4367 results about "Sequencing data" patented technology

The invention relates to the technical field of image processing, and discloses a video monitoring personnel behavior identification method and system. The method comprises the following steps: performing action node and joint angle extraction processing on a positioning area to obtain human body action skeleton sequence data; constructing an action connection graph and a time sequence association edge, and carrying out behavior pattern learning processing to obtain space-time behavior characteristics; performing action semantic mapping processing on the spatio-temporal behavior features and the human body behavior feature vectors, and performing behavior time sequence chain construction processing to obtain behavior semantic description features; and performing behavior pattern matching and anomaly measurement processing on the behavior semantic description features, performing feature discrimination processing to obtain a behavior type identifier and an anomaly degree index, performing behavior persistence analysis processing, and performing behavior risk level evaluation processing to obtain a behavior recognition result and early warning information. According to the invention, based on multi-modal feature fusion and spatio-temporal behavior modeling, accurate identification of personnel behaviors and dynamic assessment of risk levels are realized.

The invention provides a management method and system for hazardous chemicals in a laboratory, and relates to the technical field of hazardous articles, and the method comprises the steps: obtaining hazardous article operation video data of an operator through an image collection device, extracting key node coordinates and skeleton movement track information, and mapping the information into standardized operation behavior sequence data; and a time sequence behavior evaluation model constructed based on a long and short term memory network is used to identify the non-standard operation fragments, a risk level coefficient is calculated in combination with a dangerous goods attribute information base, and a multi-dimensional evaluation report is generated. And according to the evaluation report, calling a correction strategy library to generate an improvement suggestion containing characters, videos and three-dimensional models, and pushing the improvement suggestion to an operator. By monitoring the behavior of the operator after executing the improvement suggestion and dynamically updating the risk score and the time sequence behavior evaluation model, the dynamic optimization of the evaluation standard is realized, and the safety of the laboratory hazardous chemical substance operation is effectively improved.

The invention discloses a method and a device for constructing a sample efficient world model based on Transform. The method comprises the following steps: separating historical interaction data to obtain historical action sequence data and historical original state data; converting the original state data into multi-scale feature data and carrying out denoising enhancement processing to generate enhanced state data; performing adaptive quantization coding on the enhanced state data to obtain discrete latent variable data; causal structure data are constructed based on historical data, attention weights are designed, and causal enhancement representation data are generated; calculating uncertainty data based on the time sequence association data and generating multi-step prediction sequence data; decomposing the long-term prediction target to obtain layered value data and calculating strategy gradient data; and screening the new interaction data and updating model parameters. Through the methods of multi-scale feature extraction, causal structure learning, hierarchical value evaluation and the like, the prediction accuracy and the sample utilization efficiency of the world model are improved.

The present invention provides an intelligent recognition method based on the fusion of a defect pulse signal and an image, comprising: step 1, collecting data, and selecting ultrasonic A scan data and ultrasonic S scan data capable of characterizing defect information on the basis of data obtained by machine scanning a defect, wherein the ultrasonic A scan data is ultrasonic sequence data, and the ultrasonic S scan data is sector image data; step 2, pre-processing the ultrasonic A scan data and the ultrasonic S scan data; step 3, after pre-processing the ultrasonic A scan data and the ultrasonic S scan data, extracting feature information from the ultrasonic A scan data and the ultrasonic S scan as an index for distinguishing defects, and using the feature information respectively extracted from the ultrasonic A scan data and the ultrasonic S scan data as an input variable for classification; step 4, performing feature value evaluation and screening; and step 5, performing a classification task. The present invention is a recognition method for the fusion of two types of defect data features, which may improve the recognition accuracy.

The invention belongs to the field of gesture recognition, and provides a multi-head attention and spatial-temporal feature fusion-based gesture recognition method and system, and the method comprises the steps: building a neural network model structure through a deep learning frame, importing a gesture sequence of hand articulation points containing time sequence features, and carrying out the recognition of the gesture sequence; and performing feature extraction, position coding and attention calculation, feature fusion and feature mapping on the gesture sequence data by using a proposed gesture recognition algorithm, and outputting a gesture classification result with higher accuracy. A new network topology structure is adopted, and a local and global multi-head attention module is utilized to capture a complex relationship in space and time in two dimensions of time and space, so that the problem that a current model is insufficient in capturing short-term and long-term dependency relationships when coping with complex time sequence data is solved; and the fusion algorithm is more flexible and efficient when processing different types of gesture sequence data.

The invention discloses a storage hard disk remote diagnosis system and method based on the Internet of Things, and relates to the technical field of health management of the Internet of Things and storage equipment. The system comprises a data fusion module, a causal analysis module, a risk analysis module and a map construction module. The data fusion module collects SMART parameters, IO operation time sequence data and environment data through Internet of Things equipment, dynamically distributes multi-source data weights by using an attention mechanism, and extracts hard disk health state features. And the causal analysis module is combined with the multi-scale time sequence convolutional network and the Bayesian causal network to identify periodic abnormal fluctuation and generate a fault root cause analysis report. The risk analysis module matches historical cases through federal incremental learning, calculates a hard disk fault risk index and generates an early warning signal. And the atlas construction module optimizes resource isolation, data migration and response paths according to the risk indexes, generates a visual operation and maintenance atlas, provides fault positioning, risk links and repair priorities, and improves the operation and maintenance management efficiency.

The invention discloses an external data extraction method for a retrieval enhancement generation system. The external data extraction method comprises the following steps: receiving user query data, constructing initial suffix sequence data, and extracting target output template data; calculating position loss data and gradient matrix data based on the initial suffix sequence data and the target output template data; using the gradient matrix data to select candidate words and evaluate a replacement scheme, and generating candidate word data and evaluation matrix data; selecting an optimal replacement scheme according to the evaluation matrix data, updating the sequence, and generating optimized suffix sequence data and gradient feature matrix data; building enhanced prompt sequence data based on the optimized suffix sequence data; a bypassing scheme is designed by analyzing the filtering rule, and bypassing strategy set data and execution sequence data are generated; and finally carrying out integration processing to generate an extraction data set and integrity report data. According to the invention, the accuracy and efficiency of data extraction are improved, and the robustness of the system is enhanced.

The invention discloses a feed production equipment collaborative scheduling and operation optimization method based on deep learning. The method comprises the following steps: S1, constructing an equipment state sequence data set; s2, constructing a disturbance sequence data set; s3, inputting a disturbance resistance residual fusion network to generate a preliminary scheduling scheme; s4, constructing an equipment conflict reasoning graph; s5, embedding the equipment conflict reasoning graph into the scheduling network, and correcting the preliminary scheduling scheme; s6, inputting the corrected preliminary scheduling scheme into an improved NGBoost model, introducing a confidence factor estimation and dynamic distribution calibration mechanism, and outputting a predicted expected value and an uncertainty score of each scheduling behavior; s7, identifying a high-risk behavior according to the uncertainty score, and generating a scheduling correction candidate set; and S8, performing multi-target comprehensive evaluation on the scheduling correction candidate set, and screening the candidate set with the highest score as a final scheduling plan to be issued and executed. According to the method, deep learning and an improved NGBoost model are combined, and intelligent scheduling optimization of feed equipment is realized.

The invention discloses a space transcriptomics cell type deconvolution method based on a generative adversarial network and a comparative learning framework. The method comprises the following steps: firstly, enhancing a gene expression matrix by using a histological image and spatial position information; then, in combination with GraphSAGE and self-supervised comparative learning, optimizing feature representation of space transcriptomics data, and reconstructing high-quality space gene expression data; single-cell RNA sequencing data gene expression features are extracted by using a variational automatic encoder, and meanwhile, the quality of a generated sample is optimized by using a discriminator, so that efficient feature extraction and accurate gene expression reconstruction are realized; and finally, mapping single-cell RNA sequencing data into spatial points through comparative learning and mapping matrix learning based on low-rank representation, thereby predicting the cell type of each point. According to the model, multi-modal information in single-cell RNA sequencing and space transcriptomics data is comprehensively considered, and a mapping matrix is constructed and optimized, so that the cell type of each space point is predicted.

The invention discloses a human skeleton action recognition method for electric power field operation. The method comprises the following steps: acquiring a video of an actual electric power field operation scene; preprocessing the video and then building a three-dimensional human body posture estimation network model; performing feature extraction in the three-dimensional human body posture estimation network model by using a self-attention mechanism to obtain three-dimensional skeleton joint point sequence data of a human body; and building a human skeleton action recognition network model to capture the motion information of the skeleton sequence and the correlation between time and space, and decoding through a full connection layer to obtain a final action recognition result. According to the method, the three-dimensional human body posture estimation technology and the human body skeleton action recognition technology are utilized, the defects that the robustness is poor, the background is complex and illumination changes when action recognition is directly carried out through video images are overcome, the accuracy of action recognition is improved, and therefore the accuracy of action risk judgment of field operation personnel is improved, and the working efficiency is improved. And the field operation efficiency and the safety management level are improved, and possible risks are reduced.

The invention discloses a diagnostic marker of an acute myelogenous leukemia prognosis model as well as application and a prediction method of the diagnostic marker, and relates to the field of acute myelogenous leukemia prognosis research. The prognosis model is a prognosis model of acute myelogenous leukemia based on eight clinical characteristics, namely the disease age, the LDH level during disease, the RUNX1 gene, the KMT2A gene, the TP53 gene, the DMNT3A gene, the CBFB:: MYH11 gene and whether the first course of treatment is relieved or not; the construction method comprises the following steps of: sequentially carrying out univariate Cox regression analysis, Kaplan-Meier survival analysis and multivariate Cox regression analysis on clinical data of a patient from a data set and RNA sequencing data of peripheral blood and bone marrow samples in combination with survival information, further weighting an expression level according to an obtained regression coefficient, and carrying out quantitative analysis on the expression level. And establishing a prognosis model of acute myelogenous leukemia based on eight clinical characteristics. The acute myelogenous leukemia prognosis model disclosed by the invention has a relatively good evaluation effect.

The invention discloses a bus duct trend analysis early warning method and system and a storage medium, and relates to the technical field of data processing. The method comprises the following steps: determining N bus duct key positions, and arranging sensor groups on the N bus duct key positions respectively to obtain a sensor monitoring network; acquiring N pieces of temperature time sequence data and N pieces of current time sequence data; performing temperature difference calculation and current change rate calculation on the N pieces of temperature time sequence data to obtain N key position temperature change rates and N key position current change rates; carrying out integration trend analysis, and determining a bus duct abnormal operation trend result; and constructing an alarm mechanism, triggering the alarm mechanism based on a bus duct abnormal operation trend result to carry out classification matching, determining operation early warning parameters, and carrying out operation early warning control on the target bus duct. The technical problems of inaccurate monitoring of the running state of the bus duct and untimely early warning in the prior art are solved, and the technical effect of improving the accuracy and timeliness of running monitoring of the bus duct is achieved.

The invention relates to the technical field of genomics, in particular to a gene detection data cleaning method and system based on artificial intelligence. The method comprises the following steps: acquiring gene detection data, and performing preliminary filtering on the gene detection data based on removal of low-quality reads to obtain preliminary filtering data; performing linker sequence identification and removal on the preliminarily filtered data, and performing sequencing error correction to generate gene sequence data; performing GC content analysis and repetitive sequence identification on the gene sequence data to obtain sequence feature data; performing dynamic quality threshold adjustment according to the sequence feature data, and performing secondary filtering on the gene sequence data to generate high-quality sequence data; and performing sequence pattern recognition on the high-quality sequence data to obtain sequence pattern feature data. According to the method, deep analysis is carried out by utilizing sequence characteristics such as GC content and nucleotide distribution, and the recognition capability on sequence complexity and variation modes is improved.

The invention relates to the technical field of artificial intelligence, can be applied to business scenes such as agent autonomous decision making, financial science and technology and medical health, and discloses a multi-modal sequence data processing method, device and equipment and a medium. Extracting multi-scale feature hierarchies, combining the multi-scale feature hierarchies into a multi-scale feature pyramid set, performing cross-modal feature alignment to generate a multi-scale alignment feature sequence, executing local and global attention processing to generate long-distance dependency features, performing cross-layer information interaction to generate comprehensive multi-scale features, and performing multi-scale feature extraction; and dynamically fusing the multi-modal information and inputting the multi-modal information into a task decision network to obtain a target task result. According to the method, through the multi-scale feature pyramid, cross-modal alignment, attention processing and cross-layer information interaction, the problem of insufficient relevance between different modals and different scales in multi-modal long sequence data is solved, and fine modeling and dynamic fusion of multi-modal and multi-scale features are realized.

The invention relates to the technical field of health management, in particular to a gastrointestinal health assessment and management method based on big data, and the method comprises the following steps: S1, health data collection: collecting multi-dimensional health data of a user; s2, data preprocessing: preprocessing the collected multi-dimensional health data; s3, regulating and analyzing intestinal microecology, namely classifying and analyzing intestinal flora based on the intestinal flora gene sequencing data of the user in combination with living habits and diet data, identifying the condition of flora imbalance, and dynamically regulating flora balance through personalized regulation suggestions; s4, gastrointestinal health state evaluation: comprehensively evaluating the gastrointestinal health state of the user; and S5, generating a personalized health management scheme: generating the personalized health management scheme. According to the invention, by monitoring the health feedback of the user in real time, the scheme can be dynamically adjusted, so that the intervention is more flexible and targeted.

The invention relates to the technical field of recommendation methods, in particular to a consumer behavior-based commodity recommendation method, which comprises the following steps of: extracting behavior sequence data including clicking, browsing, searching and purchasing from a user interaction behavior log in an e-commerce platform, and carrying out sequence data slicing on the behavior sequence data through a sequence data slicing technology; dividing the long-time behavior sequence of the user into a plurality of behavior segments with a long-term dependency relationship; generating a behavior context feature vector; based on the long-term dependency feature vector and the behavior context feature vector, constructing a context-based intention speculation model, speculating the long-term shopping intention of the user, and generating an intention vector of the user behavior; and through a multi-target optimization algorithm, generating a recommendation strategy meeting various targets, and applying the generated recommendation strategy to real-time recommendation. According to the method and the device, the personalized degree of recommendation is improved, and diversified requirements of the user can be better met, so that the satisfaction degree of the user is improved.

The invention discloses a three-dimensional human body posture estimation method based on hybrid architecture space-time modeling, and the method comprises the steps: obtaining historical human body posture data, and generating a three-dimensional human body posture estimation network training set; constructing a three-dimensional human body posture estimation network model, wherein the network model comprises a space-time position embedding module, a space-time Mama block, a space-time self-attention block, a full connection layer and a regression layer; based on the estimation network training set and a loss function training network model, learning a mapping relation from a two-dimensional attitude joint point sequence to a three-dimensional attitude joint point sequence; and inputting two-dimensional attitude joint point sequence data to be estimated into the trained network model, and outputting a three-dimensional attitude joint point sequence. According to the method, the global spatio-temporal features are rapidly extracted by using Mama, then the global spatio-temporal feature information is further supplemented by using Transform, and the spatio-temporal features are fused and complemented more effectively by using a hybrid architecture.

The invention discloses a train dispatcher behavior anomaly detection system based on LSTM-FCN and a dynamic intervention method thereof, and relates to the technical field of intelligent monitoring. Scheduling operation time sequence data, environment state data and physiological monitoring data are collected in real time, a space-time joint feature vector is constructed, and a train dispatcher behavior anomaly detection result is obtained. The LSTM-FCN hybrid network is used to extract time sequence dependence features and spatial pattern features, and an attention weight matrix generated by physiological data and a compensation factor matrix generated by environmental parameters are combined to dynamically calculate a comprehensive abnormal probability value; and when the probability value exceeds a self-adaptive adjustment threshold value, triggering a hierarchical intervention mechanism based on the driving scheduling knowledge graph to realize progressive intervention from interface prompting to manual takeover. By fusing multi-modal data and self-adaptive dynamic compensation, the problem of poor adaptability to complex scenes due to adoption of a static model in traditional train monitoring is solved, and the accuracy and intervention timeliness of abnormal behavior detection of the train dispatcher are remarkably improved.

The invention provides a specific single nucleotide polymorphism (SNP) molecular marker for sex identification of cobia and application of the specific single nucleotide polymorphism (SNP) molecular marker. Based on whole genome high-throughput sequencing data of male and female cobia, after deep comparative analysis, the specific molecular marker related to the sex of cobia is screened for the first time, and the primer pair designed for the marker can be used independently or used in a combined mode and is high in flexibility. The method has relatively high judgment accuracy, so that the sex of cobia can be quickly and accurately identified, and the method has a very good application prospect.

The invention relates to the technical field of marine organism community monitoring, in particular to a marine organism community monitoring method and system based on a memory mixed prototype and knowledge distillation. The method comprises the following steps: carrying out data preprocessing on obtained multi-source heterogeneous monitoring data; constructing a multi-modal joint feature by using the preprocessed data; performing cross-modal teacher model training by using the multi-modal joint features; training a student model based on the historical marine ecological sequence data; performing enhanced knowledge distillation on the student model by using the cross-modal teacher model; performing marine ecological community time sequence prediction by using the trained student model; and performing anomaly detection based on the historical data and the prediction data. Based on the breakthrough of the bottleneck of the existing marine organism community monitoring technology, the multi-dimensional technical improvement and application value are realized.

The invention discloses a virtualized container environment monitoring device and method based on eBPF, and aims to solve the problems of poor isolation, insufficient security and platform adaptation deficiency in a virtualized container environment. According to the method, time sequence data of monitored events in a virtualized container environment are collected in real time through an eBPF technology, the time sequence data are transmitted to a data analysis module through eBPF mapping and message queues, data are analyzed through a long-short-term memory network, the deviation degree of the current time sequence data and historical data is calculated, abnormal behaviors are judged, and an alarm is given. According to the method, the user-defined program is operated under the condition that kernel codes do not need to be modified, intelligent analysis and abnormal behavior recognition are conducted on the collected data, comprehensive monitoring and dynamic detection of complex threats are achieved, the safety, stability and compatibility of the system are improved, and the method is suitable for virtual environments under cloud computing and big data scenes.

A method for predicting one or more features of an antigen-binding protein is provided, comprising: providing the amino acid sequence of at least part of one or more chains of the antigen-binding protein or an encoded sequence derived therefrom as input to a deep learning model comprising a sequence encoding module configured to take as input an amino acid sequence and to produce as output an embedding corresponding to the input sequence, and predicting the one or more features of the antigen-binding protein using the embedding produced by the sequence encoding module The sequence encoding module has been trained as part of a machine learning model comprising a structure encoding module, and using training sequence data and structure data from a plurality of antigen-binding proteins. Related methods, systems and products are also described.

The invention belongs to the technical field of allele typing, and discloses a gene haplotype typing method and device based on sequencing data and a medium, and the method comprises the following steps: obtaining to-be-detected gene sequencing data from a to-be-detected sample; splitting the sequencing data into genes according to a reference sequence, and performing variation information detection and variation information filtering; after sequence splitting is carried out on the basis of the sequence obtained after variation information filtering, the haplotype sequence obtained after sequence splitting is corrected, and a to-be-detected gene haplotype typing result of the to-be-detected sample is obtained. According to the method, complex calculation is simplified by adopting a mode of firstly splitting sequences into genes, the method does not completely depend on sequence comparison results, and similar typing is finally evaluated by adopting a mode of combining comparison with backtracking variation information scoring, so that typing difference information can be effectively refined, and interference of sequencing data noise on the results is reduced; and a haplotype result is verified for multiple times by adopting a primary typing and correction mode, so that the result accuracy is higher.

The invention discloses an SPR response region identification method based on image semantic segmentation and time sequence alignment, and the method comprises the following steps: collecting SPR image frame sequence data, and constructing an original image sequence; performing image preprocessing operation on the original image sequence, and outputting a standardized image sequence; constructing a time sequence window image set composed of multiple continuous frames; inputting the time sequence window image set into an improved SegFormer model, and generating a response region segmentation mask image corresponding to each frame; executing cross-frame time sequence alignment operation of the response area, and outputting time sequence consistency identification mapping of the response area; performing area statistics, intensity analysis and time positioning operation; and generating a structured response region recognition result. The spatial-temporal evolution process of the response area in the SPR image sequence can be effectively recognized, the accuracy and stability of response area recognition are improved, and the method is suitable for high-precision biological detection and real-time molecular analysis scenes.

The invention provides a multi-modal protein function prediction method and system, and the method comprises the following steps: carrying out the processing of a one-dimensional convolution layer and a pooling layer of protein sequence data, extracting the local dependence features in a protein sequence, and capturing the mode and key sequence features in the protein; using protein sequence data as input, and using a large-scale pre-trained protein language model to extract residue-level embedded information in a protein sequence as node features to be applied to the contact graph; the method comprises the following steps: taking protein sequence data as input, predicting the probability of contact between protein residues based on a Cb-Cb distance, and constructing a contact graph; and performing joint processing on features of different modes by using a multi-head attention mechanism and a residual structure, integrating data from different modes through feature fusion, and fusing the extracted key sequence features, the residue level embedded information and the contact graph. The prediction method is accurate in prediction and good in performance.

The invention provides a deep learning inference platform and an operation method and system of a deep learning inference engine. The deep learning inference engine is carried in a processor, and the operation method comprises the following steps: loading a trained inference model, establishing a tensor cache manager, and pre-distributing an output tensor; receiving input data, obtaining an initial tensor corresponding to the input data, performing tensor remodeling on the initial tensor to obtain a target tensor of a target dimension, and storing the target tensor into a first cache variable of a tensor cache manager; calling a target tensor in the first cache variable and performing mixed attention calculation on the target tensor to obtain a mixed attention calculation result; and writing the mixed attention calculation result into an output tensor for outputting. The method can dynamically adapt to multi-step decoding or high concurrency situations, can adapt to diversified texts, audios or other sequence data and other scenes, enables a deep learning inference engine to have higher universality and expandability, and improves the system performance.

A single cell sequencing data interpolation method based on a biclustering and graph convolutional neural network comprises the following steps: 1) respectively downloading matrix-form gene expression data of human and mouse from a gene database, and screening out cells of which the total number of gene expressions is less than 3 and genes of which the total number of cell expressions is less than 200; 2) performing feature selection on the gene expression matrix, finding out cells with consistent growth trend, forming a new two-dimensional matrix, and constructing a cell connection diagram; 3) constructing a BiGCN single-cell interpolation model by using a graph convolutional neural network, learning a nonlinear relationship between cells, dividing a data set into a training set and a test set, training the model by using the training set, and reserving the trained BiGCN single-cell interpolation model; 4) inputting the test set into the trained BiGCN single cell interpolation model to obtain an interpolation result, and evaluating the interpolation result by using an evaluation index; the method has the characteristics that the feature extraction capability is improved, interference of redundant zero expression is avoided, data sparsity is effectively relieved, and the interpolation accuracy is improved.

The invention discloses a civil aircraft PHM modeling method based on cross-modal coupling, a medium and equipment, and belongs to the technical field of aircrafts. The method comprises the steps of S1, multi-modal data preprocessing: performing feature extraction and time-space / event alignment for three modalities of flight time sequence data, text data and image data, and laying a foundation for subsequent fusion modeling; s2, cross-modal feature fusion and holographic data sample construction: constructing a holographic data sample library covering the whole life cycle through cross-domain fusion and dynamic optimization; and S3, PHM physical-knowledge fusion comprehensive modeling: constructing a double-layer collaborative framework combining physical simulation and knowledge reasoning, and realizing controllable fusion of physical simulation and multi-modal knowledge. The problems that in the prior art, multi-source heterogeneous data alignment is difficult, physical mechanism and data driving fusion is insufficient, and model interpretability is poor can be solved.

The invention discloses a posture feature recognition method based on a video stream, and the method comprises the steps: carrying out the preprocessing of a continuous video stream, obtaining video frame training data, extracting a key frame and an adjacent frame in each frame of image, constructing a feature extraction module for a human body region, and obtaining a global frame, performing local extraction on the human body area by using adjacent frames on the left side and the right side to obtain local frames, and constructing semantic association information for the global frame through time sequence continuity between the local adjacent frames and the current key frame; acquiring enhanced feature representation by adopting a conditional feature aggregation algorithm; obtaining attitude sequence data through the attitude detail features; the method comprises the following steps: establishing three-dimensional coordinates, adaptively extracting posture change data by adopting a human body motion decoupling model, predicting human body posture characteristics through a smooth optimization strategy, and introducing a cross attention mechanism to realize deep fusion of spatio-temporal characteristics, so that the understanding ability of the model to a complex action mode is enhanced; and the attitude expression capability of the model in a sheltered or fuzzy region is obviously improved.

Detecting cross-contamination between test samples used for determining cancer in a subject is beneficial. To detect cross-contamination, test sequences including at least one single nucleotide polymorphism are prepared using genome sequencing techniques. Some of the test sequences can be filtered to improve accuracy and precision. A prior contamination probability for each test sequence is determined based on a minor allele frequency. A contamination model including a likelihood test is applied to a test sequence. The likelihood test obtains a current contamination probability representing the likelihood that the test sample is contaminated. The contamination model can also determine a likelihood that the sample includes loss of heterozygosity representing the likelihood that the test sequence is contaminated. Test samples that are contaminated are removed. A source for the contaminated test sample can be found by comparing contaminated test sequences to other test sequences.