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.

Drug repurposing offers a promising alternative to traditional drug development and significantly re-duces 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(Fdata-set,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 pro-cessing 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 so-lution 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.

Objective:To investigate immunotherapy effects and mechanism of BCG and recombinant BCG(rBCG)with c-di-AMP as adjuvant on melanoma in mice model.Methods:Melanoma mice model was established by B16F10 cell subcutaneous injec-tion in groin,and treated with 1×106 CFU of BCG and rBCG by adjacent injection of subcutaneous tumor for 3 times,respectively.Survival of melanotic mice,tumor growth and metastasis were observed.Tumor tissues of mice were isolated to prepare cell suspen-sion,and proportion of immune cells were detected by flow cytometry.Transcriptional levels of immune-related genes in tumor tissues were detected by qRT-PCR.Results:Both BCG and rBCG immunotherapy could significantly inhibit growth in melanoma mice and prolong survival time of mice.rBCG showed better inhibition on metastasis than BCG.Both strains significantly reduced proportion of M2-type macrophages and myeloid-derived suppressor cell associated with tumor growth and metastasis.Both two strains promoted infiltration of lymphocytes in tumor tissues,and rBCG significantly increased proportion of B cells in tumor.BCG immunotherapy upregulated transcription levels of metastasis-related cytokines,while rBCG therapy had no effects on transcriptions of these genes.Conclusion:Both BCG and rBCG have immunotherapeutic effects on melanotic mice,and rBCG with c-di-AMP as adjuvant shows better inhibition on tumor metastasis than BCG,which mechanism was related to regulation of immune response in tumor tissues.

Objective:To investigate immunotherapy effects and mechanism of BCG and recombinant BCG(rBCG)with c-di-AMP as adjuvant on melanoma in mice model.Methods:Melanoma mice model was established by B16F10 cell subcutaneous injec-tion in groin,and treated with 1×106 CFU of BCG and rBCG by adjacent injection of subcutaneous tumor for 3 times,respectively.Survival of melanotic mice,tumor growth and metastasis were observed.Tumor tissues of mice were isolated to prepare cell suspen-sion,and proportion of immune cells were detected by flow cytometry.Transcriptional levels of immune-related genes in tumor tissues were detected by qRT-PCR.Results:Both BCG and rBCG immunotherapy could significantly inhibit growth in melanoma mice and prolong survival time of mice.rBCG showed better inhibition on metastasis than BCG.Both strains significantly reduced proportion of M2-type macrophages and myeloid-derived suppressor cell associated with tumor growth and metastasis.Both two strains promoted infiltration of lymphocytes in tumor tissues,and rBCG significantly increased proportion of B cells in tumor.BCG immunotherapy upregulated transcription levels of metastasis-related cytokines,while rBCG therapy had no effects on transcriptions of these genes.Conclusion:Both BCG and rBCG have immunotherapeutic effects on melanotic mice,and rBCG with c-di-AMP as adjuvant shows better inhibition on tumor metastasis than BCG,which mechanism was related to regulation of immune response in tumor tissues.

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.

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.