We propose a method called AgriPrompt to enhance the agricultural question-answering capability of ChatGPT. We propose a BERT-based model, AgriParse, to extract semantic information from agricultural questions, which is filled into pre-defined prompt templates to guide ChatGPT in answering agricultural questions. We establish an evaluation metric called KQScore, which uses the TF-IDF method and the Word2vec model to evaluate the semantic accuracy of answers. The results show that the average KQScore of ChatGPT with AgriPrompt is 0.81, ChatGPT without prompt is 0.74, and ChatGPT with a static prompt is 0.72. Our method significantly improves the accuracy of ChatGPT in answering agricultural questions.

Seismic inversion accuracy significantly affects the quality of reservoir modeling. Given the limited samples of well logging, prior information such as geological data and stratigraphic characteristics is crucial for enhancing the accuracy and reliability of inversion results. The inability to fully exploit prior information in the data to compensate for data deficiencies may impair network performance. Moreover, the traditional artificial neural networks (ANNs) cannot fully exploit prior information in the data to compensate for data deficiencies, which may impair network performance and further leads to the network’s limited ability to effectively assimilate external prior knowledge. Therefore, a prestack seismic inversion driven by prior information neural network (PINN) and statistical characteristic is proposed to integrate various geological prior information. Initially, lithological analysis is conducted on the well logging data, leading to the classification of the strata into a series of sub-layers based on lithology. After obtaining the statistical characteristics of thickness and elastic parameters for each sub-layer, a geostatistical algorithm is employed to generate numerous pseudo-wells that conform to actual sedimentary laws. Subsequently, the PINN is pre-trained using substantial samples, encompassing both pseudo and real well logging data, to integrate geological structural prior information into its network parameters. Lastly, PINN served as a specially designed prior constraint for the inversion network, allowing its geological information to effectively guide the inversion process via backpropagation. This inversion method is applied to both synthetic and field examples, and in comparison to conventional inversion algorithms, it demonstrated superior accuracy in blind well verification.

The 3-D seismic multihorizon extraction is crucial for 3-D sequence stratigraphy analysis and reservoir modeling. Deep neural networks (DNNs) often cause dislocated horizons in regions with complex geological structures, such as faults and unconformities. This issue arises from two main factors. First, obtaining horizon labels from real seismic data is subjective and expensive, resulting in existing DNNs lacking field seismic data labels, which limits their ability to extract multiple horizons across complex geological structures. Second, directly extracting multiple horizons using only seismic data reduces precision in discontinuous areas with faults and unconformities. To address these issues, this article proposes a domain adaptation layer based on multikernel maximum mean discrepancy (MK-MMD) and designs a domain adaptive DNN (DA-DNN) for seismic multihorizon extraction. We map synthetic and field seismic data to the reproducing kernel Hilbert space (RKHS) and use MK-MMD to minimize feature differences between them. Unlike the traditional multiscale Gaussian kernel function used in MK-MMD, this article constructs a hybrid kernel function that integrates multiscale Gaussian and multiscale Laplacian kernels. The multiscale Gaussian kernel evaluates local-to-global feature differences in continuous areas, whereas the multiscale Laplacian kernel captures rapid feature variations in complex geological structures. Finally, a few seismic horizons and fault attributes guide the training process of DA-DNN, further improving the prediction accuracy of multihorizon extraction. Synthetic and field seismic examples show our model can extract seismic multiple horizons more accurately in field seismic data and performs better in discontinuous areas.

Two sets of rotationally invariant, horizontal and vertical fractures permeated in a homogeneous isotropic background rock can yield a long-wavelength effective orthorhombic medium. Amplitude variation with angle and azimuth (AVAZ) inversion is an effective tool to invert azimuthal seismic data for orthorhombic medium parameters. Given the inherently band-limited nature of real seismic data, orthorhombic AVAZ inversion without reasonable model constraints is a challenging task to obtain stable and reliable estimated results. In this paper, we have developed a novel joint Laplace- and Fourier-domain seismic inversion method to estimate P- and S-wave velocities, density, and horizontal and vertical fracture densities. The orthorhombic AVAZ seismic forward modeling in the Laplace domain is established by introducing the Fourier and damping operators. We present a two-step strategy for joint Laplace- and Fourier-domain seismic inversion in a Bayesian inference framework. Firstly, we perform the Laplace-domain seismic inversion to obtain long-wavelength models of elastic parameters and fracture parameters by exploiting the low-frequency components of the damped azimuthal seismic data. Secondly, with the Laplace-domain inversion results as initial models, the Fourier-domain seismic inversion is implemented to obtain complete inversion results, where all frequency components of original azimuthal seismic data are employed. We demonstrate the feasibility of our method through a synthetic data example and an application of a field data set acquired over a fractured shale reservoir. Meanwhile, we compare our method with the Fourier-domain seismic inversion, results show that our method can yield more reasonable estimated results of orthorhombic medium parameters than the Fourier-domain seismic inversion.

Natural fractures and rock brittleness play an important role in the fracturing and development of shale gas reservoirs. Amplitude variation with angle and azimuth (AVAZ) inversion provides an effective tool for estimating brittleness and fracture properties. However, AVAZ inversion generally employs a linearized reflection coefficient approximation, commonly not applicable for cases with strong contrasts in rock properties and long-offset data. Meanwhile, the large number of model parameters poses great challenges for multiparameter simultaneous inversion. To overcome these limitations, we propose a novel optimal AVAZ inversion approach to estimate the brittleness indicator and fracture density in horizontal transversely isotropic (HTI) media. We first utilize the anisotropic Zoeppritz equation and the convolution model to generate reference AVAZ gathers from well-log data. The singular value decomposition (SVD) method is then employed to obtain optimal basis functions and optimal coefficients that directly link band-limited elastic and fracture reflectivities to observed AVAZ data. This enables the direct estimation of band-limited elastic and fracture reflectivities from observed AVAZ data. Finally, Bayesian poststack seismic inversion, constrained by modified Cauchy prior and low-frequency models, is utilized to invert these band-limited reflectivities for elastic and fracture parameters individually. The feasibility of our method is demonstrated on a synthetic example and a field dataset from the southern Sichuan Basin. Results reveal the ability of our method to produce more satisfactory estimated results of the brittleness indicator and fracture density than the AVAZ simultaneous inversion method, which could aid in the seismic characterization of rock brittleness and natural fractures.

The deconvolution method is crucial for enhancing seismic resolution. Traditional multichannel schemes incorporate lateral constraints to enhance data continuity and integrity. However, a predominant challenge is that many existing methods assume that the estimated model parameters have certain features, which are always inconsistent with the true situation. In this paper, we present a novel data-driven multi-channel seismic deconvolution method based on Convolutional Sparse Coding (CSC) and 2D K-SVD. We partition the seismic profile into low and high-frequency segments, addressing CSC’s limitation in representing low-frequency components effectively. We leverage 2D K-SVD for extracting lateral features in low-frequency components and apply CSC to the high-frequency segments. Distinctively, our method accounts for the lateral characteristics within both frequency bands during decomposition, a notable advancement over conventional filtering-based techniques. It ensures the preserved convolutional relationship in frequency separation. We enhance the conventional 1D K-SVD with 2D sample patches, evolving it into a more adept 2D K-SVD dictionary for low-frequency components. We apply this to multi-channel deconvolution regularization, enabling low-frequency seismic data deconvolution. To address the challenge of information fragmentation in high-frequency components caused by block-based sparse coding, the orthogonal constrains are added into the feature map instead of the image, making the convolutional dictionary learning more precise than the conventional CSC algorithm. This refinement is integral to optimizing the high-frequency deconvolution objective function. In the final step, an iterative processing of both frequency components yields the refined Multichannel Seismic Deconvolution based on orthogonal Convolutional Sparse Coding (MSD-oCSC). Our method’s efficacy is corroborated through rigorous model tests and real data applications.

The application of artificial intelligence in seismic impedance inversion makes the prediction of stratigraphic information more efficient. Semi-supervised framework for impedance inversion is the latest breakthrough method in this field. However, the 1-D semi-supervised methods now in use are unable to extract the spatiotemporal properties of the data solely through the network itself. Moreover, the initial model, a critical input for this method, is typically derived through extrapolation and interpolation of well log data. This can lead to significant errors, especially when the well data are sparse and the subsurface structures are complex. Well data only provide information for a limited section of the reservoir, thereby making it challenging to capture the overall behavior accurately. As a result, the creation of an accurate initial model is often fraught with errors. A more desirable approach is to use seismic attribute-guided methods, such as neural networks, which incorporate both seismic and well log data, leading to a more accurate low-frequency model with lateral variations. In this article, we develop a semi-supervised domain adversarial and spatial fusion (DASF) inversion framework. This method uses a 1-D convolutional neural network (CNN)-based global spatiotemporal analysis module and a 2-D CNN-based local spatiotemporal analysis module to complete the inversion and forward task simultaneously. Multiple spatiotemporal characteristics from two submodules can be successfully fused using an adaptive fusion approach. In this network, the step of extracting the initial model is incorporated into the learning process. Moreover, we adopt adversarial learning in the impedance domain to guide the training process, thereby reducing the network’s dependence on labels. The experiments on the synthetic and field dataset show that the proposed method can efficiently improve the prediction accuracy of the inversion results compared with conventional methods. Meanwhile, the local spatiotemporal analysis module can be used to create a more trustworthy initial model that incorporates the characteristic of seismic and well-logging data.

In zero-shot cross-lingual event argument extraction(EAE) task, a model is typically trained on source language datasets and then applied on task language datasets. There is a trend to regard the zero-shot cross-lingual EAE task as a sequence generation task with manual prompts or discrete prompts. However, there are some problems with these prompts, including using suboptimal prompts and difficult to transfer from source language to target language. To overcome these issues, we propose a method called SPTEAE(A Soft Prompt Transfer model for zero-shot cross-lingual Event Argument Extraction). SPTEAE utilizes a sequence of tunable vectors which are tuned in source language as event type prompts. These source language event type prompts can be transferred as target prompts to perform target EAE task by key-value selection mechanism. For each event type, SPTEAE learns a special target prompt by attending to highly relevant source prompts. Experiment results show that the average performance of SPTEAE with soft prompt transfer is 2.6% higher than the current state-of-the-art model on the ACE2005 dataset.

Recently, there has been growing interest in investigating the effects of self-representation on user experience and perception in virtual environments. However, few studies investigated the effects of levels of body representation (full-body, lower-body and viewpoint) on locomotion experience in terms of spatial awareness, self-presence and spatial presence during virtual locomotion. Understanding such effects is essential for building new virtual locomotion systems with better locomotion experience. In the present study, we first built a walking-in-place (WIP) virtual locomotion system that can represent users using avatars at three levels (full-body, lower-body and viewpoint) and is capable of rendering walking animations during in-place walking of a user. We then conducted a virtual locomotion experiment using three levels of representation to investigate the effects of body representation on spatial awareness, self-presence and spatial presence during virtual locomotion. Experimental results showed that the full-body representation provided better virtual locomotion experience in these three factors compared to that of the lower-body representation and the viewpoint representation. The lower-body representation also provided better experience than the viewpoint representation. These results suggest that self-representation of users in virtual environments using a full-body avatar is critical for providing better locomotion experience. Using full-body avatars for self-representation of users should be considered when building new virtual locomotion systems and applications.

Body actions and head gestures are natural interfaces for interaction in virtual environments. Existing methods for in-place body action recognition often require hardware more than a head-mounted display (HMD), making body action interfaces difficult to be introduced to ordinary virtual reality (VR) users as they usually only possess an HMD. In addition, there lacks a unified solution to recognize in-place body actions and head gestures. This potentially hinders the exploration of the use of in-place body actions and head gestures for novel interaction experiences in virtual environments. We present a unified two-stream 1-D convolutional neural network (CNN) for recognition of body actions when a user performs walking-in-place (WIP) and for recognition of head gestures when a user stands still wearing only an HMD. Compared to previous approaches, our method does not require specialized hardware and/or additional tracking devices other than an HMD and can recognize a significantly larger number of body actions and head gestures than other existing methods. In total, ten in-place body actions and eight head gestures can be recognized with the proposed method, which makes this method a readily available body action interface (head gestures included) for interaction with virtual environments. We demonstrate one utility of the interface through a virtual locomotion task. Results show that the present body action interface is reliable in detecting body actions for the VR locomotion task but is physically demanding compared to a touch controller interface. The present body action interface is promising for new VR experiences and applications, especially for VR fitness applications where workouts are intended.

MOTIVATION: N-linked glycosylation is a frequently occurring post-translational protein modification that serves critical functions in protein folding, stability, trafficking, and recognition. Its involvement spans across multiple biological processes and alterations to this process can result in various diseases. Therefore, identifying N-linked glycosylation sites is imperative for comprehending the mechanisms and systems underlying glycosylation. Due to the inherent experimental complexities, machine learning and deep learning have become indispensable tools for predicting these sites. RESULTS: In this context, a new approach called EMNGly has been proposed. The EMNGly approach utilizes pretrained protein language model (Evolutionary Scale Modeling) and pretrained protein structure model (Inverse Folding Model) for features extraction and support vector machine for classification. Ten-fold cross-validation and independent tests show that this approach has outperformed existing techniques. And it achieves Matthews Correlation Coefficient, sensitivity, specificity, and accuracy of 0.8282, 0.9343, 0.8934, and 0.9143, respectively on a benchmark independent test set.

Seismic data are always in low-resolution due to the limitations of seismic acquisition and processing technology, which bring challenges to subsequent seismic interpretation. Deep learning has been successfully applied to the seismic data super-resolution, all methods tend to directly learn the mapping relationship between low-resolution seismic images and super-resolution seismic images through some complex convolutional neural networks. But blindly increasing the depth of the network brings limited improvement to the super-resolution work. We propose a novel geophysical prior guided framework for seismic data super-resolution to solve this problem. Specifically, we select fault prior to guide the training of deep learning model: we use knowledge distillation technology to progressively propagate the fault prior from the teacher network (trained with the low-resolution synthetic seismic data/high-resolution fault prior and high-resolution synthetic seismic data pairs) to the student net-work (trained with the low-resolution synthetic seismic data and high-resolution synthetic seismic data pairs). To better propagate fault priors, we use feature space loss and soft ground truth loss in student network training. Finally, we use the trained student network to complete the super-resolution of synthetic validation seismic data and real seismic data. In addition, in our super-resolution framework, we directly process 3D seismic data instead of 2D seismic images, which further improves the effects of super-resolution and the subsequent seismic interpretation. Compared with the state-of-the-art seismic super-resolution method, the experimental results show that the super-resolution results of our method can depict the faults more clearly.

We present a kinematic approach based on animation rigging to generating real-time leg animation. Our main approach is to track vertical in-place foot movements of a user using a Kinect v2 sensor and map tracked foot height to the motions of inverse kinematics (IK) targets. We align two IK targets with an avatar's feet and guide the virtual feet to perform cyclic walking motions using a set of kinematic equations. Preliminary testing shows that this approach can produce compelling real-time forward-backward leg animation during in-place walking.

It is crucial to retain as many signal components as possible, while noise is eliminated for wavelet packet noise reduction algorithms. Conventional thresholding functions take coefficients smaller than a given threshold as the noise component and get them removed in many ways. In this letter, considering the potential probability of being the signal-related component of coefficients whose value is smaller than the given threshold and the nonnegligible fact that microseismic (MS) event is sparse compared with the noise, we proposed a new wavelet packet-based denoising method via fuzzy partition. First, instead of a hard partition from a given threshold, wavelet packet coefficients get reduced by a fuzzy partition to retain more potential signal elements and suppress the noise. Second, we employ fuzzy c-means (FCM) clustering to identify the interval period of the MS event further to remove the residual noise and additional resonance in processed time series. We tested our method on synthetic datasets and real-field data from an MS monitoring experiment in a coal mine in Sichuan Basin, China. We utilize Pearson correlation coefficient and root-mean-square error between ideal signal and denoised data as performance indicators in synthetic tests, while sample entropy and kurtosis of denoised data are involved in the real field dataset. Test results from synthetic datasets and the real field dataset demonstrate that the proposed noise reduction method is superior to traditional hard-, soft-, and garrote-thresholding and is more applicable and effective in MS data processing.

Stacking can improve the signal-to-noise ratio (SNR) of seismic data, and therefore, it plays an important role in seismic signal processing. The commonly used stacking methods involve calculating a weighted average trace to reduce the influence of harmful samples with random noise and imprecise travel-time correction. However, abnormal misaligned traces often cause large amplitude change in the same reflection time, leading to very small or even zero weights for the purpose of improving SNR. Therefore, the existed weighted stacking methods discard too much seismic reflection information and may not be conducive to stacking. In this letter, we propose a novel weighted stacking method that uses dynamic time warping (DTW) to solve the misalignment problem of seismic reflection events. Then, the weighted stacking can be applied to suppress random noise based on the aligned samples. This proposed approach will enhance the role of the misaligned traces using larger weights without introducing additional noise, thus making the stacking procedure more robust. The application results on both synthetic and real seismic data demonstrate the effectiveness of our proposed approach.

Accurate first arrival picking plays a crucial role in microseismic data processing. However, it is challenging to guarantee satisfactory accuracy with conventional approaches when the signal-to-noise ratio (SNR) of data is low. This article proposes an automatic first arrival time picking method based on fuzzy$C$means clustering (FCM) and Akaike information criterion (AIC). The proposed method consists of three steps: clustering, rough picking, and adjusting. First, we employ FCM to divide each data point into the signal cluster and the noise cluster according to a fuzzy partition. Second, unlike conventional FCM-based picking approaches, we utilize Otsu’s method to determine a data-dependent threshold, instead of an artificially predefined one, to obtain a coarse result of the microseismic event interval from clustering partition. Finally, note that the microseismic event data points are concentrated in amplitude and also correlated in time. Therefore, we employ the AIC of the clustering partition to seek time-varying information to adjust the coarse result. Besides, we investigated several commonly used characteristic factors to introduce a supervised guideline for feature selection in first arrival picking with FCM. At last, we carried out simulations and real field data tests to verify the reliability of the proposed method. The experimental results demonstrate that the proposed method outperforms the short-and long-time average ratio (SLTA) method, the AIC method, and the conventional FCM-based picking method.

Glycans play important roles in a great variety of biological processes, and these roles are closely determined by the details of their structures. It becomes possible to acquire hidden features from glycan structures using deep learning method with the great progress in recent years. Unlike the linear chain of proteins and DNAs, branching is a unique feature of glycan structures, which makes it very difficult to directly apply deep learning models on glycans. Thus, how to comprehensively and efficiently describe glycans and use them as input to deep learning models still remains challenging. Here, a graph neural network (GNN) called GlyNet was used to obtain high-dimensional representation of glycans and predict their immunogenicity. Our method was applied in SugarBase, and it works more superiorly than the state-of-art method with accuracy increased from 91.7% to 95.6%.

Existing seismic inversion methods are usually 1D, mainly focusing on improving the vertical resolution of inversion results. A few 2D or 3D inversion techniques are either too simple and lack the consideration of stratigraphic structures, or are too complicated which need to extract dip information and solve a complex constrained optimization problem. In this work, with the help of gradient structure tensor (GST) and dictionary learning and sparse representation (DLSR) technologies, we propose a 3D inversion approach (GST-DLSR) that considers both vertical and horizontal structural constraints. In the vertical direction, we investigate the vertical structural features of subsurface models from well-log data by DLSR. In the horizontal direction, we obtain the stratigraphic structural features from a 3D seismic image by GST. We then apply the acquired structural features to constraint the entire inversion procedure. The experiments show that GST-DLSR takes good advantages of both techniques, enabling to produce inversion results with high resolution, good lateral continuity, and enhanced structural features.

Accurate picking of first arrival time plays a critical role in event localization and further data processing in microseismic(MS) monitoring. A large amount of data from receivers make effective automatic time picking method an urgent issue. In this letter, we proposed an adaptive automated time picking approach based on fuzzy c-means (FCM) clustering algorithm. First, by applying FCM, data points are assigned to two clusters with certain membership degrees: signal cluster and noise cluster. Then the vector describing data points to signal cluster center is extracted from the membership degree matrix. Second, considering the shortcoming of a preset threshold, correlation coefficient based adaptive selection algorithm is performed to obtain an optimal threshold for MS event picking. Finally, tests on the synthesis and real data illustrate that our approach outperforms the short-term and long-term average ratio (SLTA) and Akaike information criterion (AIC), and it is more robust than the traditional FCM-based picking method.

Joint monitoring is more potent than conventional micro-seismic(MS) monitoring in understanding underground processes. With simultaneous observations from the surface and downhole, joint monitoring has shown its advantages in disaster early-warning in tunneling and mining, station deployment. However, in Sichuan Basin, China, due to the complex environment, it is intuitively more difficult to transport, install, and maintain stations on the surface. Thus, we have developed a new joint monitoring system for real-time and long-term MS observations based on novel self-developed stations. First, we developed a novel lightweight wireless MS sensor with flexible support of power supply from the battery, solar power, even alternating current for surface monitoring. Second, we also developed a wired station powered by the electricity network in the downhole for underground observation. Finally, a case study in a coal mine located in Sichuan Basin was undertaken. The result illustrates advantages of our joint monitoring system in deployment, real- and long-time joint observation.

Epidermal growth factor receptor (EGFR) plays an essential role in tumor cell proliferation, angiogenesis and apoptosis inhibition; it is a crucial factor leading to cancer occurrence. For example, EGFR tyrosine kinase inhibitors in treating lung cancer patients have an excellent therapeutic effect. Targeted therapy based on EGFR gene mutation is one of the mainstream lung cancer treatment methods. Recent studies have shown that pulmonary nodules' characteristics are associated with the mutant status of EGFR, which provides the possibility of using CT images of patients with pulmonary nodules to predict the mutant status of EGFR. This study used the deep learning algorithm to establish the EGFR mutation type prediction model based on CT image recognition. The data sets used for model training and testing included 121 labeled CT images from hospital patients with pulmonary nodules. The research results showed that the model could be used for the non-invasive EGFR mutation type based on CT images.

MOTIVATION: Glycan identification has long been hampered by complicated branching patterns and various isomeric structures of glycans. Multistage mass spectrometry (MSn) is a promising glycan identification technique as it generates multiple-level fragments of a glycan, which can be explored to deduce branching pattern of the glycan and further distinguish it from other candidates with identical mass. However, the automatic glycan identification still remains a challenge since it mainly relies on expertise to guide a MSn instrument to generate spectra. RESULTS: Here, we proposed a novel method, named bestFSA, based on a best-first search algorithm to guide the process of spectrum producing in glycan identification using MSn. BestFSA is able to select the most appropriate peaks for next round of experiments and complete the identification using as few experimental rounds. Our analysis of seven representative glycans shows that bestFSA correctly distinguishes actual glycans efficiently and suggested bestFSA could be used in practical glycan identification. The combination of the MSn technology coupled with bestFSA should greatly facilitate the automatic identification of glycan branching patterns, with significantly improved identification sensitivity, and reduce time and cost of MSn experiments. AVAILABILITY AND IMPLEMENTATION: http://glycan.ict.ac.cn. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

BACKGROUND: Tandem mass spectrometry (MS/MS) acts as a key technique for peptide identification. The MS/MS-based peptide identification approaches can be categorized into two families, namely, de novo and database search. Both of the two types of approaches can benefit from an accurate prediction of theoretical spectrum. A theoretical spectrum consists of m/z and intensity of possibly occurring ions, which are estimated via simulating the spectrum generating process. Extensive researches have been conducted for theoretical spectrum prediction; however, the prediction methods suffer from low prediciton accuracy due to oversimplifications in the spectrum simulation process. RESULTS: In the study, we present an open-source software package, called OpenMS-Simulator, to predict theoretical spectrum for a given peptide sequence. Based on the mobile-proton hypothesis for peptide fragmentation, OpenMS-Simulator trained a closed-form model for the intensity ratio of adjacent y ions, from which the whole theoretical spectrum can be constructed. On a collection of representative spectra datasets with annotated peptide sequences, experimental results suggest that OpenMS-Simulator can predict theoretical spectra with considerable accuracy. The study also presents an application of OpenMS-Simulator: the similarity between theoretical spectra and query spectra can be used to re-rank the peptide sequence reported by SEQUEST/X!Tandem. CONCLUSIONS: OpenMS-Simulator implements a novel model to predict theoretical spectrum for a given peptide sequence. Compared with existing theoretical spectrum prediction tools, say MassAnalyzer and MSSimulator, our method not only simplifies the computation process, but also improves the prediction accuracy. Currently, OpenMS-Simulator supports the prediction of CID and HCD spectrum for peptides with double charges. The extension to cover more fragmentation models and support multiple-charged peptides remains as one of the future works.

BACKGROUND: The analysis of mass spectra suggests that the existence of derivative peaks is strongly dependent on the intensity of the primary peaks. Peak selection from tandem mass spectrum is used to filter out noise and contaminant peaks. It is widely accepted that a valid primary peak tends to have high intensity and is accompanied by derivative peaks, including isotopic peaks, neutral loss peaks, and complementary peaks. Existing models for peak selection ignore the dependence between the existence of the derivative peaks and the intensity of the primary peaks. Simple models for peak selection assume that these two attributes are independent; however, this assumption is contrary to real data and prone to error. RESULTS: In this paper, we present a statistical model to quantitatively measure the dependence of the derivative peak's existence on the primary peak's intensity. Here, we propose a statistical model, named ProbPS, to capture the dependence in a quantitative manner and describe a statistical model for peak selection. Our results show that the quantitative understanding can successfully guide the peak selection process. By comparing ProbPS with AuDeNS we demonstrate the advantages of our method in both filtering out noise peaks and in improving de novo identification. In addition, we present a tag identification approach based on our peak selection method. Our results, using a test data set, suggest that our tag identification method (876 correct tags in 1000 spectra) outperforms PepNovoTag (790 correct tags in 1000 spectra). CONCLUSIONS: We have shown that ProbPS improves the accuracy of peak selection which further enhances the performance of de novo sequencing and tag identification. Thus, our model saves valuable computation time and improving the accuracy of the results.