pwc-archive/papers-with-abstracts huggingface dataset:
Equipping Pretrained Unconditional Music Transformers with Instrument and Genre Controls
Authors: Weihan Xu, Julian McAuley, Shlomo Dubnov, Hao-Wen Dong
Date: 2023-11-21 00:00:00
Tasks: Music Generation

The ''pretraining-and-finetuning'' paradigm has become a norm for training domain-specific models in natural language processing and computer vision. In this work, we aim to examine this paradigm for symbolic music generation through leveraging the largest ever symbolic music dataset sourced from the MuseScore forum. We first pretrain a large unconditional transformer model using 1.5 million songs. We then propose a simple technique to equip this pretrained unconditional music transformer model with instrument and genre controls by finetuning the model with additional control tokens. Our proposed representation offers improved high-level controllability and expressiveness against two existing representations. The experimental results show that the proposed model can successfully generate music with user-specified instruments and genre. In a subjective listening test, the proposed model outperforms the pretrained baseline model in terms of coherence, harmony, arrangement and overall quality.
Multi-view Graph Convolution for Participant Recommendation
Authors: Xiaolong Liu, Liangwei Yang, Chen Wang, Mingdai Yang, Zhiwei Liu, Philip S. Yu
Date: 2023-11-20 00:00:00

Social networks have become essential for people's lives. The proliferation of web services further expands social networks at an unprecedented scale, leading to immeasurable commercial value for online platforms. Recently, the group buying (GB) business mode is prevalent and also becoming more popular in E-commerce. GB explicitly forms groups of users with similar interests to secure better discounts from the merchants, often operating within social networks. It is a novel way to further unlock the commercial value by explicitly utilizing the online social network in E-commerce. Participant recommendation, a fundamental problem emerging together with GB, aims to find the participants for a launched group buying process with an initiator and a target item to increase the GB success rate. This paper proposes Multi-View Graph Convolution for Participant Recommendation (MVPRec) to tackle this problem. To differentiate the roles of users (Initiator/Participant) within the GB process, we explicitly reconstruct historical GB data into initiator-view and participant-view graphs. Together with the social graph, we obtain a multi-view user representation with graph encoders. Then MVPRec fuses the GB and social representation with an attention module to obtain the user representation and learns a matching score with the initiator's social friends via a multi-head attention mechanism. Social friends with the Top-k matching score are recommended for the corresponding GB process. Experiments on three datasets justify the effectiveness of MVPRec in the emerging participant recommendation problem.
Optimal Investment and Fair Sharing Rules of the Incentives for Renewable Energy Communities
Authors: Almendra Awerkin, Paolo Falbo, Tiziano Vargiolu
Date: 2023-11-18 00:00:00

The focus on Renewable Energy Communities (REC) is fastly growing after the European Union (EU) has introduced a dedicated regulation in 2018. The idea of creating local groups of citizens, small- and medium-sized companies, and public institutions, which self-produce and self-consume energy from renewable sources is at the same time a way to save money for the participants, increase efficiency of the energy system, and reduce CO$_2$ emissions. Member states inside the EU are fixing more detailed regulations, which describe, how public incentives are measured. A natural objective for the incentive policies is of course to promote the self-consumption of a REC. A sophisticated incentive policy is that based on the so called 'virtual framework'. Under this framework all the energy produced by a REC is sold to the market, and all the energy consumed must be paid to retailers: self-consumption occurs only 'virtually', thanks a money compensation (paid by a central authority) for every MWh produced and consumed by the REC in the same hour. In this context, two problems have to be solved: the optimal investment in new technologies and a fair division of the incentive among the community members. We address these problems by considering a particular type of REC, composed by a representative household and a biogas producer, where the potential demand of the community is given by the household's demand, while both members produce renewable energy. We set the problem as a leader-follower problem: the leader decide how to share the incentive for the self-consumed energy, while the followers decide their own optimal installation strategy. We solve the leader's problem by searching for a Nash bargaining solution for the incentive's fair division, while the follower problem is solved by finding the Nash equilibria of a static competitive game between the members.
Mitigating the missing-fragmentation problem in de novo peptide sequencing with a two-stage graph-based deep learning model
Authors: Zeping Mao, Ruixue Zhang, Lei Xin, Ming Li
Date: 2023-10-19 00:00:00
Proceeding: Nature Machine Intelligence 2023 10
Tasks: de novo peptide sequencing, Graph Neural Network

Novel protein discovery and immunopeptidomics depend on highly sensitive de novo peptide sequencing with tandem mass spectrometry. Despite notable improvement using deep learning models, the missing-fragmentation problem remains an important hurdle that severely degrades the performance of de novo peptide sequencing. Here we reveal that in the process of peptide prediction, missing fragmentation results in the generation of incorrect amino acids within those regions and causes error accumulation thereafter. To tackle this problem, we propose GraphNovo, a two-stage de novo peptide-sequencing algorithm based on a graph neural network. GraphNovo focuses on finding the optimal path in the first stage to guide the sequence prediction in the second stage. Our experiments demonstrate that GraphNovo mitigates the effects of missing fragmentation and outperforms the state-of-the-art de novo peptide-sequencing algorithms.
Sequence-to-sequence translation from mass spectra to peptides with a transformer model
Authors: Melih Yilmaz, William E. Fondrie, Wout Bittremieux, Rowan Nelson, Varun Ananth, Sewoong Oh, William Stafford Noble
Date: 2023-01-04 00:00:00
Proceeding: bioRxiv 2023 1
Tasks: de novo peptide sequencing

A fundamental challenge for any mass spectrometry-based proteomics experiment is the identification of the peptide that generated each acquired tandem mass spectrum. Although approaches that leverage known peptide sequence databases are widely used and effective for well-characterized model organisms, such methods cannot detect unexpected peptides and can be impractical or impossible to apply in some settings. Thus, the ability to assign peptide sequences to the acquired tandem mass spectra without prior information—de novo peptide sequencing—is valuable for gaining biological insights for tasks including antibody sequencing, immunopeptidomics, and metaproteomics. Although many methods have been developed to address this de novo sequencing problem, it remains an outstanding challenge, in part due to the difficulty of modeling the irregular data structure of tandem mass spectra. Here, we describe Casanovo, a machine learning model that uses a transformer neural network architecture to translate the sequence of peaks in a tandem mass spectrum into the sequence of amino acids that comprise the generating peptide. We train a Casanovo model from 30 million labeled spectra and demonstrate that the model outperforms several state-of-the-art methods on a cross-species benchmark dataset. We also develop a version of Casanovo that is fine-tuned for non-enzymatic peptides. Finally, we demonstrate that Casanovo’s superior performance improves the analysis of immunopeptideomics and metaproteomics experiments and allows us to delve deeper into the dark proteome.
Deep learning-driven fragment ion series classification enables highly precise and sensitive de novo peptide sequencing
Authors: Daniela Klaproth-Andrade, Johannes Hingerl, Nicholas H. Smith, Jakob Träuble, Mathias Wilhelm, Julien Gagneur
Date: 2023-01-06 00:00:00
Proceeding: bioRxiv 2023 1
Tasks: de novo peptide sequencing, Sensitivity

Unlike for DNA and RNA, accurate and high-throughput sequencing methods for proteins are lacking, hindering the utility of proteomics in applications where the sequences are unknown including variant calling, neoepitope identification, and metaproteomics. We introduce Spectralis, a new de novo peptide sequencing method for tandem mass spectrometry. Spectralis leverages several innovations including a new convolutional neural network layer connecting peaks in spectra spaced by amino acid masses, proposing fragment ion series classification as a pivotal task for de novo peptide sequencing, and a new peptide-spectrum confidence score. On spectra for which database search provided a ground truth, Spectralis surpassed 40% sensitivity at 90% precision, nearly doubling state-of-the-art sensitivity. Application to unidentified spectra confirmed its superiority and showcased its applicability to variant calling. Altogether, these algorithmic innovations and the substantial sensitivity increase in the high-precision range constitute an important step toward broadly applicable peptide sequencing.
Introducing π-HelixNovo for practical large-scale de novo peptide sequencing
Authors: Tingpeng Yang, Tianze Ling, Boyan Sun, Zhendong Liang, Fan Xu, Xiansong Huang, Linhai Xie, Yonghong He, Leyuan Li, Fuchu He, Yu Wang, Cheng Chang
Date: 2023-08-27 00:00:00
Proceeding: bioRxiv 2023 8
Tasks: de novo peptide sequencing

De novo peptide sequencing is a promising approach for novel peptide discovery. We use a novel concept of complementary spectra to enhance ion information and propose a de novo sequencing model π-HelixNovo based on Transformer architecture. π-HelixNovo outperforms other state-of-the-art models and enhances the taxonomic resolution of gut metaproteome, taking a significant step forward in de novo sequencing.
De novo peptide sequencing by deep learning
Authors: Ngoc Hieu Tran, Xianglilan Zhang, Lei Xin, Baozhen Shan, Ming Li
Date: 2017-07-18 00:00:00
Proceeding: PNAS 2017 7
Tasks: Deep Learning, de novo peptide sequencing

Significance
Our method, DeepNovo, introduces deep learning to de novo peptide sequencing from tandem MS data, the key technology for protein characterization in proteomics research. DeepNovo achieves major improvement of sequencing accuracy over state of the art methods and subsequently enables complete assembly of protein sequences without assisting databases. Our model is retrainable to adapt to any sources of data and provides a complete end-to-end training and prediction solution, an important feature given the growing massive amount of data. Our study also presents an innovative approach to combine deep learning and dynamic programming to solve optimization problems.
Abstract
De novo peptide sequencing from tandem MS data is the key technology in proteomics for the characterization of proteins, especially for new sequences, such as mAbs. In this study, we propose a deep neural network model, DeepNovo, for de novo peptide sequencing. DeepNovo architecture combines recent advances in convolutional neural networks and recurrent neural networks to learn features of tandem mass spectra, fragment ions, and sequence patterns of peptides. The networks are further integrated with local dynamic programming to solve the complex optimization task of de novo sequencing. We evaluated the method on a wide variety of species and found that DeepNovo considerably outperformed state of the art methods, achieving 7.7–22.9% higher accuracy at the amino acid level and 38.1–64.0% higher accuracy at the peptide level. We further used DeepNovo to automatically reconstruct the complete sequences of antibody light and heavy chains of mouse, achieving 97.5–100% coverage and 97.2–99.5% accuracy, without assisting databases. Moreover, DeepNovo is retrainable to adapt to any sources of data and provides a complete end-to-end training and prediction solution to the de novo sequencing problem. Not only does our study extend the deep learning revolution to a new field, but it also shows an innovative approach in solving optimization problems by using deep learning and dynamic programming.
Deep learning enables de novo peptide sequencing from data-independent-acquisition mass spectrometry
Authors: Ngoc Hieu Tran, Rui Qiao, Lei Xin, Xin Chen, Chuyi Liu, Xianglilan Zhang, Baozhen Shan, Ali Ghodsi, Ming Li
Date: 2018-12-20 00:00:00
Proceeding: Nature Methods 2018 12
Tasks: de novo peptide sequencing

We present DeepNovo-DIA, a de novo peptide-sequencing method for data-independent acquisition (DIA) mass spectrometry data. We use neural networks to capture precursor and fragment ions across m/z, retention-time, and intensity dimensions. They are then further integrated with peptide sequence patterns to address the problem of highly multiplexed spectra. DIA coupled with de novo sequencing allowed us to identify novel peptides in human antibodies and antigens.
Uncovering Thousands of New Peptides with Sequence-Mask-Search Hybrid De Novo Peptide Sequencing Framework
Authors: Korrawe Karunratanakul, Hsin-Yao Tang, David W. Speicher, Ekapol Chuangsuwanich, Sira Sriswasdi
Date: 2019-12-01 00:00:00
Proceeding: Molecular & Cellular Proteomics 2019 12
Tasks: de novo peptide sequencing

Typical analyses of mass spectrometry data only identify amino acid sequences that exist in reference databases. This restricts the possibility of discovering new peptides such as those that contain uncharacterized mutations or originate from unexpected processing of RNAs and proteins. De novo peptide sequencing approaches address this limitation but often suffer from low accuracy and require extensive validation by experts. Here, we develop SMSNet, a deep learning-based de novo peptide sequencing framework that achieves >95% amino acid accuracy while retaining good identification coverage. Applications of SMSNet on landmark proteomics and peptidomics studies reveal over 10,000 previously uncharacterized HLA antigens and phosphopeptides, and in conjunction with database-search methods, expand the coverage of peptide identification by almost 30%. The power to accurately identify new peptides of SMSNet would make it an invaluable tool for any future proteomics and peptidomics studies, including tumor neoantigen discovery, antibody sequencing, and proteome characterization of non-model organisms.
Computationally instrument-resolution-independent de novo peptide sequencing for high-resolution devices
Authors: Rui Qiao, Ngoc Hieu Tran, Lei Xin, Xin Chen, Ming Li, Baozhen Shan, Ali Ghodsi
Date: 2021-03-18 00:00:00
Proceeding: Nature Machine Intelligence 2021 3
Tasks: de novo peptide sequencing

De novo peptide sequencing is the key technology for finding novel peptides from mass spectra. The overall quality of sequencing results depends on the de novo peptide sequencing algorithm as well as the quality of mass spectra. Over the past decade, the resolution and accuracy of mass spectrometers have improved by orders of magnitude and higher-resolution mass spectra have been generated. How to effectively take advantage of those high-resolution data without substantially increasing the computational complexity remains a challenge for de novo peptide sequencing tools. Here we present PointNovo, a neural network-based de novo peptide sequencing model that can robustly handle any resolution levels of mass spectrometry data while keeping the computational complexity unchanged. Our extensive experiment results show PointNovo outperforms existing de novo peptide sequencing tools by capitalizing on the ultra-high resolution of the latest mass spectrometers.
PGPointNovo: an efficient neural network-based tool for parallel de novo peptide sequencing
Authors: Xiaofang Xu, Chunde Yang, Qiang He, Kunxian Shu, Yuan Xinpu, Zhiguang Chen, Yunping Zhu, Tao Chen
Date: 2023-04-25 00:00:00
Proceeding: Bioinformatics Advances 2023 4
Tasks: de novo peptide sequencing, Efficient Neural Network

De novo peptide sequencing for tandem mass spectrometry data is not only a key technology for novel peptide identification, but also a precedent task for many downstream tasks, such as vaccine and antibody studies. In recent years, neural network models for de novo peptide sequencing have manifested a remarkable ability to accommodate various data sources and outperformed conventional peptide identification tools. However, the excellent model is computationally expensive, taking up to 1 week to process about 400 000 spectrums. This article presents PGPointNovo, a novel neural network-based tool for parallel de novo peptide sequencing. PGPointNovo uses data parallelization technology to accelerate training and inference and optimizes the training obstacles caused by large batch sizes. The results of extensive experiments conducted on multiple datasets of different sizes demonstrate that compared with PointNovo the excellent neural network-based de novo peptide sequencing tool, PGPointNovo, accelerates de novo peptide sequencing by up to 7.35× without precision or recall compromises.
pNovo 3: precise de novo peptide sequencing using a learning-to-rank framework
Authors: Hao Yang, Hao Chi, Wen-Feng Zeng, Wen-Jing Zhou, Si-Min He
Date: 2019-07-05 00:00:00
Proceeding: Bioinformatics 2019 7
Tasks: Deep Learning, de novo peptide sequencing, Learning-To-Rank

De novo peptide sequencing based on tandem mass spectrometry data is the key technology of shotgun proteomics for identifying peptides without any database and assembling unknown proteins. However, owing to the low ion coverage in tandem mass spectra, the order of certain consecutive amino acids cannot be determined if all of their supporting fragment ions are missing, which results in the low precision of de novo sequencing.
In order to solve this problem, we developed pNovo 3, which used a learning-to-rank framework to distinguish similar peptide candidates for each spectrum. Three metrics for measuring the similarity between each experimental spectrum and its corresponding theoretical spectrum were used as important features, in which the theoretical spectra can be precisely predicted by the pDeep algorithm using deep learning. On seven benchmark datasets from six diverse species, pNovo 3 recalled 29–102% more correct spectra, and the precision was 11–89% higher than three other state-of-the-art de novo sequencing algorithms. Furthermore, compared with the newly developed DeepNovo, which also used the deep learning approach, pNovo 3 still identified 21–50% more spectra on the nine datasets used in the study of DeepNovo. In summary, the deep learning and learning-to-rank techniques implemented in pNovo 3 significantly improve the precision of de novo sequencing, and such machine learning framework is worth extending to other related research fields to distinguish the similar sequences.
PepNet: A Fully Convolutional Neural Network for De novo Peptide Sequencing
Authors: Kaiyuan Liu, Yuzhen Ye, Haixu Tang
Date: 2022-02-17 00:00:00
Proceeding: ResearchSquare 2022 2
Tasks: de novo peptide sequencing

The de novo peptide sequencing, which does not rely on a comprehensive target sequence database, provided us a way to identify novel peptides from tandem mass (MS/MS) spectra. However, current de novo sequencing algorithms suffer from lower accuracy and coverage, which hinders their applications in proteomics. In this paper, we present PepNet, a fully convolutional neural network (CNN) for high accuracy de novo peptide sequencing. It takes an MS/MS spectrum (represented as a high dimensional vector) as input, and outputs the optimal peptide sequence along with its confidence score. Our model was trained using a total of 30 million high-energy collisional dissociation (HCD) MS/MS spectra from multiple human peptide spectral libraries. The evaluation results show that PepNet significantly outperformed currently best-performing de novo sequencing algorithms (e.g. PointNovo and DeepNovo) at both peptide level accuracy and positional level accuracy. In addition, PepNet can sequence a large fraction of spectra that were not identified by database search engines, and thus could be used as a complementary tool of database search engines for peptide identification in proteomics.
STemGAN: spatio-temporal generative adversarial network for video anomaly detection
Authors: Rituraj Singh, Krishanu Saini, Anikeit Sethi, Sumeet Saurav, Aruna Tiwari, Sanjay Singh
Date: 2023-09-22 00:00:00
Proceeding: Applied Intelligence 2023 9
Tasks: Anomaly Detection, Anomaly Detection In Surveillance Videos, Decoder, Generative Adversarial Network, Transfer Learning, Unsupervised Anomaly Detection, Video Anomaly Detection

Automatic detection and interpretation of abnormal events have become crucial tasks in large-scale video surveillance systems. The challenges arise from the lack of a clear definition of abnormality, which restricts the usage of supervised methods. To this end, we propose a novel unsupervised anomaly detection method, Spatio-Temporal Generative Adversarial Network (STemGAN). This framework consists of a generator and discriminator that learns from the video context, utilizing both spatial and temporal information to predict future frames. The generator follows an Autoencoder (AE) architecture, having a dual-stream encoder for extracting appearance and motion information, and a decoder having a Channel Attention (CA) module to focus on dynamic foreground features. In addition, we provide a transfer-learning method that enhances the generalizability of STemGAN. We use benchmark Anomaly Detection (AD) datasets to compare the performance of our approach with the existing state-of-the-art approaches using standard evaluation metrics, i.e., AUC (Area Under Curve) and EER (Equal Error Rate). The empirical results show that our proposed STemGAN outperforms the existing state-of-the-art methods achieving an AUC score of 97.5% on UCSDPed2, 86.0% on CUHK Avenue, 90.4% on Subway-entrance, and 95.2% on Subway-exit.