterations performed by the algorithm.An experimental evaluation was performed with synthetic and real-world datasets, and the results have been compared with the most time and memory-efficient sequential pattern mining algorithm in the literature, the CM-SPAM algorithm, which we have taken as a baseline. We analysed separately how each of the proposed contributions affects time and memory usage and found that the one that reduced the most time and memory was the representation of the proposed patterns. Pre-pruning strategies and common candidate selection policies reduce runtime in datasets with many sequences and similar lengths of transactions and sequences.

The Brain Tracks Multiple Predictions About the Auditory Scene.

The predictable rhythmic structure is important to most ecologically relevant sounds for humans, such as is found in the rhythm of speech or music. This study addressed the question of how rhythmic predictions are maintained in the auditory system when there are multiple perceptual interpretations occurring simultaneously and emanating from the same sound source. We recorded the electroencephalogram (EEG) while presenting participants with a tone sequence that had two different tone feature patterns, one based on the sequential rhythmic variation in tone duration and the other on sequential rhythmic variation in tone intensity. Participants were presented with the same sound sequences and were instructed to listen for the intensity pattern (ignore fluctuations in duration) and press a response key to detected pattern deviants (attend intensity pattern task); to listen to the duration pattern (ignore fluctuations in intensity) and make a button press to duration pattern deviants (attend duration pattern task), and to watch a movie and ignore the sounds presented to their ears (attend visual task). Both intensity and duration patterns occurred predictably 85% of the time, thus the key question involved evaluating how the brain treated the irrelevant feature patterns (standards and deviants) while performing an auditory or visual task. We expected that task-based feature patterns would have a more robust brain response to attended standards and deviants than the unattended feature patterns. Instead, we found that the neural entrainment to the rhythm of the standard attended patterns had similar power to the standard of the unattended feature patterns. In addition, the infrequent pattern deviants elicited the event-related brain potential called the mismatch negativity component (MMN). The MMN elicited by task-based feature pattern deviants had a similar amplitude to MMNs elicited by unattended pattern deviants that were unattended because they were not the target pattern or because the participant ignored the sounds and watched a movie. Thus, these results demonstrate that the brain tracks multiple predictions about the complexities in sound streams and can automatically track and detect deviations with respect to these predictions. This capability would be useful for switching attention rapidly among multiple objects in a busy auditory scene.

An Optimization Approach for Mining of Process Models with Infrequent Behaviors Integrating Data Flow and Control Flow

Infrequent behaviors of business process refer to behaviors that occur in very exceptional cases, and their occurrence frequency is low as their required conditions are rarely fulfilled. Hence, a strong coupling relationship between infrequent behavior and data flow exists. Furthermore, some infrequent behaviors may reveal very important information about the process. Thus, not all infrequent behaviors should be disregarded as noise, and identifying infrequent but correct behaviors in the event log is vital to process mining from the perspective of data flow. Existing process mining approaches construct a process model from frequent behaviors in the event log, mostly concentrating on control flow only, without considering infrequent behavior and data flow information. In this paper, we focus on data flow to extract infrequent but correct behaviors from logs. For an infrequent trace, frequent patterns and interactive behavior profiles are combined to find out which part of the behavior in the trace occurs in low frequency. And, conditional dependency probability is used to analyze the influence strength of the data flow information on infrequent behavior. An approach for identifying effective infrequent behaviors based on the frequent pattern under data awareness is proposed correspondingly. Subsequently, an optimization approach for mining of process models with infrequent behaviors integrating data flow and control flow is also presented. The experiments on synthetic and real-life event logs show that the proposed approach can distinguish effective infrequent behaviors from noise compared with others. The proposed approaches greatly improve the fitness of the mined process model without significantly decreasing its precision.

Infrequent Pattern Detection for Reliable Network Traffic Analysis Using Robust Evolutionary Computation.

While anomaly detection is very important in many domains, such as in cybersecurity, there are many rare anomalies or infrequent patterns in cybersecurity datasets. Detection of infrequent patterns is computationally expensive. Cybersecurity datasets consist of many features, mostly irrelevant, resulting in lower classification performance by machine learning algorithms. Hence, a feature selection (FS) approach, i.e., selecting relevant features only, is an essential preprocessing step in cybersecurity data analysis. Despite many FS approaches proposed in the literature, cooperative co-evolution (CC)-based FS approaches can be more suitable for cybersecurity data preprocessing considering the Big Data scenario. Accordingly, in this paper, we have applied our previously proposed CC-based FS with random feature grouping (CCFSRFG) to a benchmark cybersecurity dataset as the preprocessing step. The dataset with original features and the dataset with a reduced number of features were used for infrequent pattern detection. Experimental analysis was performed and evaluated using 10 unsupervised anomaly detection techniques. Therefore, the proposed infrequent pattern detection is termed Unsupervised Infrequent Pattern Detection (UIPD). Then, we compared the experimental results with and without FS in terms of true positive rate (TPR). Experimental analysis indicates that the highest rate of TPR improvement was by cluster-based local outlier factor (CBLOF) of the backdoor infrequent pattern detection, and it was 385.91% when using FS. Furthermore, the highest overall infrequent pattern detection TPR was improved by 61.47% for all infrequent patterns using clustering-based multivariate Gaussian outlier score (CMGOS) with FS.

WITHDRAWN: Mining of negative association rules using correlate frequent pattern tree

Associative rule mining is the discovery of rules from the item set constraint that contain a set of specific items in antecedent rule and a class label. Frequent Pattern (FP-Tree) is associative rule mining method that doesn’t provide large set of candidate sets. Recently, the researchers are focusing on finding the infrequent item set leads to Negative Association Rule (NAR) mining. In this research, (FP-Tree) is added with the linkage table called Correlated Frequent Pattern Tree (CFPT) that allows the method to analyze the patterns with frequent items. The Coverage Co-efficient Measure (CCM) is applied in the FP-Tree with linkage table to identify the correlated item sets. In the mining process, CCM helps to remove irrelevant item set from the transaction. The developed IFP-Tree reduces the memory usage and the execution time in mining the frequent pattern. The four datasets have used to experiment the performance of proposed CFPT technique. The execution time of proposed CFPT method is the 0.043 s in frequent pattern mining and 0.075 s in infrequent pattern mining.

CRS-iEclat: Implementation of Critical Relative Support in iEclat Model for Rare Pattern Mining

The research purpose is to develop a performance enhancement in Incremental Eclat (iEclat) model by embedding Critical Relative Support (CRS) in mining of infrequent itemset. The CRS measure acts as an interestingness measure (filter) in iEclat model that comprises of i-Eclat-diffset algorithm, i-Eclat-sortdiffset algorithm and i-Eclat-postdiffset algorithm for infrequent (rare) itemset mining. The association rule is performed to reveal the relationships among itemsets in a transactional database. The task of association rule mining is to discover if there exist the frequent itemset or infrequent patterns in the database and if any, an interesting relationship between these frequent or infrequent itemsets can reveal a new pattern analysis for the future decision making. Regardless of frequent or infrequent itemsets, the persisting issues are deemed to execution time to display the rules and the highest memory consumption during mining process. CRS-iEclat engine is proposed to overcome the said issues. Prior to experimentation, results indicate that CRS-iEclat outperforms iEclat from 54% to 100% accuracy on execution time (ET) in selected database as to show the improvement of ET efficiency.

Data Streams Oriented Outlier Detection Method: A Fast Minimal Infrequent Pattern Mining

Outlier detection is a common method for analyzing data streams. In the existing outlier detection methods, most of methods compute distance of points to solve certain specific outlier detection problems. However, these methods are computationally expensive and cannot process data streams quickly. The outlier detection method based on pattern mining resolves the aforementioned issues, but the existing methods are inefficient and cannot meet requirements of quickly mining data streams. In order to improve the efficiency of the method, a new outlier detection method is proposed in this paper. First, a fast minimal infrequent pattern mining method is proposed to mine the minimal infrequent pattern from data streams. Second, an efficient outlier detection algorithm based on minimal infrequent pattern is proposed for detecting the outliers in the data streams by mining minimal infrequent pattern. The algorithm proposed in this paper is demonstrated by real telemetry data of a satellite in orbit. The experimental results show that the proposed method not only can be applied to satellite outlier detection, but also is superior to the existing methods.

Mining Temporal Exception Rules from Multivariate Time Series Using a new Support Measure

Association rules are a common task to discover useful and comprehensive relationships among frequent and infrequent data. Frequent patterns describe a usual behavior, while infrequent ones represent uncommon knowledge. Our interest lies in finding exception rules, a class of infrequent patterns that may have critical effects as a consequence. Existing approaches for exception rules mining usually handle “itemsets databases”, where transactions are organized with no temporal information. However, temporality may be inherent to some real contexts and should be considered to improve the semantic quality of results. Moreover, these approaches implement a non-discriminatory support measure to estimate the relevance of an item, thus interpreting a large volume of data that may be merely occasional as patterns. Aiming to overcome these drawbacks, we propose TRiER (TempoRal Exception Ruler), an efficient method for mining temporal exception rules that not only discover exceptional behaviors and their causative agents, but also identifies how long consequences take to appear. We also present a new support measure to manipulate time series. This measure considers the context in which a pattern occurs, thus incorporating more semantics to the results obtained. We performed an extensive experimental analysis in real multivariate time series to verify the practical applicability of TRiER. Our results show TRiER has lower computational cost and is more scalable than existing approaches while finding a succinct and relevant set of patterns.

Next generation sequencing to decipher concurrent loss of PMS2 and MSH6 in colorectal cancer

BackgroundImmunohistochemistry (IHQ) is commonly used for the detection of mismatch repair proteins deficiency (MMRD). One very infrequent abnormal pattern of MMR protein expression is the loss of PMS2 and MSH6, with intact expression of MLH1 and MSH2.Case presentationWe review the frequency of this MMRD IHC pattern among 108 colorectal (CRCs) and 35 endometrial cancers in our files with loss of expression of at least one protein, and present two CRCs showing loss of PMS2 and MSH6 protein expression (1.9% of CRCs). NGS analysis of these tumours identified PMS2 mutations (R134* germline mutation in one tumour and M1R and c.1239delA somatic mutation in the other) as the primary event and somatic MSH6 mutation (c.3261dupC) as the secondary event in both tumours.ConclusionsThis study suggests that Next Generation Sequencing (NGS) tumour analysis should be considered in the algorithm of Lynch syndrome screening to detect MMR gen somatic mutation in inconclusive cases.