Developing computer-aided design (CAD) generation models has significantly enhanced design efficiency, facilitating innovation and transformation in the design industry. Existing methods typically require users to input prompts in a specific format, such as text descriptions or images, limiting their broader application in diverse scenarios. To address this limitation, we introduce FreeCAD, a user-friendly CAD generation framework that supports free-form inputs, including text descriptions and/or images, enabling users to express their design intentions more flexibly. Specifically, we propose a Large Language Models (LLMs)-based Text Translator, which effectively increases the success rate of generating CAD models by converting users' diversified requests for the same object into a unified expression. Additionally, the Multi-View Representation Fusion (MVRF) module enables the network to capture richer interaction information across views, facilitating the generation of more fine-grained CAD models. To support the training of FreeCAD, we construct a multimodal dataset RealCAD, comprising text, image, and CAD triplets, where the images are derived from the 3D printed products of CAD models. Extensive experiments demonstrate that FreeCAD consistently outperforms the existing state-of-the-art (SOTA) methods in multiple tasks.

Cancer is one of the leading causes of deaths worldwide. Survival analysis and prediction of cancer patients is of great significance for their precision medicine. The robustness and interpretability of the survival prediction models are important, where robustness tells whether a model has learned the knowledge, and interpretability means if a model can show human what it has learned. In this paper, we propose a robust and interpretable model SurvConvMixer, which uses pathways customized gene expression images and ConvMixer for cancer short-term, mid-term and long-term overall survival prediction. With ConvMixer, the representation of each pathway can be learned respectively. We show the robustness of our model by testing the trained model on absolutely untrained external datasets. The interpretability of SurvConvMixer depends on gradient-weighted class activation mapping (Grad-Cam), by which we can obtain the pathway-level activation heat map. Then wilcoxon rank-sum tests are conducted to obtain the statistically significant pathways, thereby revealing which pathways the model focuses on more. SurvConvMixer achieves remarkable performance on the short-term, mid-term and long-term overall survival of lung adenocarcinoma, lung squamous cell carcinoma and skin cutaneous melanoma, and the external validation tests show that SurvConvMixer can generalize to external datasets so that it is robust. Finally, we investigate the activation maps generated by Grad-Cam, after wilcoxon rank-sum test and Kaplan-Meier estimation, we find that some survival-related pathways play important role in SurvConvMixer.

Non-coding RNA (ncRNA) plays a critical role in biology. ncRNAs from the same family usually have similar functions, as a result, it is essential to predict ncRNA families before identifying their functions. There are two primary methods for predicting ncRNA families, namely, traditional biological methods and computational methods. In traditional biological methods, a lot of manpower and resources are required to predict ncRNA families. Therefore, this paper proposed a new ncRNA family prediction method called MFPred based on computational methods. MFPred identified ncRNA families by extracting sequence features of ncRNAs, and it possessed three primary modules, including (1) four ncRNA sequences encoding and feature extraction module, which encoded ncRNA sequences and extracted four different features of ncRNA sequences, (2) dynamic Bi_GRU and feature fusion module, which extracted contextual information features of the ncRNA sequence and (3) ResNet_SE module that extracted local information features of the ncRNA sequence. In this study, MFPred was compared with the previously proposed ncRNA family prediction methods using two frequently used public ncRNA datasets, NCY and nRC. The results showed that MFPred outperformed other prediction methods in the two datasets.

BACKGROUND: Although research on non-coding RNAs (ncRNAs) is a hot topic in life sciences, the functions of numerous ncRNAs remain unclear. In recent years, researchers have found that ncRNAs of the same family have similar functions, therefore, it is important to accurately predict ncRNAs families to identify their functions. There are several methods available to solve the prediction problem of ncRNAs family, whose main ideas can be divided into two categories, including prediction based on the secondary structure features of ncRNAs, and prediction according to sequence features of ncRNAs. The first type of prediction method requires a complicated process and has a low accuracy in obtaining the secondary structure of ncRNAs, while the second type of method has a simple prediction process and a high accuracy, but there is still room for improvement. The existing methods for ncRNAs family prediction are associated with problems such as complicated prediction processes and low accuracy, in this regard, it is necessary to propose a new method to predict the ncRNAs family more perfectly. RESULTS: A deep learning model-based method, ncDENSE, was proposed in this study, which predicted ncRNAs families by extracting ncRNAs sequence features. The bases in ncRNAs sequences were encoded by one-hot coding and later fed into an ensemble deep learning model, which contained the dynamic bi-directional gated recurrent unit (Bi-GRU), the dense convolutional network (DenseNet), and the Attention Mechanism (AM). To be specific, dynamic Bi-GRU was used to extract contextual feature information and capture long-term dependencies of ncRNAs sequences. AM was employed to assign different weights to features extracted by Bi-GRU and focused the attention on information with greater weights. Whereas DenseNet was adopted to extract local feature information of ncRNAs sequences and classify them by the full connection layer. According to our results, the ncDENSE method improved the Accuracy, Sensitivity, Precision, F-score, and MCC by 2.08[Formula: see text], 2.33[Formula: see text], 2.14[Formula: see text], 2.16[Formula: see text], and 2.39[Formula: see text], respectively, compared with the suboptimal method. CONCLUSIONS: Overall, the ncDENSE method proposed in this paper extracts sequence features of ncRNAs by dynamic Bi-GRU and DenseNet and improves the accuracy in predicting ncRNAs family and other data.

OBJECTIVE: Breast cancer is a significant health issue for women, and human epidermal growth factor receptor-2 (HER2) plays a crucial role as a vital prognostic and predictive factor. The HER2 status is essential for formulating effective treatment plans for breast cancer. However, the assessment of HER2 status using immunohistochemistry (IHC) is time-consuming and costly. Existing computational methods for evaluating HER2 status have limitations and lack sufficient accuracy. Therefore, there is an urgent need for an improved computational method to better assess HER2 status, which holds significant importance in saving lives and alleviating the burden on pathologists. RESULTS: This paper analyzes the characteristics of histological images of breast cancer and proposes a neural network model named HAHNet that combines multi-scale features with attention mechanisms for HER2 status classification. HAHNet directly classifies the HER2 status from hematoxylin and eosin (H&E) stained histological images, reducing additional costs. It achieves superior performance compared to other computational methods. CONCLUSIONS: According to our experimental results, the proposed HAHNet achieved high performance in classifying the HER2 status of breast cancer using only H&E stained samples. It can be applied in case classification, benefiting the work of pathologists and potentially helping more breast cancer patients.

Iris images captured in less-constrained environments often suffer from adverse noise, challenging many existing segmentation algorithms. In this paper, we propose an efficient Hybrid Transformer U-Net (HTU-Net) to address this dilemma. Unlike previous studies that only focus on utilizing popular CNN technology to predict iris masks accurately, HTU-Net can simultaneously obtain segmentation masks and parameterized pupillary and limbic boundaries by a multi-task network, further enabling CNN-based iris segmentation to be applied in any regular iris recognition systems. We explore the application of the Transformer in iris segmentation and propose a hybrid encoder that employs convolutional layers to extract local intensity features and the Transformer to capture long-range associative information. For decoding, we adopt a novel Multi-Head Dilated Attention to exploit the multi-scale contextual information by gating mechanism, thus emphasizing the important features and rendering powerful representations. Inspired by the consistent class characteristics of iris, we further devise a Pyramid Center-Aware Module to capture the global structural context of iris from a categorical perspective to improve performance. Experimental results show that our method, with fewer parameters than previous approaches, achieves competitive or new state-of-the-art performance in both iris segmentation and localization on three challenging iris datasets. Code will be released at https://github.com/Syloveslife/HTU-Net.

BACKGROUND: RNA secondary structure is very important for deciphering cell's activity and disease occurrence. The first method which was used by the academics to predict this structure is biological experiment, But this method is too expensive, causing the promotion to be affected. Then, computing methods emerged, which has good efficiency and low cost. However, the accuracy of computing methods are not satisfactory. Many machine learning methods have also been applied to this area, but the accuracy has not improved significantly. Deep learning has matured and achieves great success in many areas such as computer vision and natural language processing. It uses neural network which is a kind of structure that has good functionality and versatility, but its effect is highly correlated with the quantity and quality of the data. At present, there is no model with high accuracy, low data dependence and high convenience in predicting RNA secondary structure. RESULTS: This paper designs a neural network called LTPConstraint to predict RNA secondary structure. The network is based on many network structure such as Bidirectional LSTM, Transformer and generator. It also uses transfer learning to train modelso that the data dependence can be reduced. CONCLUSIONS: LTPConstraint has achieved high accuracy in RNA secondary structure prediction. Compared with the previous methods, the accuracy improves obviously both in predicting the structure with pseudoknot and the structure without pseudoknot. At the same time, LTPConstraint is easy to operate and can achieve result very quickly.

In this paper, a two-stage multi-category recognition structure based on texture features is proposed. This method can solve the problem of the decline in recognition accuracy in the scene of lightweight training samples. Besides, the problem of recognition effect different in the same recognition structure caused by the unsteady iris can also be solved. In this paper’s structure, digitized values of the edge shape in the iris texture of the image are set as the texture trend feature, while the differences between the gray values of the image obtained by convolution are set as the grayscale difference feature. Furthermore, the texture trend feature is used in the first-stage recognition. The template category that does not match the tested iris is the elimination category, and the remaining categories are uncertain categories. Whereas, in the second-stage recognition, uncertain categories are adopted to determine the iris recognition conclusion through the grayscale difference feature. Then, the experiment results using the JLU iris library show that the method in this paper can be highly efficient in multi-category heterogeneous iris recognition under lightweight training samples and unsteady state.

Graphical User Interface (GUI) is ubiquitous in almost all modern desktop software, mobile applications and online websites. A good GUI design is crucial to the success of the software in the market, but designing a good GUI which requires much innovation and creativity is difficult even to well-trained designers. In addition, the requirement of rapid development of GUI design also aggravates designers' working load. So, the availability of various automated generated GUIs can help enhance the design personalization and specialization as they can cater to the taste of different designers. To assist designers, we develop a model tool to automatically generate GUI designs. Different from conventional image generation models based on image pixels, our tool is to reuse GUI components collected from existing mobile app GUIs for composing a new design which is similar to natural-language generation. Our tool is based on SeqGAN by modelling the GUI component style compatibility and GUI structure. The evaluation demonstrates that our model significantly outperforms the best of the baseline methods by 30.77% in Fr'echet Inception distance (FID) and 12.35% in 1-Nearest Neighbor Accuracy (1-NNA). Through a pilot user study, we provide initial evidence of the usefulness of our approach for generating acceptable brand new GUI designs.

BACKGROUND: Studies have proven that the same family of non-coding RNAs (ncRNAs) have similar functions, so predicting the ncRNAs family is helpful to the research of ncRNAs functions. The existing calculation methods mainly fall into two categories: the first type is to predict ncRNAs family by learning the features of sequence or secondary structure, and the other type is to predict ncRNAs family by the alignment among homologs sequences. In the first type, some methods predict ncRNAs family by learning predicted secondary structure features. The inaccuracy of predicted secondary structure may cause the low accuracy of those methods. Different from that, ncRFP directly learning the features of ncRNA sequences to predict ncRNAs family. Although ncRFP simplifies the prediction process and improves the performance, there is room for improvement in ncRFP performance due to the incomplete features of its input data. In the secondary type, the homologous sequence alignment method can achieve the highest performance at present. However, due to the need for consensus secondary structure annotation of ncRNA sequences, and the helplessness for modeling pseudoknots, the use of the method is limited. RESULTS: In this paper, a novel method "ncDLRES", which according to learning the sequence features, is proposed to predict the family of ncRNAs based on Dynamic LSTM (Long Short-term Memory) and ResNet (Residual Neural Network). CONCLUSIONS: ncDLRES extracts the features of ncRNA sequences based on Dynamic LSTM and then classifies them by ResNet. Compared with the homologous sequence alignment method, ncDLRES reduces the data requirement and expands the application scope. By comparing with the first type of methods, the performance of ncDLRES is greatly improved.

BACKGROUND: Studies have shown that RNA secondary structure, a planar structure formed by paired bases, plays diverse vital roles in fundamental life activities and complex diseases. RNA secondary structure profile can record whether each base is paired with others. Hence, accurate prediction of secondary structure profile can help to deduce the secondary structure and binding site of RNA. RNA secondary structure profile can be obtained through biological experiment and calculation methods. Of them, the biological experiment method involves two ways: chemical reagent and biological crystallization. The chemical reagent method can obtain a large number of prediction data, but its cost is high and always associated with high noise, making it difficult to get results of all bases on RNA due to the limited of sequencing coverage. By contrast, the biological crystallization method can lead to accurate results, yet heavy experimental work and high costs are required. On the other hand, the calculation method is CROSS, which comprises a three-layer fully connected neural network. However, CROSS can not completely learn the features of RNA secondary structure profile since its poor network structure, leading to its low performance. RESULTS: In this paper, a novel end-to-end method, named as "RPRes, was proposed to predict RNA secondary structure profile based on Bidirectional LSTM and Residual Neural Network. CONCLUSIONS: RPRes utilizes data sets generated by multiple biological experiment methods as the training, validation, and test sets to predict profile, which can compatible with numerous prediction requirements. Compared with the biological experiment method, RPRes has reduced the costs and improved the prediction efficiency. Compared with the state-of-the-art calculation method CROSS, RPRes has significantly improved performance.

Evidence has accumulated enough to prove non-coding RNAs (ncRNAs) play important roles in cellular biological processes and disease pathogenesis. High throughput techniques have produced a large number of ncRNAs whose function remains unknown. Since the accurate identification of ncRNAs family is helpful to the research of their function, it is of necessity and urgency to predict the family of each ncRNAs. Although several traditional excellent methods are applicable to predict the family of ncRNAs, their complex procedures or inaccurate performance remain major problems confronting us. The main idea of those methods is first to predict the secondary structure, and then identify ncRNAs family according to properties of the secondary structure. Unfortunately, the multi-step error superposition, especially the imperfection of RNA secondary structure prediction tools, maybe the cause of low accuracy. In this paper, a novel end-to-end method 'ncRFP' was proposed to complete the prediction task based on Deep Learning. Instead of predicting the secondary structure, ncRFP predicts the ncRNAs family by automatically extracting features from ncRNAs sequences. Compared with other methods, ncRFP not only simplifies the process but also improves accuracy. The source code of ncRFP can be available at https://github.com/linyuwangPHD/ncRFP.

RNA plays an important role in various biological processes; hence, it is essential when determining the functions of RNA to research its secondary structures. So far, the accuracy of RNA secondary structure prediction remains an area in need of improvement. This paper presents a novel method for predicting RNA secondary structure based on an RNA folding simulation model. This model assumes that the process of RNA folding from the random coil state to full structure is staged and in every stage of folding, the final state of an RNA is determined by the optimal combination of helical regions, which are urgently essential to dynamics of RNA formation. This paper proposes the First Large Free Energy Difference (FLED) in order to find the helical regions most urgently needed for optimal final state formation among all the possible helical regions. Tests on the datasets with known structures from public databases demonstrate that our method can outperform other current RNA secondary structure prediction methods in terms of prediction accuracy.

Tumor samples are usually heterogeneous, containing admixture of more than one kind of tumor subclones. Studies of genomic aberrations from heterogeneous tumor data are hindered by the mixed signal of tumor subclone cells. Most of the existing algorithms cannot distinguish contributions of different subclones from the measured single nucleotide polymorphism (SNP) array signals, which may cause erroneous estimation of genomic aberrations. Here, we have introduced a computational method, Cancer Heterogeneity Analysis from SNP-array Experiments (CHASE), to automatically detect subclone proportions and genomic aberrations from heterogeneous tumor samples. Our method is based on HMM, and incorporates EM algorithm to build a statistical model for modeling mixed signal of multiple tumor subclones. We tested the proposed approach on simulated datasets and two real datasets, and the results show that the proposed method can efficiently estimate tumor subclone proportions and recovery the genomic aberrations.

MOTIVATION: Whole-genome sequencing of tumor samples has been demonstrated as an efficient approach for comprehensive analysis of genomic aberrations in cancer genome. Critical issues such as tumor impurity and aneuploidy, GC-content and mappability bias have been reported to complicate identification of copy number alteration and loss of heterozygosity in complex tumor samples. Therefore, efficient computational methods are required to address these issues. RESULTS: We introduce CLImAT (CNA and LOH Assessment in Impure and Aneuploid Tumors), a bioinformatics tool for identification of genomic aberrations from tumor samples using whole-genome sequencing data. Without requiring a matched normal sample, CLImAT takes integrated analysis of read depth and allelic frequency and provides extensive data processing procedures including GC-content and mappability correction of read depth and quantile normalization of B-allele frequency. CLImAT accurately identifies copy number alteration and loss of heterozygosity even for highly impure tumor samples with aneuploidy. We evaluate CLImAT on both simulated and real DNA sequencing data to demonstrate its ability to infer tumor impurity and ploidy and identify genomic aberrations in complex tumor samples. AVAILABILITY AND IMPLEMENTATION: The CLImAT software package can be freely downloaded at http://bioinformatics.ustc.edu.cn/CLImAT/.

After utterly researching existing fingerprint classification algorithms, this article gives out a new definition of core and delta point, putting forward an improved algorithm based on the gray level picture, implementing a fast and precise fingerprint classification system used in police. The experiments indicate that the excellent improvement has been made compared to the known methods in localization of delta and core points and accuracy ratio of classification, which can meet practical requirement.