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Qingxia Yang et al.
Summary: Multiclass metabolomics is widely used in clinical practice for understanding disease progression and identifying diagnostic biomarkers. It is more challenging than the binary problem due to the complexity of determining class decision boundaries. However, there is still a lack of a systematic assessment for selecting appropriate methods in multiclass metabolomics.
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Hongning Zhang et al.
Summary: SoCube is a deep learning algorithm that accurately detects doublets in various types of scRNA-seq data, with excellent performance and wide applicability.
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Shuiping Liu et al.
Summary: As the most prevalent internal modification in eukaryotic RNAs, N-6-methyladenosine (m(6)A) has been discovered to play an essential role in cellular proliferation, metabolic homeostasis, embryonic development, etc. A new database named 'M6AREG' is developed to systematically cover, for the first time, data on the effects of m(6)A-centered regulation on both disease development and drug response, and explicitly describe the molecular mechanism underlying each type of regulation.
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Biochemistry & Molecular Biology
Xiuna Sun et al.
Summary: Drug resistance has become a major global healthcare issue. This study introduces a comprehensive database called 'DRESIS' that provides information on all types of drug resistance mechanisms and covers a wide range of diseases. The database includes extensively validated resistance data, making it a valuable resource for future drug discovery and clinical treatment optimization.
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Biochemistry & Molecular Biology
Fengcheng Li et al.
Summary: The efficacy and safety of drugs depend on their interactions with various pharmacologically important molecules. To better understand this information, a new database called DrugMAP has been developed, which provides a comprehensive list of interacting molecules for over 30,000 drugs/drug candidates. It also includes differential expression patterns for over 5,000 interacting molecules in different disease sites, ADME-relevant organs, and physiological tissues. With over 200,000 interactions among drugs and molecules, DrugMAP is expected to be a valuable resource for drug discovery and network analysis.
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Biochemistry & Molecular Biology
Kuerbannisha Amahong et al.
Summary: This study constructed a new database (CovInter) to describe the interactions between coronavirus RNAs and host proteins. The unique feature of this database lies in the explicit characterization of these interactions and the comprehensive provision of experimentally validated biological functions for important host proteins, as well as the systematic quantification of differential expression patterns of these key proteins before and after infection.
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Yunxia Wang et al.
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Biochemical Research Methods
Yongchao Luo et al.
Summary: Polypharmacology is a promising approach for improving drug efficacy in complex diseases. In silico strategies are essential for high throughput and cost-effective discovery of novel multi-target drugs (MTDs). However, most current research focuses on closely related target pairs, while distantly related targets also play crucial roles in synergistic treatment. Developing drugs that can target distantly related target pairs simultaneously is of utmost importance, as well as reducing the false discovery rate in MTD design.
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Weiwei Xue et al.
Summary: This study explored the allosteric modulation mechanism of hSERT using two MD simulation strategies. The results showed that escitalopram enhanced its binding to the orthosteric site by binding to the allosteric site. Additionally, escitalopram occupying the allosteric site also blocked its dissociation from the orthosteric site. A new conformational coupling between extracellular and intracellular salt bridges was also identified. These findings are important for understanding the allosteric modulations of not only hSERT but also other clinically important therapeutic targets.
ACS CHEMICAL NEUROSCIENCE
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Biochemical Research Methods
Song Zhang et al.
Summary: This study demonstrates that the genomic RNA of SARS-CoV-2 can absorb host miRNAs that regulate immune-related genes, suggesting that this may be a crucial mechanism for the virus to survive and develop in the host. By analyzing miRNA datasets and identifying enriched immune-related pathways and protein-protein interactions, the study reveals an imbalance in the host immune response caused by the absorption of SARS-CoV-2 to host miRNAs, with implications for infection and pathogenesis. Additionally, the study finds that the absorption of host miRNAs by SARS-CoV-2 may contribute to the increased infectivity and pathogenicity of certain mutant strains.
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Biochemical Research Methods
Fengcheng Li et al.
Summary: Mass spectrometry-based proteomic technique is essential in studying biological processes. However, current statistical frameworks neglect the reproducibility among identified features. Thus, developing a tool to identify reproducible and generalizable proteomic signatures is crucial.
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Biochemical Research Methods
Phasit Charoenkwan et al.
Summary: The development of efficient bioinformatics tools for identifying DPP-IV inhibitory peptides is crucial for discovering potential antidiabetic drugs. This study introduces a novel stacking-based ensemble learning predictor, StackDPPIV, which outperforms existing methods by combining different feature encodings and machine learning algorithms. Experimental results demonstrate the superior predictive performance of StackDPPIV on both training and independent datasets.
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Biochemical Research Methods
Jianbo Fu et al.
Summary: A peak table is a typical output of metabolomic experiments, and its processing is crucial in metabolomics research. The NOREVA protocol, a newly developed software tool, evaluates and optimizes data processing through various workflows, making data processing more efficient.
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Biochemistry & Molecular Biology
Xiaona Wang et al.
Summary: The SYNBIP database provides comprehensive information on thousands of SBPs, illustrating their scaffolds, biophysical and functional properties, binding targets and applications, as well as enabling similarity search. This database serves as a valuable resource for future development of novel SBPs through experimental protein engineering and in-silico studies. The SYNBIP database is accessible without login requirement at both official and mirror sites.
NUCLEIC ACIDS RESEARCH
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Biochemistry & Molecular Biology
Tingting Fu et al.
Summary: This study describes a major update of the VARIDT database, including experimental resolved structures and structural variability data updates through literature review and homology modeling. The newly collected data are essential for explaining drug sensitivity, revealing drug-drug interaction mechanisms, and more.
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Jonathan S. Abebe et al.
Summary: The study introduces a new method called DRUMMER for detecting RNA modifications. This method combines statistical tests and noise correction, resulting in high accuracy and sensitivity. It also shows good correlation with other methods.
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Biology
Weiqi Xia et al.
Summary: This study presents a novel protein function annotation strategy, PFmulDL, which integrates multiple deep learning methods to address the limitations of existing methods in annotating proteins in "rare classes". The new model is capable of annotating a larger number of protein families and significantly improving prediction performance.
COMPUTERS IN BIOLOGY AND MEDICINE
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Multidisciplinary Sciences
Phasit Charoenkwan et al.
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SCIENTIFIC REPORTS
(2022)
Review
Chemistry, Medicinal
Minjie Mou et al.
Summary: Spatial proteomics is an interdisciplinary field studying the localization and dynamics of proteins, with a particular focus on subcellular proteomics. Machine learning methods have been widely used to analyze spatial proteomic data obtained through mass spectrometry and imaging-based approaches. This review comprehensively surveys the applications of machine learning in spatial proteomics, including data resources, algorithms, successful applications, and challenges. It provides guidance for researchers and contributes to cell biology research, medical advancements, and drug discovery.
JOURNAL OF CHEMICAL INFORMATION AND MODELING
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Fengcheng Li et al.
Summary: The discovery of proper molecular signature from OMIC data is crucial for determining biological state, physiological condition, disease etiology, and therapeutic response. However, the identified signature is highly inconsistent and shows little overlap among different biological datasets, hindering its reliability and applications. To address this issue, researchers developed an online tool called ConSIG that utilizes a novel strategy to enhance consistency, determines the optimal signature through collective assessment, and confirms biological relevance through disease/gene ontology enrichment.
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Hanyu Zhang et al.
Summary: Recent studies have shown the significant role of non-coding RNA (ncRNA), particularly lncRNA and miRNA interactions, in biological activities. However, existing methods for identifying novel lncRNA-miRNA interactions have room for improvement in their RNA representation and information extraction approaches. This study proposed a novel method called ncRNAInter, which utilized a comprehensive strategy for RNA representation and an optimized deep learning algorithm to predict lncRNA-miRNA interactions. ncRNAInter showed improved performance compared to existing methods and demonstrated its applicability in different species and disease contexts.
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Qingxia Yang et al.
Summary: Large-scale metabolomics is a powerful technique used for identifying biomarkers and interpreting the mechanisms of complex diseases. The analysis of metabolomic data remains challenging, but this study developed the LargeMetabo package that can integrate multiple experiments, select appropriate biomarker identification methods, and provide metabolite annotation and enrichment analysis, improving flexibility and reproducibility in large-scale metabolomics.
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Song Zhang et al.
Summary: Multiomics is a powerful technique in molecular biology that helps identify new associations among different molecules. The REGLIV database provides validated molecular regulation data from living systems, which has important implications for research and applications.
COMPUTERS IN BIOLOGY AND MEDICINE
(2022)
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Zhengtao Luo et al.
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INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES
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Jianbo Fu et al.
Summary: Label-free quantification (LFQ) is an important technique in proteomics due to its broad proteome coverage and enhanced reproducibility. In this study, an R package EVALFQ was developed to evaluate and discover well-performing LFQ chains from over 3000 options.
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Jing Tang et al.
Summary: Metaproteomics faces challenges of dimensionality and sparsity, and data reduction methods are crucial for identifying significant features and reducing redundancy. The performance of feature selection methods depends on data characteristics, and the online tool MetaFS offers a variety of FS methods for evaluating potential biomarkers in microbiome studies through comprehensive criteria.
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Jiayi Yin et al.
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Yanrong Ji et al.
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Song Zhang et al.
Summary: This article summarizes the role of miRNA in COVID-19 infection and suggests that COVID-19 cleverly exploits the interaction between miRNA and other biomolecules to evade host immune response. The study proposes that the miRNA-based interplay between the host and SARS-CoV-2 may be the primary cause of SARS-CoV-2 accessing and attacking host cells.
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