arXiv subjects

Yiqing Shen

Publications and source records attributed to Yiqing Shen.

At least 55 records · Page 3Linked to original sources

arXiv2024

TourSynbio-Search: A Large Language Model Driven Agent Framework for Unified Search Method for Protein Engineering

The exponential growth in protein-related databases and scientific literature, combined with increasing demands for efficient biological information retrieval, has created an urgent need for unified and accessible search methods in protein engineering research. We present TourSynbio-Search, a novel bioinformatics search agent framework powered by the TourSynbio-7B protein multimodal large language model (LLM), designed to address the growing challenges of information retrieval across rapidly expanding protein databases and corresponding online research literature. The agent's dual-module architecture consists of PaperSearch and ProteinSearch components, enabling comprehensive exploration of both scientific literature and protein data across multiple biological databases. At its core, TourSynbio-Search employs an intelligent agent system that interprets natural language queries, optimizes search parameters, and executes search operations across major platforms including UniProt, PDB, ArXiv, and BioRxiv. The agent's ability to process intuitive natural language queries reduces technical barriers, allowing researchers to efficiently access and analyze complex biological data without requiring extensive bioinformatics expertise. Through detailed case studies in literature retrieval and protein structure visualization, we demonstrate TourSynbio-Search's effectiveness in streamlining biological information retrieval and enhancing research productivity. This framework represents an advancement in bridging the accessibility gap between complex biological databases and researchers, potentially accelerating progress in protein engineering applications. Our codes are available at: https://github.com/tsynbio/Toursynbio-Search

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arXiv2024

Validation of an LLM-based Multi-Agent Framework for Protein Engineering in Dry Lab and Wet Lab

Recent advancements in Large Language Models (LLMs) have enhanced efficiency across various domains, including protein engineering, where they offer promising opportunities for dry lab and wet lab experiment workflow automation. Previous work, namely TourSynbio-Agent, integrates a protein-specialized multimodal LLM (i.e. TourSynbio-7B) with domain-specific deep learning (DL) models to streamline both computational and experimental protein engineering tasks. While initial validation demonstrated TourSynbio-7B's fundamental protein property understanding, the practical effectiveness of the complete TourSynbio-Agent framework in real-world applications remained unexplored. This study presents a comprehensive validation of TourSynbio-Agent through five diverse case studies spanning both computational (dry lab) and experimental (wet lab) protein engineering. In three computational case studies, we evaluate the TourSynbio-Agent's capabilities in mutation prediction, protein folding, and protein design. Additionally, two wet-lab validations demonstrate TourSynbio-Agent's practical utility: engineering P450 proteins with up to 70% improved selectivity for steroid 19-hydroxylation, and developing reductases with 3.7x enhanced catalytic efficiency for alcohol conversion. Our findings from the five case studies establish that TourSynbio-Agent can effectively automate complex protein engineering workflows through an intuitive conversational interface, potentially accelerating scientific discovery in protein engineering.

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arXiv2024

AutoProteinEngine: A Large Language Model Driven Agent Framework for Multimodal AutoML in Protein Engineering

Protein engineering is important for biomedical applications, but conventional approaches are often inefficient and resource-intensive. While deep learning (DL) models have shown promise, their training or implementation into protein engineering remains challenging for biologists without specialized computational expertise. To address this gap, we propose AutoProteinEngine (AutoPE), an agent framework that leverages large language models (LLMs) for multimodal automated machine learning (AutoML) for protein engineering. AutoPE innovatively allows biologists without DL backgrounds to interact with DL models using natural language, lowering the entry barrier for protein engineering tasks. Our AutoPE uniquely integrates LLMs with AutoML to handle model selection for both protein sequence and graph modalities, automatic hyperparameter optimization, and automated data retrieval from protein databases. We evaluated AutoPE through two real-world protein engineering tasks, demonstrating substantial performance improvements compared to traditional zero-shot and manual fine-tuning approaches. By bridging the gap between DL and biologists' domain expertise, AutoPE empowers researchers to leverage DL without extensive programming knowledge. Our code is available at https://github.com/tsynbio/AutoPE.

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arXiv2024

LaGDif: Latent Graph Diffusion Model for Efficient Protein Inverse Folding with Self-Ensemble

Protein inverse folding aims to identify viable amino acid sequences that can fold into given protein structures, enabling the design of novel proteins with desired functions for applications in drug discovery, enzyme engineering, and biomaterial development. Diffusion probabilistic models have emerged as a promising approach in inverse folding, offering both feasible and diverse solutions compared to traditional energy-based methods and more recent protein language models. However, existing diffusion models for protein inverse folding operate in discrete data spaces, necessitating prior distributions for transition matrices and limiting smooth transitions and gradients inherent to continuous spaces, leading to suboptimal performance. Drawing inspiration from the success of diffusion models in continuous domains, we introduce the Latent Graph Diffusion Model for Protein Inverse Folding (LaGDif). LaGDif bridges discrete and continuous realms through an encoder-decoder architecture, transforming protein graph data distributions into random noise within a continuous latent space. Our model then reconstructs protein sequences by considering spatial configurations, biochemical attributes, and environmental factors of each node. Additionally, we propose a novel inverse folding self-ensemble method that stabilizes prediction results and further enhances performance by aggregating multiple denoised output protein sequence. Empirical results on the CATH dataset demonstrate that LaGDif outperforms existing state-of-the-art techniques, achieving up to 45.55% improvement in sequence recovery rate for single-chain proteins and maintaining an average RMSD of 1.96 Å between generated and native structures. The code is public available at https://github.com/TaoyuW/LaGDif.

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arXiv2024

A Regressor-Guided Graph Diffusion Model for Predicting Enzyme Mutations to Enhance Turnover Number

Enzymes are biological catalysts that can accelerate chemical reactions compared to uncatalyzed reactions in aqueous environments. Their catalytic efficiency is quantified by the turnover number (kcat), a parameter in enzyme kinetics. Enhancing enzyme activity is important for optimizing slow chemical reactions, with far-reaching implications for both research and industrial applications. However, traditional wet-lab methods for measuring and optimizing enzyme activity are often resource-intensive and time-consuming. To address these limitations, we introduce kcatDiffuser, a novel regressor-guided diffusion model designed to predict and improve enzyme turnover numbers. Our approach innovatively reformulates enzyme mutation prediction as a protein inverse folding task, thereby establishing a direct link between structural prediction and functional optimization. kcatDiffuser is a graph diffusion model guided by a regressor, enabling the prediction of amino acid mutations at multiple random positions simultaneously. Evaluations on BERENDA dataset shows that kcatDiffuser can achieve a Δ log kcat of 0.209, outperforming state-of-the-art methods like ProteinMPNN, PiFold, GraDe-IF in improving enzyme turnover numbers. Additionally, kcatDiffuser maintains high structural fidelity with a recovery rate of 0.716, pLDDT score of 92.515, RMSD of 3.764, and TM-score of 0.934, demonstrating its ability to generate enzyme variants with enhanced activity while preserving essential structural properties. Overall, kcatDiffuser represents a more efficient and targeted approach to enhancing enzyme activity. The code is available at https://github.com/xz32yu/KcatDiffuser.

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arXiv2024

DiffuseReg: Denoising Diffusion Model for Obtaining Deformation Fields in Unsupervised Deformable Image Registration

Deformable image registration aims to precisely align medical images from different modalities or times. Traditional deep learning methods, while effective, often lack interpretability, real-time observability and adjustment capacity during registration inference. Denoising diffusion models present an alternative by reformulating registration as iterative image denoising. However, existing diffusion registration approaches do not fully harness capabilities, neglecting the critical sampling phase that enables continuous observability during the inference. Hence, we introduce DiffuseReg, an innovative diffusion-based method that denoises deformation fields instead of images for improved transparency. We also propose a novel denoising network upon Swin Transformer, which better integrates moving and fixed images with diffusion time step throughout the denoising process. Furthermore, we enhance control over the denoising registration process with a novel similarity consistency regularization. Experiments on ACDC datasets demonstrate DiffuseReg outperforms existing diffusion registration methods by 1.32 in Dice score. The sampling process in DiffuseReg enables real-time output observability and adjustment unmatched by previous deep models.

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arXiv2024

SAM-Med3D: Towards General-purpose Segmentation Models for Volumetric Medical Images

Existing volumetric medical image segmentation models are typically task-specific, excelling at specific target but struggling to generalize across anatomical structures or modalities. This limitation restricts their broader clinical use. In this paper, we introduce SAM-Med3D for general-purpose segmentation on volumetric medical images. Given only a few 3D prompt points, SAM-Med3D can accurately segment diverse anatomical structures and lesions across various modalities. To achieve this, we gather and process a large-scale 3D medical image dataset, SA-Med3D-140K, from a blend of public sources and licensed private datasets. This dataset includes 22K 3D images and 143K corresponding 3D masks. Then SAM-Med3D, a promptable segmentation model characterized by the fully learnable 3D structure, is trained on this dataset using a two-stage procedure and exhibits impressive performance on both seen and unseen segmentation targets. We comprehensively evaluate SAM-Med3D on 16 datasets covering diverse medical scenarios, including different anatomical structures, modalities, targets, and zero-shot transferability to new/unseen tasks. The evaluation shows the efficiency and efficacy of SAM-Med3D, as well as its promising application to diverse downstream tasks as a pre-trained model. Our approach demonstrates that substantial medical resources can be utilized to develop a general-purpose medical AI for various potential applications. Our dataset, code, and models are available at https://github.com/uni-medical/SAM-Med3D.

cs.CV↗

arXiv2024

A Survey for Foundation Models in Autonomous Driving

The advent of foundation models has revolutionized the fields of natural language processing and computer vision, paving the way for their application in autonomous driving (AD). This survey presents a comprehensive review of more than 40 research papers, demonstrating the role of foundation models in enhancing AD. Large language models contribute to planning and simulation in AD, particularly through their proficiency in reasoning, code generation and translation. In parallel, vision foundation models are increasingly adapted for critical tasks such as 3D object detection and tracking, as well as creating realistic driving scenarios for simulation and testing. Multi-modal foundation models, integrating diverse inputs, exhibit exceptional visual understanding and spatial reasoning, crucial for end-to-end AD. This survey not only provides a structured taxonomy, categorizing foundation models based on their modalities and functionalities within the AD domain but also delves into the methods employed in current research. It identifies the gaps between existing foundation models and cutting-edge AD approaches, thereby charting future research directions and proposing a roadmap for bridging these gaps.

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arXiv2024

A Survey for Large Language Models in Biomedicine

Recent breakthroughs in large language models (LLMs) offer unprecedented natural language understanding and generation capabilities. However, existing surveys on LLMs in biomedicine often focus on specific applications or model architectures, lacking a comprehensive analysis that integrates the latest advancements across various biomedical domains. This review, based on an analysis of 484 publications sourced from databases including PubMed, Web of Science, and arXiv, provides an in-depth examination of the current landscape, applications, challenges, and prospects of LLMs in biomedicine, distinguishing itself by focusing on the practical implications of these models in real-world biomedical contexts. Firstly, we explore the capabilities of LLMs in zero-shot learning across a broad spectrum of biomedical tasks, including diagnostic assistance, drug discovery, and personalized medicine, among others, with insights drawn from 137 key studies. Then, we discuss adaptation strategies of LLMs, including fine-tuning methods for both uni-modal and multi-modal LLMs to enhance their performance in specialized biomedical contexts where zero-shot fails to achieve, such as medical question answering and efficient processing of biomedical literature. Finally, we discuss the challenges that LLMs face in the biomedicine domain including data privacy concerns, limited model interpretability, issues with dataset quality, and ethics due to the sensitive nature of biomedical data, the need for highly reliable model outputs, and the ethical implications of deploying AI in healthcare. To address these challenges, we also identify future research directions of LLM in biomedicine including federated learning methods to preserve data privacy and integrating explainable AI methodologies to enhance the transparency of LLMs.

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arXiv2024

TourSynbio: A Multi-Modal Large Model and Agent Framework to Bridge Text and Protein Sequences for Protein Engineering

The structural similarities between protein sequences and natural languages have led to parallel advancements in deep learning across both domains. While large language models (LLMs) have achieved much progress in the domain of natural language processing, their potential in protein engineering remains largely unexplored. Previous approaches have equipped LLMs with protein understanding capabilities by incorporating external protein encoders, but this fails to fully leverage the inherent similarities between protein sequences and natural languages, resulting in sub-optimal performance and increased model complexity. To address this gap, we present TourSynbio-7B, the first multi-modal large model specifically designed for protein engineering tasks without external protein encoders. TourSynbio-7B demonstrates that LLMs can inherently learn to understand proteins as language. The model is post-trained and instruction fine-tuned on InternLM2-7B using ProteinLMDataset, a dataset comprising 17.46 billion tokens of text and protein sequence for self-supervised pretraining and 893K instructions for supervised fine-tuning. TourSynbio-7B outperforms GPT-4 on the ProteinLMBench, a benchmark of 944 manually verified multiple-choice questions, with 62.18% accuracy. Leveraging TourSynbio-7B's enhanced protein sequence understanding capability, we introduce TourSynbio-Agent, an innovative framework capable of performing various protein engineering tasks, including mutation analysis, inverse folding, protein folding, and visualization. TourSynbio-Agent integrates previously disconnected deep learning models in the protein engineering domain, offering a unified conversational user interface for improved usability. Finally, we demonstrate the efficacy of TourSynbio-7B and TourSynbio-Agent through two wet lab case studies on vanilla key enzyme modification and steroid compound catalysis.

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arXiv2024

Performance and Non-adversarial Robustness of the Segment Anything Model 2 in Surgical Video Segmentation

Fully supervised deep learning (DL) models for surgical video segmentation have been shown to struggle with non-adversarial, real-world corruptions of image quality including smoke, bleeding, and low illumination. Foundation models for image segmentation, such as the segment anything model (SAM) that focuses on interactive prompt-based segmentation, move away from semantic classes and thus can be trained on larger and more diverse data, which offers outstanding zero-shot generalization with appropriate user prompts. Recently, building upon this success, SAM-2 has been proposed to further extend the zero-shot interactive segmentation capabilities from independent frame-by-frame to video segmentation. In this paper, we present a first experimental study evaluating SAM-2's performance on surgical video data. Leveraging the SegSTRONG-C MICCAI EndoVIS 2024 sub-challenge dataset, we assess SAM-2's effectiveness on uncorrupted endoscopic sequences and evaluate its non-adversarial robustness on videos with corrupted image quality simulating smoke, bleeding, and low brightness conditions under various prompt strategies. Our experiments demonstrate that SAM-2, in zero-shot manner, can achieve competitive or even superior performance compared to fully-supervised deep learning models on surgical video data, including under non-adversarial corruptions of image quality. Additionally, SAM-2 consistently outperforms the original SAM and its medical variants across all conditions. Finally, frame-sparse prompting can consistently outperform frame-wise prompting for SAM-2, suggesting that allowing SAM-2 to leverage its temporal modeling capabilities leads to more coherent and accurate segmentation compared to frequent prompting.

eess.IV↗

arXiv2024

AutoM3L: An Automated Multimodal Machine Learning Framework with Large Language Models

Automated Machine Learning (AutoML) offers a promising approach to streamline the training of machine learning models. However, existing AutoML frameworks are often limited to unimodal scenarios and require extensive manual configuration. Recent advancements in Large Language Models (LLMs) have showcased their exceptional abilities in reasoning, interaction, and code generation, presenting an opportunity to develop a more automated and user-friendly framework. To this end, we introduce AutoM3L, an innovative Automated Multimodal Machine Learning framework that leverages LLMs as controllers to automatically construct multimodal training pipelines. AutoM3L comprehends data modalities and selects appropriate models based on user requirements, providing automation and interactivity. By eliminating the need for manual feature engineering and hyperparameter optimization, our framework simplifies user engagement and enables customization through directives, addressing the limitations of previous rule-based AutoML approaches. We evaluate the performance of AutoM3L on six diverse multimodal datasets spanning classification, regression, and retrieval tasks, as well as a comprehensive set of unimodal datasets. The results demonstrate that AutoM3L achieves competitive or superior performance compared to traditional rule-based AutoML methods. Furthermore, a user study highlights the user-friendliness and usability of our framework, compared to the rule-based AutoML methods.

cs.LG↗

arXiv2024

FastSAM-3DSlicer: A 3D-Slicer Extension for 3D Volumetric Segment Anything Model with Uncertainty Quantification

Accurate segmentation of anatomical structures and pathological regions in medical images is crucial for diagnosis, treatment planning, and disease monitoring. While the Segment Anything Model (SAM) and its variants have demonstrated impressive interactive segmentation capabilities on image types not seen during training without the need for domain adaptation or retraining, their practical application in volumetric 3D medical imaging workflows has been hindered by the lack of a user-friendly interface. To address this challenge, we introduce FastSAM-3DSlicer, a 3D Slicer extension that integrates both 2D and 3D SAM models, including SAM-Med2D, MedSAM, SAM-Med3D, and FastSAM-3D. Building on the well-established open-source 3D Slicer platform, our extension enables efficient, real-time segmentation of 3D volumetric medical images, with seamless interaction and visualization. By automating the handling of raw image data, user prompts, and segmented masks, FastSAM-3DSlicer provides a streamlined, user-friendly interface that can be easily incorporated into medical image analysis workflows. Performance evaluations reveal that the FastSAM-3DSlicer extension running FastSAM-3D achieves low inference times of only 1.09 seconds per volume on CPU and 0.73 seconds per volume on GPU, making it well-suited for real-time interactive segmentation. Moreover, we introduce an uncertainty quantification scheme that leverages the rapid inference capabilities of FastSAM-3D for practical implementation, further enhancing its reliability and applicability in medical settings. FastSAM-3DSlicer offers an interactive platform and user interface for 2D and 3D interactive volumetric medical image segmentation, offering a powerful combination of efficiency, precision, and ease of use with SAMs. The source code and a video demonstration are publicly available at https://github.com/arcadelab/FastSAM3D_slicer.

eess.IV↗

arXiv2024

Promptable Counterfactual Diffusion Model for Unified Brain Tumor Segmentation and Generation with MRIs

Brain tumor analysis in Magnetic Resonance Imaging (MRI) is crucial for accurate diagnosis and treatment planning. However, the task remains challenging due to the complexity and variability of tumor appearances, as well as the scarcity of labeled data. Traditional approaches often address tumor segmentation and image generation separately, limiting their effectiveness in capturing the intricate relationships between healthy and pathological tissue structures. We introduce a novel promptable counterfactual diffusion model as a unified solution for brain tumor segmentation and generation in MRI. The key innovation lies in our mask-level prompting mechanism at the sampling stage, which enables guided generation and manipulation of specific healthy or unhealthy regions in MRI images. Specifically, the model's architecture allows for bidirectional inference, which can segment tumors in existing images and generate realistic tumor structures in healthy brain scans. Furthermore, we present a two-step approach for tumor generation and position transfer, showcasing the model's versatility in synthesizing realistic tumor structures. Experiments on the BRATS2021 dataset demonstrate that our method outperforms traditional counterfactual diffusion approaches, achieving a mean IoU of 0.653 and mean Dice score of 0.785 for tumor segmentation, outperforming the 0.344 and 0.475 of conventional counterfactual diffusion model. Our work contributes to improving brain tumor detection and segmentation accuracy, with potential implications for data augmentation and clinical decision support in neuro-oncology. The code is available at https://github.com/arcadelab/counterfactual_diffusion.

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arXiv2024

A Fine-tuning Dataset and Benchmark for Large Language Models for Protein Understanding

The parallels between protein sequences and natural language in their sequential structures have inspired the application of large language models (LLMs) to protein understanding. Despite the success of LLMs in NLP, their effectiveness in comprehending protein sequences remains an open question, largely due to the absence of datasets linking protein sequences to descriptive text. Researchers have then attempted to adapt LLMs for protein understanding by integrating a protein sequence encoder with a pre-trained LLM. However, this adaptation raises a fundamental question: "Can LLMs, originally designed for NLP, effectively comprehend protein sequences as a form of language?" Current datasets fall short in addressing this question due to the lack of a direct correlation between protein sequences and corresponding text descriptions, limiting the ability to train and evaluate LLMs for protein understanding effectively. To bridge this gap, we introduce ProteinLMDataset, a dataset specifically designed for further self-supervised pretraining and supervised fine-tuning (SFT) of LLMs to enhance their capability for protein sequence comprehension. Specifically, ProteinLMDataset includes 17.46 billion tokens for pretraining and 893,000 instructions for SFT. Additionally, we present ProteinLMBench, the first benchmark dataset consisting of 944 manually verified multiple-choice questions for assessing the protein understanding capabilities of LLMs. ProteinLMBench incorporates protein-related details and sequences in multiple languages, establishing a new standard for evaluating LLMs' abilities in protein comprehension. The large language model InternLM2-7B, pretrained and fine-tuned on the ProteinLMDataset, outperforms GPT-4 on ProteinLMBench, achieving the highest accuracy score.

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arXiv2024

M$^4$oE: A Foundation Model for Medical Multimodal Image Segmentation with Mixture of Experts

Medical imaging data is inherently heterogeneous across different modalities and clinical centers, posing unique challenges for developing generalizable foundation models. Conventional entails training distinct models per dataset or using a shared encoder with modality-specific decoders. However, these approaches incur heavy computational overheads and suffer from poor scalability. To address these limitations, we propose the Medical Multimodal Mixture of Experts (M$^4$oE) framework, leveraging the SwinUNet architecture. Specifically, M$^4$oE comprises modality-specific experts; each separately initialized to learn features encoding domain knowledge. Subsequently, a gating network is integrated during fine-tuning to modulate each expert's contribution to the collective predictions dynamically. This enhances model interpretability and generalization ability while retaining expertise specialization. Simultaneously, the M$^4$oE architecture amplifies the model's parallel processing capabilities, and it also ensures the model's adaptation to new modalities with ease. Experiments across three modalities reveal that M$^4$oE can achieve 3.45% over STU-Net-L, 5.11% over MED3D, and 11.93% over SAM-Med2D across the MICCAI FLARE22, AMOS2022, and ATLAS2023 datasets. Moreover, M$^4$oE showcases a significant reduction in training duration with 7 hours less while maintaining a parameter count that is only 30% of its compared methods. The code is available at https://github.com/JefferyJiang-YF/M4oE.

eess.IV↗

arXiv2024

ProteinEngine: Empower LLM with Domain Knowledge for Protein Engineering

Large language models (LLMs) have garnered considerable attention for their proficiency in tackling intricate tasks, particularly leveraging their capacities for zero-shot and in-context learning. However, their utility has been predominantly restricted to general tasks due to an absence of domain-specific knowledge. This constraint becomes particularly pertinent in the realm of protein engineering, where specialized expertise is required for tasks such as protein function prediction, protein evolution analysis, and protein design, with a level of specialization that existing LLMs cannot furnish. In response to this challenge, we introduce \textsc{ProteinEngine}, a human-centered platform aimed at amplifying the capabilities of LLMs in protein engineering by seamlessly integrating a comprehensive range of relevant tools, packages, and software via API calls. Uniquely, \textsc{ProteinEngine} assigns three distinct roles to LLMs, facilitating efficient task delegation, specialized task resolution, and effective communication of results. This design fosters high extensibility and promotes the smooth incorporation of new algorithms, models, and features for future development. Extensive user studies, involving participants from both the AI and protein engineering communities across academia and industry, consistently validate the superiority of \textsc{ProteinEngine} in augmenting the reliability and precision of deep learning in protein engineering tasks. Consequently, our findings highlight the potential of \textsc{ProteinEngine} to bride the disconnected tools for future research in the protein engineering domain.

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arXiv2024

DiffImpute: Tabular Data Imputation With Denoising Diffusion Probabilistic Model

Tabular data plays a crucial role in various domains but often suffers from missing values, thereby curtailing its potential utility. Traditional imputation techniques frequently yield suboptimal results and impose substantial computational burdens, leading to inaccuracies in subsequent modeling tasks. To address these challenges, we propose DiffImpute, a novel Denoising Diffusion Probabilistic Model (DDPM). Specifically, DiffImpute is trained on complete tabular datasets, ensuring that it can produce credible imputations for missing entries without undermining the authenticity of the existing data. Innovatively, it can be applied to various settings of Missing Completely At Random (MCAR) and Missing At Random (MAR). To effectively handle the tabular features in DDPM, we tailor four tabular denoising networks, spanning MLP, ResNet, Transformer, and U-Net. We also propose Harmonization to enhance coherence between observed and imputed data by infusing the data back and denoising them multiple times during the sampling stage. To enable efficient inference while maintaining imputation performance, we propose a refined non-Markovian sampling process that works along with Harmonization. Empirical evaluations on seven diverse datasets underscore the prowess of DiffImpute. Specifically, when paired with the Transformer as the denoising network, it consistently outperforms its competitors, boasting an average ranking of 1.7 and the most minimal standard deviation. In contrast, the next best method lags with a ranking of 2.8 and a standard deviation of 0.9. The code is available at https://github.com/Dendiiiii/DiffImpute.

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