Ming Li

Qianru Zhang, Yuting Sun, Honggang Wen et al.

Frequency-domain analysis has emerged as a powerful paradigm for time series analysis, offering unique advantages over traditional time-domain approaches while introducing new theoretical and practical challenges. This survey provides a comprehensive examination of spectral methods from classical Fourier analysis to modern neural operators, systematically summarizing three open challenges in current research: (1) causal structure preservation during spectral transformations, (2) uncertainty quantification in learned frequency representations, and (3) topology-aware analysis for non-Euclidean data structures. Through rigorous reviewing of over 100 studies, we develop a unified taxonomy that bridges conventional spectral techniques with cutting-edge machine learning approaches, while establishing standardized benchmarks for performance evaluation. Our work identifies key knowledge gaps in the field, particularly in geometric deep learning and quantum-enhanced spectral analysis. The survey offers practitioners a systematic framework for method selection and implementation, while charting promising directions for future research in this rapidly evolving domain.

Fatema Tuz Zohora, M Ziaur Rahman, Ngoc Hieu Tran et al.

A promising technique of discovering disease biomarkers is to measure the relative protein abundance in multiple biofluid samples through liquid chromatography with tandem mass spectrometry (LC-MS/MS) based quantitative proteomics. The key step involves peptide feature detection in LC-MS map, along with its charge and intensity. Existing heuristic algorithms suffer from inaccurate parameters since different settings of the parameters result in significantly different outcomes. Therefore, we propose PointIso, to serve the necessity of an automated system for peptide feature detection that is able to find out the proper parameters itself, and is easily adaptable to different types of datasets. It consists of an attention based scanning step for segmenting the multi-isotopic pattern of peptide features along with charge and a sequence classification step for grouping those isotopes into potential peptide features. PointIso is the first point cloud based, arbitrary-precision deep learning network to address the problem and achieves 98% detection of high quality MS/MS identifications in a benchmark dataset, which is higher than several other widely used algorithms. Besides contributing to the proteomics study, we believe our novel segmentation technique should serve the general image processing domain as well.

Rui Qiao, Ngoc Hieu Tran, Lei Xin et al.

Personalized cancer vaccines are envisioned as the next generation rational cancer immunotherapy. The key step in developing personalized therapeutic cancer vaccines is to identify tumor-specific neoantigens that are on the surface of tumor cells. A promising method for this is through de novo peptide sequencing from mass spectrometry data. In this paper we introduce DeepNovoV2, the state-of-the-art model for peptide sequencing. In DeepNovoV2, a spectrum is directly represented as a set of (m/z, intensity) pairs, therefore it does not suffer from the accuracy-speed/memory trade-off problem. The model combines an order invariant network structure (T-Net) and recurrent neural networks and provides a complete end-to-end training and prediction framework to sequence patterns of peptides. Our experiments on a wide variety of data from different species show that DeepNovoV2 outperforms previous state-of-the-art methods, achieving 13.01-23.95\% higher accuracy at the peptide level.

Fatema Tuz Zohora, Ngoc Hieu Tran, Xianglilan Zhang et al.

Liquid chromatography with tandem mass spectrometry (LC-MS/MS) based proteomics is a well-established research field with major applications such as identification of disease biomarkers, drug discovery, drug design and development. In proteomics, protein identification and quantification is a fundamental task, which is done by first enzymatically digesting it into peptides, and then analyzing peptides by LC-MS/MS instruments. The peptide feature detection and quantification from an LC-MS map is the first step in typical analysis workflows. In this paper we propose a novel deep learning based model, DeepIso, that uses Convolutional Neural Networks (CNNs) to scan an LC-MS map to detect peptide features and estimate their abundance. Existing tools are often designed with limited engineered features based on domain knowledge, and depend on pretrained parameters which are hardly updated despite huge amount of new coming proteomic data. Our proposed model, on the other hand, is capable of learning multiple levels of representation of high dimensional data through its many layers of neurons and continuously evolving with newly acquired data. To evaluate our proposed model, we use an antibody dataset including a heavy and a light chain, each digested by Asp-N, Chymotrypsin, Trypsin, thus giving six LC-MS maps for the experiment. Our model achieves 93.21% sensitivity with specificity of 99.44% on this dataset. Our results demonstrate that novel deep learning tools are desirable to advance the state-of-the-art in protein identification and quantification.

Xiaopeng Yang, Xiaowen Lin, Shunda Suo et al.

Computer poetry generation is our first step towards computer writing. Writing must have a theme. The current approaches of using sequence-to-sequence models with attention often produce non-thematic poems. We present a novel conditional variational autoencoder with a hybrid decoder adding the deconvolutional neural networks to the general recurrent neural networks to fully learn topic information via latent variables. This approach significantly improves the relevance of the generated poems by representing each line of the poem not only in a context-sensitive manner but also in a holistic way that is highly related to the given keyword and the learned topic. A proposed augmented word2vec model further improves the rhythm and symmetry. Tests show that the generated poems by our approach are mostly satisfying with regulated rules and consistent themes, and 73.42% of them receive an Overall score no less than 3 (the highest score is 5).