Journal of Computational Biology最新文献

{"title":"Evaluating Protein Language Model Embeddings for Viral Clade Assignment.","authors":"Brendonas Stakauskas, Virginijus MarcinkeviČius","doi":"10.1177/15578666261443336","DOIUrl":"10.1177/15578666261443336","url":null,"abstract":"<p><p>Protein language models (PLMs) provide powerful sequence representations, yet their effectiveness for unsupervised viral clade assignment remains uncertain. In this study, we evaluated embeddings from ProtT5, ProtBert, CARP, and several ESM-2 variants on influenza A/H3N2 hemagglutinin sequences. Using dimensionality reduction (t-SNE, UMAP, PCA, MDS) and clustering with HDBSCAN, we compared PLM embeddings against baseline Hamming distance approaches. Our results show that t-SNE combined with PLM embeddings can recover clade structure, with ProtBert yielding the most stable performance and larger ESM-2 models occasionally achieving lower normalized variation of information scores but with greater variability. These findings suggest that while PLM embeddings capture clade-relevant signals, they also suffer from instability and the loss of site- or nucleotide-specific detail. Future improvements in pooling strategies may enhance their utility for viral surveillance.</p>","PeriodicalId":15526,"journal":{"name":"Journal of Computational Biology","volume":" ","pages":"733-743"},"PeriodicalIF":1.6,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147729237","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Investigations on Multiple Protein Scaffold Filling.","authors":"Ismoiljon Muzaffarov, Xiaowen Liu, Letu Qingge, Lusheng Wang, Binhai Zhu","doi":"10.1177/15578666261457452","DOIUrl":"https://doi.org/10.1177/15578666261457452","url":null,"abstract":"<p><p>In this article, we initiate the study on some problems related to multiple protein scaffold filling, with or without references. The objective is to maximize the sum of Blosum62 scores of the filled sequences when no reference is given, or to maximize the Blosum62 score between the filled sequence and a reference. We present the following results: (1) given <i>n</i> scaffolds generated from the top-down tandem mass spectra, finding <i>k</i> scaffolds whose corresponding contents can be used to fill into a target sequence (or a sequence whose Blosum62 score with a reference is maximized) takes <math><mrow><mi>Ω</mi><mo>(</mo><mrow><msup><mrow><mi>n</mi></mrow><mrow><mi>k</mi><mo>-</mo><mi>ε</mi></mrow></msup></mrow><mo>)</mo></mrow></math> time, unless the Strong Exponential Time Hypothesis (SETH) fails. (2) Given two or more protein scaffolds and the corresponding multisets of amino acids to be filled accordingly, the corresponding optimization problem can be solved in polynomial time with dynamic programming. (3) Due to the high (and impractical) running times of these algorithms, we implement several heuristic algorithms for the special cases when three scaffolds are given. The corresponding empirical results are quite promising, from the testing of 12 datasets spanning five different kinds of proteins: antibody, myoglobin, mitochondrial respiratory-chain, calmodulin, and thioredoxin.</p>","PeriodicalId":15526,"journal":{"name":"Journal of Computational Biology","volume":" ","pages":"15578666261457452"},"PeriodicalIF":1.3,"publicationDate":"2026-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148277611","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"FDS-CAP: Modeling Fragmented Disease Subgraphs with Component-Level Attention for Comorbidity Prediction.","authors":"Ashwag Altayyar, Li Liao","doi":"10.1177/15578666261427466","DOIUrl":"10.1177/15578666261427466","url":null,"abstract":"<p><p>Understanding comorbidity between human diseases is essential for uncovering shared pathophysiological mechanisms and improving diagnostic and therapeutic strategies. Although prior studies have investigated genetic and network-based disease associations, they often overlook the fragmented nature of disease modules within the human interactome. To address this limitation, we introduce Fragmented Disease Subgraphs with Component-Level Attention for Comorbidity Prediction (FDS-CAP), a novel graph-based deep learning framework. FDS-CAP first embeds fragmented disease subgraphs using Subgraph Neural Networks (SUBGNN) with component-level attention, then applies a variational comorbidity predictor built upon a Variational Graph Auto-Encoder that is used to predict comorbid disease associations within the Human Disease Network. SUBGNN encodes disease subgraphs by propagating information at the connected component level across three property-aware channels-capturing positional, neighborhood, and structural roles-and integrates a component-level attention mechanism that weighs each connected component based on its significance to the overall subgraph representation. A core contribution of our method is the attention-based aggregation of connected component embeddings, enabling more accurate and expressive disease representations that reflect the biological complexity in fragmented disease subgraphs for improved comorbidity prediction. FDS-CAP achieves state-of-the-art performance for comorbidity prediction on a benchmark dataset, with an AUROC of 0.966. We further illustrate its biological interpretability through a single representative case study on glioma, showing that attention-weighted subgraph components capture meaningful patterns associated with disease mechanisms.</p>","PeriodicalId":15526,"journal":{"name":"Journal of Computational Biology","volume":" ","pages":"611-623"},"PeriodicalIF":1.3,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147512551","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Random Projection Methods Outperform Principal Component Analysis for Dimensionality Reduction in Single Cell RNA-Seq.","authors":"Mohamed Abdelnaby, Marmar R Moussa","doi":"10.1177/15578666261436821","DOIUrl":"10.1177/15578666261436821","url":null,"abstract":"<p><p>Principal component analysis (PCA) is one of the most frequently used dimensionality reduction methods for high-dimensional datasets, especially single-cell RNA sequencing (scRNA-seq). Despite its popularity, PCA faces challenges, particularly related to its performance degrading as the dataset size increases. Additionally, PCA is sensitive to outliers and assumes linearity. Random projection (RP) methods have emerged as a promising alternative to address several of PCA's limitations. In this study, we conduct a systematic and comprehensive evaluation of PCA and RP methods, including singular value decomposition (SVD) and randomized SVD approaches, against multiple RP methods including sparse random projection, Gaussian random projection, and we introduce a Matching Sparsity Random Projection algorithm that adaptively calibrates projection matrix density according to input data sparsity patterns, emphasizing both computational scalability and effectiveness in downstream analytical tasks. We evaluated these methods on multiple publicly available scRNA-seq datasets that include both labeled and unlabeled scenarios. Clustering performance is assessed using Hierarchical Clustering and Spherical K-Means algorithms, with labeled datasets evaluated through Hungarian algorithm accuracy and Mutual Information metrics. For unlabeled datasets, we used the Dunn Index and Gap Statistic to quantify cluster separation quality. Across both dataset types, the Within-Cluster Sum of Squares metric is used to assess variability. Moreover, locality preservation is examined, with RP methods, including our adaptive sparsity approach, outperforming PCA in several of the evaluated metrics. Our experimental results show that RP methods not only deliver substantial computational speed improvements over PCA but also rival, and in some cases, exceed PCA in preserving data variability and clustering quality. Through this comprehensive methodological comparison, our work provides critical guidance for selecting appropriate dimensionality reduction strategies that effectively balance computational demands, scalability requirements, and analytical quality in downstream analyses.</p>","PeriodicalId":15526,"journal":{"name":"Journal of Computational Biology","volume":" ","pages":"660-679"},"PeriodicalIF":1.3,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13535611/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147772888","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"<i>Special Issue:</i> 13th International Conference on Computational Advances in Bio and Medical Sciences.","authors":"Mohammed Alser, Mukul S Bansal, Yury Khudyakov, Serghei Mangul, Ion I Mandoiu, Marmar R Moussa, Murray Patterson, Sanguthevar Rajasekaran, Pavel Skums, Shibu Yooseph, Alexander Zelikovsky","doi":"10.1177/15578666261451662","DOIUrl":"10.1177/15578666261451662","url":null,"abstract":"","PeriodicalId":15526,"journal":{"name":"Journal of Computational Biology","volume":" ","pages":"573-574"},"PeriodicalIF":1.3,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13535539/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147930030","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Fast Hashing of Spaced Seeds with DuoHash.","authors":"Leonardo Gemin, Cinzia Pizzi, Matteo Comin","doi":"10.1177/15578666261423555","DOIUrl":"10.1177/15578666261423555","url":null,"abstract":"<p><p>Many state-of-the-art tools for sequence analysis are based on alignment-free techniques to manage high-throughput processing. Several routine tasks such as querying, indexing, and similarity search are based on <i>k</i>-mer statistics. In order to accommodate errors or mutations, spaced seeds have been increasingly used instead of <i>k</i>-mers, enhancing sensitivity in various applications. However, spaced seed hashing is computationally intensive, introducing significant slowdown in the processing.This article addresses the challenge of efficient spaced seed hashing, which is functional for the computation of spaced <i>k</i>-mers counting. We present DuoHash, a framework that enables the efficient computation of hash functions for spaced seeds. DuoHash exploits an efficient spaced seed binary encoding and precomputed tables to speedup the computation of the hash value for both the forward and reverse strands of a DNA sequence. In our experiments, DuoHash substantially outperforms existing algorithms, achieving speedups of up to 11x on short reads with a spaced seed of medium density. Furthermore, we show the applicability of DuoHash to the problem of spaced <i>k</i>-mers counting. The code of DuoHash is available at https://github.com/CominLab/DuoHash/.</p>","PeriodicalId":15526,"journal":{"name":"Journal of Computational Biology","volume":" ","pages":"593-610"},"PeriodicalIF":1.3,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147574217","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"USTAR-CR: Efficient and Compact Compression of <i>k</i>-Mer Sets Through Colored de Bruijn Graphs.","authors":"Enrico Rossignolo, Matteo Comin","doi":"10.1177/15578666261416557","DOIUrl":"10.1177/15578666261416557","url":null,"abstract":"<p><p>A core task in computational genomics is transforming input sequences into their constituent <i>k</i>-mers. Efficiently storing these <i>k</i>-mer collections is crucial for scaling bioinformatics workflows. A common strategy involves representing the <i>k</i>-mers as a de Bruijn graph (dBG) and deriving a compact plain text form through a minimum path cover. In this article, we introduce USTAR-CR (Unitig STitch Advanced constRuction with Colors Reordering), a fast and space-efficient algorithm for compressing multiple <i>k</i>-mer sets. USTAR-CR exploits the structural properties of colored dBGs to construct a succinct plain text representation while also incorporating an effective scheme for encoding <i>k</i>-mer color information. We evaluate USTAR-CR on real sequencing datasets and benchmark it against the state-of-the-art tool GGCAT. USTAR-CR achieves superior compression ratios, significantly reduces memory usage, and offers substantial speed improvements-up to 64× faster-highlighting its effectiveness for large-scale genomic data processing.</p>","PeriodicalId":15526,"journal":{"name":"Journal of Computational Biology","volume":" ","pages":"575-592"},"PeriodicalIF":1.3,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146085938","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhancing Clinical Note Generation with ICD-10, Clinical Ontology Knowledge Graphs, and Chain-of-Thought Prompting Using GPT-4.","authors":"Ivan Makohon, Mohamad Najafi, Jian Wu, Mathias Brochhausen, Yaohang Li","doi":"10.1177/15578666251411390","DOIUrl":"10.1177/15578666251411390","url":null,"abstract":"<p><p>In the past decade, a surge in the amount of electronic health record (EHR) data in the United States has been attributed to a favorable policy environment created by the Health Information Technology for Economic and Clinical Health (HITECH) Act of 2009 and the 21st Century Cures Act of 2016. Clinical notes for patients' assessments, diagnoses, and treatments are captured in these EHRs in free-form text by physicians, who spend a considerable amount of time entering and editing them. Manually writing clinical notes takes a considerable amount of a doctor's valuable time, increasing the patient's waiting time and possibly delaying diagnoses. Large language models (LLMs) possess the ability to generate news articles that closely resemble human-written ones. We investigate the usage of Chain-of-Thought (CoT) prompt engineering to improve the LLM's response in clinical note generation. In our prompts, we use as input International Classification of Diseases codes and basic patient information. We investigate a strategy that combines the traditional CoT with semantic search results to improve the quality of generated clinical notes. Additionally, we infuse a knowledge graph built from clinical ontology to further enrich the domain-specific knowledge of generated clinical notes. We test our prompting technique on six clinical cases from the CodiEsp test dataset using GPT-4, and our results show that it outperformed the clinical notes generated by standard one-shot prompts.</p>","PeriodicalId":15526,"journal":{"name":"Journal of Computational Biology","volume":"33 5-6","pages":"624-642"},"PeriodicalIF":1.3,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148030798","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"MEditome: Computational Detection of RNA Edit Sites Using <i>de Novo</i> Assembly in Microbiomes.","authors":"Arpit Mehta, Vitalii Stebliankin, Kalai Mathee, Giri Narasimhan","doi":"10.1177/15578666261428562","DOIUrl":"10.1177/15578666261428562","url":null,"abstract":"<p><p>RNA editing is a post-transcriptional modification that alters single-nucleotide sites within RNA strands, thus diversifying transcriptomes and proteomes and modulating gene expression. While better characterized in eukaryotes and in a few microbes, the study of RNA editing in entire microbiomes remains unexplored. Recent studies have demonstrated that A-to-I RNA editing contributes to bacterial adaptation and pathogenicity. Previously, we developed MetaEdit, a reference-based computational pipeline to detect RNA edit sites in microbiomes. While MetaEdit successfully identified RNA edit sites in <i>Escherichia coli</i> within the context of the human gut microbiome, including previously reported loci, it relied primarily on aligning reads to reference genomes of target bacteria. This dependence on reference genomes introduced potential biases, as editing can only be identified in reference genomes, while editing in novel microbial strains missing from the reference databases could be overlooked. Even for reference genomes, the search for edit sites is inefficient since it would have to be conducted one reference genome at a time.Here, we introduce MEditome, employing <i>de novo</i> assembly to overcome these limitations. This crucial change enables the detection of RNA edit sites across all microbial organisms in the microbiome, including novel bacterial strains for which comprehensive reference genomes are unavailable. Using sequencing data from the Integrative Human Microbiome Project, MEditome identified 2,295 unique RNA editing sites across diverse bacterial taxa. Several of these overlaps with previously identified edits in <i>E. coli</i> detected by MetaEdit in <i>hok/gef</i> gene family and arginine-associated genes, providing <i>in silico</i> validation of accuracy. We observed taxon-specific editing patterns and gene-level differential editing associated with inflammatory bowel disease, highlighting RNA editing as a potential regulatory mechanism influencing microbial adaptation and host-microbe interactions.</p>","PeriodicalId":15526,"journal":{"name":"Journal of Computational Biology","volume":" ","pages":"643-659"},"PeriodicalIF":1.3,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147499261","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Assessment of PHRED Score Characteristics in Illumina MiSeq Amplicon Sequencing.","authors":"Seth Sims, Yury Khudyakov, Alexander Zelikovsky","doi":"10.1177/15578666261436820","DOIUrl":"10.1177/15578666261436820","url":null,"abstract":"<p><p>PHRED scores are confidence values associated with each basecall generated by sequencers. The score is defined as a monotonic function of the probability that the basecall is incorrect. The calibration of PHRED scores has previously been examined by evaluating errors made in reading known sequences. We investigated the calibration of the Illumina MiSeq instrument PHRED model using data from a large dataset. We also derive calibration methods for the PHRED scores in datasets similar to those produced by the Global Hepatitis Outbreak and Surveillance Technology (GHOST). The GHOST protocol uses a short amplicon, resulting in many positions having two base calls, one coming from each of the paired reads. A maximum likelihood model of redundant base calls that match each other was used to estimate corrected probabilities of the PHRED scores. The PHRED scores showed only small absolute deviations from their target values. These differences are statistically significant deviations (<math><mrow><mi>p</mi><mo> </mo><mo><</mo><mo> </mo><mn>0.0001</mn></mrow></math>) from being calibrated. The accuracy of the scores varied significantly between the MiSeq instrument runs. Recalibration produced quality scores that improved Brier scores for the dataset by an average relative improvement of <math><mrow><mn>2.83</mn><mi>%</mi></mrow></math>. Methods developed to create calibration curves for PHRED scores will be useful in improving error-correction pipelines based on redundant deep sequencing of amplicon data. However, quality scores are relatively uninformative of substitution errors. The quality scores assigned are determined more by the global error rate of the sequencing run in the current machine cycle than by the characteristics of the specific base call.</p>","PeriodicalId":15526,"journal":{"name":"Journal of Computational Biology","volume":" ","pages":"705-715"},"PeriodicalIF":1.3,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13051658/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147574222","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}