Drug repurposing offers a promising alternative to traditional drug development and significantly reduces costs and timelines by identifying new therapeutic uses for existing drugs. However, the current approaches often rely on limited data sources and simplistic hypotheses, which restrict their ability to capture the multi-faceted nature of biological systems. This study introduces adaptive multi-view learning (AMVL), a novel methodology that integrates chemical-induced transcriptional profiles (CTPs), knowledge graph (KG) embeddings, and large language model (LLM) representations, to enhance drug repurposing predictions. AMVL incorporates an innovative similarity matrix expansion strategy and leverages multi-view learning (MVL), matrix factorization, and ensemble optimization techniques to integrate heterogeneous multi-source data. Comprehensive evaluations on benchmark datasets (Fdataset, Cdataset, and Ydataset) and the large-scale iDrug dataset demonstrate that AMVL outperforms state-of-the-art (SOTA) methods, achieving superior accuracy in predicting drug-disease associations across multiple metrics. Literature-based validation further confirmed the model's predictive capabilities, with seven out of the top ten predictions corroborated by post-2011 evidence. To promote transparency and reproducibility, all data and codes used in this study were open-sourced, providing resources for processing CTPs, KG, and LLM-based similarity calculations, along with the complete AMVL algorithm and benchmarking procedures. By unifying diverse data modalities, AMVL offers a robust and scalable solution for accelerating drug discovery, fostering advancements in translational medicine and integrating multi-omics data. We aim to inspire further innovations in multi-source data integration and support the development of more precise and efficient strategies for advancing drug discovery and translational medicine.

Antimicrobial peptides (AMPs) are small molecule peptides that are widely found in living organisms with broad-spectrum antibacterial activity and immunomodulatory effect. Due to slower emergence of resistance, excellent clinical potential and wide range of application, AMP is a strong alternative to conventional antibiotics. AMP recognition is a significant direction in the field of AMP research. The high cost, low efficiency and long period shortcomings of the wet experiment methods prevent it from meeting the need for the large-scale AMP recognition. Therefore, computer-aided identification methods are important supplements to AMP recognition approaches, and one of the key issues is how to improve the accuracy. Protein sequences could be approximated as a language composed of amino acids. Consequently, rich features may be extracted using natural language processing (NLP) techniques. In this paper, we combine the pre-trained model BERT and the fine-tuned structure Text-CNN in the field of NLP to model protein languages, develop an open-source available antimicrobial peptide recognition tool and conduct a comparison with other five published tools. The experimental results show that the optimization of the two-phase training approach brings an overall improvement in accuracy, sensitivity, specificity, and Matthew correlation coefficient, offering a novel approach for further research on AMP recognition.
Anti-Bacterial Agents/chemistry* ; Amino Acid Sequence ; Antimicrobial Cationic Peptides/chemistry* ; Antimicrobial Peptides ; Natural Language Processing

Data-independent acquisition (DIA) is a high-throughput, unbiased mass spectrometry data acquisition method which has good quantitative reproducibility and is friendly to low-abundance proteins. It becomes the preferred choice for clinical proteomic studies especially for large cohort studies in recent years. The mass-spectrometry (MS)/MS spectra generated by DIA is usually heavily mixed with fragment ion information of multiple peptides, which makes the protein identification and quantification more difficult. Currently, DIA data analysis methods fall into two main categories, namely peptide-centric and spectrum-centric. The peptide-centric strategy is more sensitive for identification and more accurate for quantification. Thus, it has become the mainstream strategy for DIA data analysis, which includes four key steps: building a spectral library, extracting ion chromatogram, feature scoring and statistical quality control. This work reviews the peptide-centric DIA data analysis procedure, introduces the corresponding algorithms and software tools, and summarizes the improvements for the existing algorithms. Finally, the future development directions are discussed.
Humans ; Proteomics/methods* ; Reproducibility of Results ; Peptides/chemistry* ; Software ; Algorithms ; Tandem Mass Spectrometry/methods* ; Proteome/analysis*

Cancer is a heterogeneous disease with complex mechanisms that requires targeted precision medicine strategies. The growth of precision medicine is indispensable from the rapid development of genomics. However, genomics has certain limitations in molecular phenotype analysis, proteogenomics thus arose at the right time. Proteogenomics is the merging of proteomics and genomics. This review describes the limitations of genomic analysis and highlights the importance of proteogenomics to re-understand precision oncology from a proteogenomic perspective. In addition, the application of proteogenomics in precision oncology is briefly introduced, the related public data projects are described, and finally, the challenges that need to be addressed at this stage are proposed.
Humans ; Proteogenomics ; Precision Medicine ; Neoplasms/genetics* ; Proteomics ; Genomics

Mass spectrometry and database searching are necessary to identify proteins and peptides. With the rapid development of mass spectrometry technology, mass spectrometry data in proteomics are acquired very quickly, providing a powerful method to identify large-scale proteins and peptides, making mass spectrometry data-based proteomics research more and more into the mainstream. The traditional database searching method has many limitations to identify post-translational modifications of peptides. This paper systematically reviews the development, theoretical concept and applications of spectral network method, and the advantages of spectral network library to identify peptides.

Exponential growth of the mass spectrometry (MS) data is exhibited when the mass spectrometry-based proteomics has been developing rapidly. It is a great challenge to develop some quick, accurate and repeatable methods to identify peptides and proteins. Nowadays, the spectral library searching has become a mature strategy for tandem mass spectra based proteins identification in proteomics, which searches the experiment spectra against a collection of confidently identified MS/MS spectra that have been observed previously, and fully utilizes the abundance in the spectrum, peaks from non-canonical fragment ions, and other features. This review provides an overview of the implement of spectral library search strategy, and two key steps, spectral library construction and spectral library searching comprehensively, and discusses the progress and challenge of the library search strategy.

Anammox bacteria can perform anaerobic ammonium oxidation,a long missing process which contributes 30%~50% to dinitrogen gas in marine nitrogen cycling.The potential role of anam-mox bacteria coupling with ammonium oxidizing bacteria and archaea will benefit to elaborate the complex mechanism of marine nitrogen cycling.Furthermore,the unique cell and genomic characteris-tics make anammox bacteria an important model microorganism to explore the bacterial evolution.Here we reviewed the current status of molecular ecology of marine anammox bacteria and give a perspective into the future based on our understanding of the literature and our own work.