arXiv - QuanBio - Genomics最新文献_第7页

DYNA: Disease-Specific Language Model for Variant Pathogenicity DYNA：变异致病性疾病特异性语言模型

arXiv - QuanBio - Genomics Pub Date : 2024-05-31 DOI: arxiv-2406.00164

Huixin Zhan, Zijun Zhang

{"title":"DYNA: Disease-Specific Language Model for Variant Pathogenicity","authors":"Huixin Zhan, Zijun Zhang","doi":"arxiv-2406.00164","DOIUrl":"https://doi.org/arxiv-2406.00164","url":null,"abstract":"Clinical variant classification of pathogenic versus benign genetic variants\u0000remains a challenge in clinical genetics. Recently, the proposition of genomic\u0000foundation models has improved the generic variant effect prediction (VEP)\u0000accuracy via weakly-supervised or unsupervised training. However, these VEPs\u0000are not disease-specific, limiting their adaptation at the point of care. To\u0000address this problem, we propose DYNA: Disease-specificity fine-tuning via a\u0000Siamese neural network broadly applicable to all genomic foundation models for\u0000more effective variant effect predictions in disease-specific contexts. We\u0000evaluate DYNA in two distinct disease-relevant tasks. For coding VEPs, we focus\u0000on various cardiovascular diseases, where gene-disease relationships of\u0000loss-of-function vs. gain-of-function dictate disease-specific VEP. For\u0000non-coding VEPs, we apply DYNA to an essential post-transcriptional regulatory\u0000axis of RNA splicing, the most common non-coding pathogenic mechanism in\u0000established clinical VEP guidelines. In both cases, DYNA fine-tunes various\u0000pre-trained genomic foundation models on small, rare variant sets. The DYNA\u0000fine-tuned models show superior performance in the held-out rare variant\u0000testing set and are further replicated in large, clinically-relevant variant\u0000annotations in ClinVAR. Thus, DYNA offers a potent disease-specific variant\u0000effect prediction method, excelling in intra-gene generalization and\u0000generalization to unseen genetic variants, making it particularly valuable for\u0000disease associations and clinical applicability.","PeriodicalId":501070,"journal":{"name":"arXiv - QuanBio - Genomics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141257748","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A SARS-CoV-2 Interaction Dataset and VHH Sequence Corpus for Antibody Language Models 用于抗体语言模型的 SARS-CoV-2 相互作用数据集和 VHH 序列语料库

arXiv - QuanBio - Genomics Pub Date : 2024-05-29 DOI: arxiv-2405.18749

Hirofumi Tsuruta, Hiroyuki Yamazaki, Ryota Maeda, Ryotaro Tamura, Akihiro Imura

{"title":"A SARS-CoV-2 Interaction Dataset and VHH Sequence Corpus for Antibody Language Models","authors":"Hirofumi Tsuruta, Hiroyuki Yamazaki, Ryota Maeda, Ryotaro Tamura, Akihiro Imura","doi":"arxiv-2405.18749","DOIUrl":"https://doi.org/arxiv-2405.18749","url":null,"abstract":"Antibodies are crucial proteins produced by the immune system to eliminate\u0000harmful foreign substances and have become pivotal therapeutic agents for\u0000treating human diseases. To accelerate the discovery of antibody therapeutics,\u0000there is growing interest in constructing language models using antibody\u0000sequences. However, the applicability of pre-trained language models for\u0000antibody discovery has not been thoroughly evaluated due to the scarcity of\u0000labeled datasets. To overcome these limitations, we introduce AVIDa-SARS-CoV-2,\u0000a dataset featuring the antigen-variable domain of heavy chain of heavy chain\u0000antibody (VHH) interactions obtained from two alpacas immunized with severe\u0000acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike proteins.\u0000AVIDa-SARS-CoV-2 includes binary labels indicating the binding or non-binding\u0000of diverse VHH sequences to 12 SARS-CoV-2 mutants, such as the Delta and\u0000Omicron variants. Furthermore, we release VHHCorpus-2M, a pre-training dataset\u0000for antibody language models, containing over two million VHH sequences. We\u0000report benchmark results for predicting SARS-CoV-2-VHH binding using VHHBERT\u0000pre-trained on VHHCorpus-2M and existing general protein and antibody-specific\u0000pre-trained language models. These results confirm that AVIDa-SARS-CoV-2\u0000provides valuable benchmarks for evaluating the representation capabilities of\u0000antibody language models for binding prediction, thereby facilitating the\u0000development of AI-driven antibody discovery. The datasets are available at\u0000https://datasets.cognanous.com.","PeriodicalId":501070,"journal":{"name":"arXiv - QuanBio - Genomics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141194172","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Metadata-guided Feature Disentanglement for Functional Genomics 元数据指导下的功能基因组学特征分解

arXiv - QuanBio - Genomics Pub Date : 2024-05-29 DOI: arxiv-2405.19057

Alexander Rakowski, Remo Monti, Viktoriia Huryn, Marta Lemanczyk, Uwe Ohler, Christoph Lippert

{"title":"Metadata-guided Feature Disentanglement for Functional Genomics","authors":"Alexander Rakowski, Remo Monti, Viktoriia Huryn, Marta Lemanczyk, Uwe Ohler, Christoph Lippert","doi":"arxiv-2405.19057","DOIUrl":"https://doi.org/arxiv-2405.19057","url":null,"abstract":"With the development of high-throughput technologies, genomics datasets\u0000rapidly grow in size, including functional genomics data. This has allowed the\u0000training of large Deep Learning (DL) models to predict epigenetic readouts,\u0000such as protein binding or histone modifications, from genome sequences.\u0000However, large dataset sizes come at a price of data consistency, often\u0000aggregating results from a large number of studies, conducted under varying\u0000experimental conditions. While data from large-scale consortia are useful as\u0000they allow studying the effects of different biological conditions, they can\u0000also contain unwanted biases from confounding experimental factors. Here, we\u0000introduce Metadata-guided Feature Disentanglement (MFD) - an approach that\u0000allows disentangling biologically relevant features from potential technical\u0000biases. MFD incorporates target metadata into model training, by conditioning\u0000weights of the model output layer on different experimental factors. It then\u0000separates the factors into disjoint groups and enforces independence of the\u0000corresponding feature subspaces with an adversarially learned penalty. We show\u0000that the metadata-driven disentanglement approach allows for better model\u0000introspection, by connecting latent features to experimental factors, without\u0000compromising, or even improving performance in downstream tasks, such as\u0000enhancer prediction, or genetic variant discovery. The code for our\u0000implemementation is available at https://github.com/HealthML/MFD","PeriodicalId":501070,"journal":{"name":"arXiv - QuanBio - Genomics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141194101","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

CAVACHON: a hierarchical variational autoencoder to integrate multi-modal single-cell data CAVACHON：用于整合多模态单细胞数据的分层变异自动编码器

arXiv - QuanBio - Genomics Pub Date : 2024-05-28 DOI: arxiv-2405.18655

Ping-Han Hsieh, Ru-Xiu Hsiao, Katalin Ferenc, Anthony Mathelier, Rebekka Burkholz, Chien-Yu Chen, Geir Kjetil Sandve, Tatiana Belova, Marieke Lydia Kuijjer

{"title":"CAVACHON: a hierarchical variational autoencoder to integrate multi-modal single-cell data","authors":"Ping-Han Hsieh, Ru-Xiu Hsiao, Katalin Ferenc, Anthony Mathelier, Rebekka Burkholz, Chien-Yu Chen, Geir Kjetil Sandve, Tatiana Belova, Marieke Lydia Kuijjer","doi":"arxiv-2405.18655","DOIUrl":"https://doi.org/arxiv-2405.18655","url":null,"abstract":"Paired single-cell sequencing technologies enable the simultaneous\u0000measurement of complementary modalities of molecular data at single-cell\u0000resolution. Along with the advances in these technologies, many methods based\u0000on variational autoencoders have been developed to integrate these data.\u0000However, these methods do not explicitly incorporate prior biological\u0000relationships between the data modalities, which could significantly enhance\u0000modeling and interpretation. We propose a novel probabilistic learning\u0000framework that explicitly incorporates conditional independence relationships\u0000between multi-modal data as a directed acyclic graph using a generalized\u0000hierarchical variational autoencoder. We demonstrate the versatility of our\u0000framework across various applications pertinent to single-cell multi-omics data\u0000integration. These include the isolation of common and distinct information\u0000from different modalities, modality-specific differential analysis, and\u0000integrated cell clustering. We anticipate that the proposed framework can\u0000facilitate the construction of highly flexible graphical models that can\u0000capture the complexities of biological hypotheses and unravel the connections\u0000between different biological data types, such as different modalities of paired\u0000single-cell multi-omics data. The implementation of the proposed framework can\u0000be found in the repository https://github.com/kuijjerlab/CAVACHON.","PeriodicalId":501070,"journal":{"name":"arXiv - QuanBio - Genomics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141194173","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Range-Limited Heaps' Law for Functional DNA Words in the Human Genome 人类基因组中功能 DNA 词的范围限制希普斯定律

arXiv - QuanBio - Genomics Pub Date : 2024-05-22 DOI: arxiv-2405.13825

Wentian Li, Yannis Almirantis, Astero Provata

{"title":"Range-Limited Heaps' Law for Functional DNA Words in the Human Genome","authors":"Wentian Li, Yannis Almirantis, Astero Provata","doi":"arxiv-2405.13825","DOIUrl":"https://doi.org/arxiv-2405.13825","url":null,"abstract":"Heaps' or Herdan's law is a linguistic law describing the relationship\u0000between the vocabulary/dictionary size (type) and word counts (token) to be a\u0000power-law function. Its existence in genomes with certain definition of DNA\u0000words is unclear partly because the dictionary size in genome could be much\u0000smaller than that in a human language. We define a DNA word in a genome as a\u0000DNA coding region that codes for a protein domain. Using human chromosomes and\u0000chromosome arms as individual samples, we establish the existence of Heaps' law\u0000in the human genome within limited range. Our definition of words in a genomic\u0000or proteomic context is different from that in large language models for DNA or\u0000protein sequences where words are usually short. Although an approximate\u0000power-law distribution of protein domain sizes due to gene duplication and the\u0000related Zipf's law is well known, their translation to the Heaps' law in DNA\u0000words is not automatic. Several other animal genomes are shown herein also to\u0000exhibit range-limited Heaps' law with our definition of DNA words, though with\u0000various exponents, partially depending on their level of complexity.\u0000Investigation of Heaps' law and its exponent value could provide an alternative\u0000narrative of reusage and redundancy of protein domains as well as creation of\u0000new protein domains from a linguistic perspective.","PeriodicalId":501070,"journal":{"name":"arXiv - QuanBio - Genomics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141152918","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Accurate and efficient protein embedding using multi-teacher distillation learning 利用多教师蒸馏学习实现准确高效的蛋白质嵌入

arXiv - QuanBio - Genomics Pub Date : 2024-05-20 DOI: arxiv-2405.11735

Jiayu Shang, Cheng Peng, Yongxin Ji, Jiaojiao Guan, Dehan Cai, Xubo Tang, Yanni Sun

{"title":"Accurate and efficient protein embedding using multi-teacher distillation learning","authors":"Jiayu Shang, Cheng Peng, Yongxin Ji, Jiaojiao Guan, Dehan Cai, Xubo Tang, Yanni Sun","doi":"arxiv-2405.11735","DOIUrl":"https://doi.org/arxiv-2405.11735","url":null,"abstract":"Motivation: Protein embedding, which represents proteins as numerical\u0000vectors, is a crucial step in various learning-based protein\u0000annotation/classification problems, including gene ontology prediction,\u0000protein-protein interaction prediction, and protein structure prediction.\u0000However, existing protein embedding methods are often computationally expensive\u0000due to their large number of parameters, which can reach millions or even\u0000billions. The growing availability of large-scale protein datasets and the need\u0000for efficient analysis tools have created a pressing demand for efficient\u0000protein embedding methods. Results: We propose a novel protein embedding approach based on multi-teacher\u0000distillation learning, which leverages the knowledge of multiple pre-trained\u0000protein embedding models to learn a compact and informative representation of\u0000proteins. Our method achieves comparable performance to state-of-the-art\u0000methods while significantly reducing computational costs and resource\u0000requirements. Specifically, our approach reduces computational time by ~70%\u0000and maintains almost the same accuracy as the original large models. This makes\u0000our method well-suited for large-scale protein analysis and enables the\u0000bioinformatics community to perform protein embedding tasks more efficiently.","PeriodicalId":501070,"journal":{"name":"arXiv - QuanBio - Genomics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141146356","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An Autoencoder and Generative Adversarial Networks Approach for Multi-Omics Data Imbalanced Class Handling and Classification 一种自动编码器和生成式对抗网络方法用于多传感器数据不平衡类别处理和分类

arXiv - QuanBio - Genomics Pub Date : 2024-05-16 DOI: arxiv-2405.09756

Ibrahim Al-Hurani, Abedalrhman Alkhateeb, Salama Ikki

{"title":"An Autoencoder and Generative Adversarial Networks Approach for Multi-Omics Data Imbalanced Class Handling and Classification","authors":"Ibrahim Al-Hurani, Abedalrhman Alkhateeb, Salama Ikki","doi":"arxiv-2405.09756","DOIUrl":"https://doi.org/arxiv-2405.09756","url":null,"abstract":"In the relentless efforts in enhancing medical diagnostics, the integration\u0000of state-of-the-art machine learning methodologies has emerged as a promising\u0000research area. In molecular biology, there has been an explosion of data\u0000generated from multi-omics sequencing. The advent sequencing equipment can\u0000provide large number of complicated measurements per one experiment. Therefore,\u0000traditional statistical methods face challenging tasks when dealing with such\u0000high dimensional data. However, most of the information contained in these\u0000datasets is redundant or unrelated and can be effectively reduced to\u0000significantly fewer variables without losing much information. Dimensionality\u0000reduction techniques are mathematical procedures that allow for this reduction;\u0000they have largely been developed through statistics and machine learning\u0000disciplines. The other challenge in medical datasets is having an imbalanced\u0000number of samples in the classes, which leads to biased results in machine\u0000learning models. This study, focused on tackling these challenges in a neural\u0000network that incorporates autoencoder to extract latent space of the features,\u0000and Generative Adversarial Networks (GAN) to generate synthetic samples. Latent\u0000space is the reduced dimensional space that captures the meaningful features of\u0000the original data. Our model starts with feature selection to select the\u0000discriminative features before feeding them to the neural network. Then, the\u0000model predicts the outcome of cancer for different datasets. The proposed model\u0000outperformed other existing models by scoring accuracy of 95.09% for bladder\u0000cancer dataset and 88.82% for the breast cancer dataset.","PeriodicalId":501070,"journal":{"name":"arXiv - QuanBio - Genomics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141062651","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

VQDNA: Unleashing the Power of Vector Quantization for Multi-Species Genomic Sequence Modeling VQDNA：为多物种基因组序列建模释放矢量量化的力量

arXiv - QuanBio - Genomics Pub Date : 2024-05-13 DOI: arxiv-2405.10812

Siyuan Li, Zedong Wang, Zicheng Liu, Di Wu, Cheng Tan, Jiangbin Zheng, Yufei Huang, Stan Z. Li

{"title":"VQDNA: Unleashing the Power of Vector Quantization for Multi-Species Genomic Sequence Modeling","authors":"Siyuan Li, Zedong Wang, Zicheng Liu, Di Wu, Cheng Tan, Jiangbin Zheng, Yufei Huang, Stan Z. Li","doi":"arxiv-2405.10812","DOIUrl":"https://doi.org/arxiv-2405.10812","url":null,"abstract":"Similar to natural language models, pre-trained genome language models are\u0000proposed to capture the underlying intricacies within genomes with unsupervised\u0000sequence modeling. They have become essential tools for researchers and\u0000practitioners in biology. However, the textit{hand-crafted} tokenization\u0000policies used in these models may not encode the most discriminative patterns\u0000from the limited vocabulary of genomic data. In this paper, we introduce VQDNA,\u0000a general-purpose framework that renovates genome tokenization from the\u0000perspective of genome vocabulary learning. By leveraging vector-quantized\u0000codebook as textit{learnable} vocabulary, VQDNA can adaptively tokenize\u0000genomes into textit{pattern-aware} embeddings in an end-to-end manner. To\u0000further push its limits, we propose Hierarchical Residual Quantization (HRQ),\u0000where varying scales of codebooks are designed in a hierarchy to enrich the\u0000genome vocabulary in a coarse-to-fine manner. Extensive experiments on 32\u0000genome datasets demonstrate VQDNA's superiority and favorable parameter\u0000efficiency compared to existing genome language models. Notably, empirical\u0000analysis of SARS-CoV-2 mutations reveals the fine-grained pattern awareness and\u0000biological significance of learned HRQ vocabulary, highlighting its untapped\u0000potential for broader applications in genomics.","PeriodicalId":501070,"journal":{"name":"arXiv - QuanBio - Genomics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141146433","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Characterizing virulence differences in a parasitoid wasp through comparative transcriptomic and proteomic 通过比较转录组和蛋白质组鉴定寄生蜂的毒力差异

arXiv - QuanBio - Genomics Pub Date : 2024-05-13 DOI: arxiv-2405.07772

Samuel GornardEGCE, Pascaline Venon, Florian Lasfont, Thierry Balliau, Laure Marie-Paule Kaiser-Arnauld, Florence Mougel

{"title":"Characterizing virulence differences in a parasitoid wasp through comparative transcriptomic and proteomic","authors":"Samuel GornardEGCE, Pascaline Venon, Florian Lasfont, Thierry Balliau, Laure Marie-Paule Kaiser-Arnauld, Florence Mougel","doi":"arxiv-2405.07772","DOIUrl":"https://doi.org/arxiv-2405.07772","url":null,"abstract":"Background: Two strains of the endoparasitoid Cotesia typhae present a\u0000differential parasitism success on the host, Sesamia nonagrioides. One is\u0000virulent on both permissive and resistant host populations, and the other only\u0000on the permissive host. This interaction provides a very interesting frame for\u0000studying virulence factors. Here, we used a combination of comparative\u0000transcriptomic and proteomic analyses to unravel the molecular basis underlying\u0000virulence differences between the strains.Results: First, we report that\u0000virulence genes are mostly expressed during the nymphal stage of the\u0000parasitoid. Especially, proviral genes are broadly up-regulated at this stage,\u0000while their expression is only expected in the host. Parasitoid gene expression\u0000in the host increases with time, indicating the production of more virulence\u0000factors. Secondly, comparison between strains reveals differences in venom\u0000composition, with 12 proteins showing differential abundance. Proviral\u0000expression in the host displays a strong temporal variability, along with\u0000differential patterns between strains. Notably, a subset of proviral genes\u0000including protein-tyrosine phosphatases is specifically over-expressed in the\u0000resistant host parasitized by the less virulent strain, 24 hours after\u0000parasitism. This result particularly hints at host modulation of proviral\u0000expression.Conclusions: This study sheds light on the temporal expression of\u0000virulence factors of Cotesia typhae, both in the host and in the parasitoid. It\u0000also identifies potential molecular candidates driving differences in\u0000parasitism success between two strains. Together, those findings provide a path\u0000for further exploration of virulence mechanisms in parasitoid wasps, and offer\u0000insights into host-parasitoid coevolution.","PeriodicalId":501070,"journal":{"name":"arXiv - QuanBio - Genomics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140934831","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Fine-tuning Protein Language Models with Deep Mutational Scanning improves Variant Effect Prediction 利用深度突变扫描微调蛋白质语言模型，提高变异效应预测能力

arXiv - QuanBio - Genomics Pub Date : 2024-05-10 DOI: arxiv-2405.06729

Aleix Lafita, Ferran Gonzalez, Mahmoud Hossam, Paul Smyth, Jacob Deasy, Ari Allyn-Feuer, Daniel Seaton, Stephen Young

引用次数: 0