Commun Biol. 2024 Dec 27;7(1):1705. doi: 10.1038/s42003-024-07411-y.

ABSTRACT

Understanding the function of proteins is of great significance for revealing disease pathogenesis and discovering new targets. Benefiting from the explosive growth of the protein universal, deep learning has been applied to accelerate the protein annotation cycle from different biological modalities. However, most existing deep learning-based methods not only fail to effectively fuse different biological modalities, resulting in low-quality protein representations, but also suffer from the convergence of suboptimal solution caused by sparse label representations. Aiming at the above issue, we propose a multiprocedural approach for fusing heterogeneous biological modalities and annotating protein functions, i.e., MIF2GO (Multimodal Information Fusion to infer Gene Ontology terms), which sequentially fuses up to six biological modalities ranging from different biological levels in three steps, thus leading to powerful protein representations. Evaluation results on seven benchmark datasets show that the proposed method not only considerably outperforms state-of-the-art performance, but also demonstrates great robustness and generalizability across species. Besides, we also present biological insights into the associations between those modalities and protein functions. This research provides a robust framework for integrating multimodal biological data, offering a scalable solution for protein function annotation, ultimately facilitating advancements in precision medicine and the discovery of novel therapeutic strategies.

PMID:39730886 | DOI:10.1038/s42003-024-07411-y

Invest Radiol. 2024 Oct 10. doi: 10.1097/RLI.0000000000001128. Online ahead of print.

ABSTRACT

OBJECTIVES: The aim of this study was to evaluate the feasibility and efficacy of visual scoring, low-attenuation volume (LAV), and deep learning methods for estimating emphysema extent in x-ray dose photon-counting detector computed tomography (PCD-CT), aiming to explore future dose reduction potentials.

METHODS: One hundred one prospectively enrolled patients underwent noncontrast low- and chest x-ray dose CT scans in the same study using PCD-CT. Overall image quality, sharpness, and noise, as well as visual emphysema pattern (no, trace, mild, moderate, confluent, and advanced destructive emphysema; as defined by the Fleischner Society), were independently assessed by 2 experienced radiologists for low- and x-ray dose images, followed by an expert consensus read. In the second step, automated emphysema quantification was performed using an established LAV algorithm with a threshold of -950 HU and a commercially available deep learning model for automated emphysema quantification. Automated estimations of emphysema extent were converted and compared with visual scoring ratings.

RESULTS: X-ray dose scans exhibited a significantly lower computed tomography dose index than low-dose scans (low-dose: 0.66 ± 0.16 mGy, x-ray dose: 0.11 ± 0.03 mGy, P < 0.001). Interreader agreement between low- and x-ray dose for visual emphysema scoring was excellent (κ = 0.83). Visual emphysema scoring consensus showed good agreement between low-dose and x-ray dose scans (κ = 0.70), with significant and strong correlation (Spearman ρ = 0.79). Although trace emphysema was underestimated in x-ray dose scans, there was no significant difference in the detection of higher-grade (mild to advanced destructive) emphysema (P = 0.125) between the 2 scan doses. Although predicted emphysema volumes on x-ray dose scans for the LAV method showed strong and the deep learning model excellent significant correlations with predictions on low-dose scans, both methods significantly overestimated emphysema volumes on lower quality scans (P < 0.001), with the deep learning model being more robust. Further, deep learning emphysema severity estimations showed higher agreement (κ = 0.65) and correlation (Spearman ρ = 0.64) with visual scoring for low-dose scans than LAV predictions (κ = 0.48, Spearman ρ = 0.45).

CONCLUSIONS: The severity of emphysema can be reliably estimated using visual scoring on CT scans performed with x-ray equivalent doses on a PCD-CT. A deep learning algorithm demonstrated good agreement and strong correlation with the visual scoring method on low-dose scans. However, both the deep learning and LAV algorithms overestimated emphysema extent on x-ray dose scans. Nonetheless, x-ray equivalent radiation dose scans may revolutionize the detection and monitoring of disease in chronic obstructive pulmonary disease patients.

PMID:39729642 | DOI:10.1097/RLI.0000000000001128

PLoS One. 2024 Dec 27;19(12):e0315245. doi: 10.1371/journal.pone.0315245. eCollection 2024.

ABSTRACT

The increasing utilization of deep learning models in drug repositioning has proven to be highly efficient and effective. In this study, we employed an integrated deep-learning model followed by traditional drug screening approach to screen a library of FDA-approved drugs, aiming to identify novel inhibitors targeting the TNF-α converting enzyme (TACE). TACE, also known as ADAM17, plays a crucial role in the inflammatory response by converting pro-TNF-α to its active soluble form and cleaving other inflammatory mediators, making it a promising target for therapeutic intervention in diseases such as rheumatoid arthritis. Reference datasets containing active and decoy compounds specific to TACE were obtained from the DUD-E database. Using RDKit, a cheminformatics toolkit, we extracted molecular features from these compounds. We applied the GraphConvMol model within the DeepChem framework, which utilizes graph convolutional networks, to build a predictive model based on the DUD-E datasets. Our trained model was subsequently used to predict the TACE inhibitory potential of FDA-approved drugs. From these predictions, Vorinostat was identified as a potential TACE inhibitor. Moreover, molecular docking and molecular dynamics simulation were conducted to validate these findings, using BMS-561392 as a reference TACE inhibitor. Vorinostat, originally an FDA-approved drug for cancer treatment, exhibited strong binding interactions with key TACE residues, suggesting its repurposing potential. Biological evaluation with RAW 264.7 cell confirmed the computational results, demonstrating that Vorinostat exhibited comparable inhibitory activity against TACE. In conclusion, our study highlights the capability of deep learning models to enhance virtual screening efforts in drug discovery, efficiently identifying potential candidates for specific targets such as TACE. Vorinostat, as a newly identified TACE inhibitor, holds promise for further exploration and investigation in the treatment of inflammatory diseases like rheumatoid arthritis.

PMID:39729480 | DOI:10.1371/journal.pone.0315245

PLoS One. 2024 Dec 27;19(12):e0311081. doi: 10.1371/journal.pone.0311081. eCollection 2024.

ABSTRACT

BACKGROUND: Dyspnoea is one of the emergency department's (ED) most common and deadly chief complaints, but frequently misdiagnosed and mistreated. We aimed to design a diagnostic decision support which classifies dyspnoeic ED visits into acute heart failure (AHF), exacerbation of chronic obstructive pulmonary disease (eCOPD), pneumonia and "other diagnoses" by using deep learning and complete, unselected data from an entire regional health care system.

METHODS: In this cross-sectional study, we included all dyspnoeic ED visits of patients ≥ 18 years of age at the two EDs in the region of Halland, Sweden, 07/01/2017-12/31/2019. Data from the complete regional health care system within five years prior to the ED visit were analysed. Gold standard diagnoses were defined as the subsequent in-hospital or ED discharge notes, and a subsample was manually reviewed by emergency medicine experts. A novel deep learning model, the clinical attention-based recurrent encoder network (CareNet), was developed. Cohort performance was compared to a simpler CatBoost model. A list of all variables and their importance for diagnosis was created. For each unique patient visit, the model selected the most important variables, analysed them and presented them to the clinician interpretably by taking event time and clinical context into account. AUROC, sensitivity and specificity were compared.

FINDINGS: The most prevalent diagnoses among the 10,315 dyspnoeic ED visits were AHF (15.5%), eCOPD (14.0%) and pneumonia (13.3%). Median number of unique events, i.e., registered clinical data with time stamps, per ED visit was 1,095 (IQR 459-2,310). CareNet median AUROC was 87.0%, substantially higher than the CatBoost model´s (81.4%). CareNet median sensitivity for AHF, eCOPD, and pneumonia was 74.5%, 92.6%, and 54.1%, respectively, with a specificity set above 75.0, slightly inferior to that of the CatBoost baseline model. The model assembled a list of 1,596 variables by importance for diagnosis, on top were prior diagnoses of heart failure or COPD, daily smoking, atrial fibrillation/flutter, life management difficulties and maternity care. Each patient visit received their own unique attention plot, graphically displaying important clinical events for the diagnosis.

INTERPRETATION: We designed a novel interpretable deep learning model for diagnosis in emergency department dyspnoea patients by analysing unselected data from a complete regional health care system.

PMID:39729465 | DOI:10.1371/journal.pone.0311081

Oral Radiol. 2024 Dec 27. doi: 10.1007/s11282-024-00799-7. Online ahead of print.

ABSTRACT

OBJECTIVE: The aim of this study is to determine the contact relationship and position of impacted mandibular third molar teeth (IMM3) with the mandibular canal (MC) in panoramic radiography (PR) images using deep learning (DL) models trained with the help of cone beam computed tomography (CBCT) and DL to compare the performances of the architectures.

METHODS: In this study, a total of 546 IMM3s from 290 patients with CBCT and PR images were included. The performances of SqueezeNet, GoogLeNet, and Inception-v3 architectures in solving four problems on two different regions of interest (RoI) were evaluated.

RESULTS: The SqueezeNet architecture performed the best on the vertical RoI, showing 93.2% accuracy in the identification of the 2nd problem (contact relationship buccal or lingual). Inception-v3 showed the highest performance with 84.8% accuracy in horizontal RoI for the 1st problem (contact relationship-no contact relationship), GoogLeNet showed 77.4% accuracy in horizontal RoI for the 4th problem (contact relationship buccal, lingual, other category, or no contact relationship), and GoogLeNet showed 70.0% accuracy in horizontal RoI for the 3rd problem (contact relationship buccal, lingual, or other category).

CONCLUSION: This study found that the Inception-v3 model showed the highest accuracy values in determining the contact relationship, and SqueezeNet architecture showed the highest accuracy values in determining the position of IMM3 relative to MC in the presence of a contact relationship.

PMID:39729224 | DOI:10.1007/s11282-024-00799-7

Tomography. 2024 Dec 23;10(12):2144-2157. doi: 10.3390/tomography10120151.

ABSTRACT

BACKGROUND/OBJECTIVES: Calculating the radiation dose from CT in 18F-PET/CT examinations poses a significant challenge. The objective of this study is to develop a deep learning-based automated program that standardizes the measurement of radiation doses.

METHODS: The torso CT was segmented into six distinct regions using TotalSegmentator. An automated program was employed to extract the necessary information and calculate the effective dose (ED) of PET/CT. The accuracy of our automated program was verified by comparing the EDs calculated by the program with those determined by a nuclear medicine physician (n = 30). Additionally, we compared the EDs obtained from an older PET/CT scanner with those from a newer PET/CT scanner (n = 42).

RESULTS: The CT ED calculated by the automated program was not significantly different from that calculated by the nuclear medicine physician (3.67 ± 0.61 mSv and 3.62 ± 0.60 mSv, respectively, p = 0.7623). Similarly, the total ED showed no significant difference between the two calculation methods (8.10 ± 1.40 mSv and 8.05 ± 1.39 mSv, respectively, p = 0.8957). A very strong correlation was observed in both the CT ED and total ED between the two measurements (r2 = 0.9981 and 0.9996, respectively). The automated program showed excellent repeatability and reproducibility. When comparing the older and newer PET/CT scanners, the PET ED was significantly lower in the newer scanner than in the older scanner (4.39 ± 0.91 mSv and 6.00 ± 1.17 mSv, respectively, p < 0.0001). Consequently, the total ED was significantly lower in the newer scanner than in the older scanner (8.22 ± 1.53 mSv and 9.65 ± 1.34 mSv, respectively, p < 0.0001).

CONCLUSIONS: We successfully developed an automated program for calculating the ED of torso 18F-PET/CT. By integrating a deep learning model, the program effectively eliminated inter-operator variability.

PMID:39728913 | DOI:10.3390/tomography10120151

Tomography. 2024 Dec 18;10(12):2073-2086. doi: 10.3390/tomography10120147.

ABSTRACT

OBJECTIVES: We evaluated the noise reduction effects of deep learning reconstruction (DLR) and hybrid iterative reconstruction (HIR) in brain computed tomography (CT).

METHODS: CT images of a 16 cm dosimetry phantom, a head phantom, and the brains of 11 patients were reconstructed using filtered backprojection (FBP) and various levels of DLR and HIR. The slice thickness was 5, 2.5, 1.25, and 0.625 mm. Phantom imaging was also conducted at various tube currents. The noise reduction ratio was calculated using FBP as the reference. For patient imaging, overall image quality was visually compared between DLR and HIR images that exhibited similar noise reduction ratios.

RESULTS: The noise reduction ratio increased with increasing levels of DLR and HIR in phantom and patient imaging. For DLR, noise reduction was more pronounced with decreasing slice thickness, while such thickness dependence was less evident for HIR. Although the noise reduction effects of DLR were similar between the head phantom and patients, they differed for the dosimetry phantom. Variations between imaging objects were small for HIR. The noise reduction ratio was low at low tube currents for the dosimetry phantom using DLR; otherwise, the influence of the tube current was small. In terms of visual image quality, DLR outperformed HIR in 1.25 mm thick images but not in thicker images.

CONCLUSIONS: The degree of noise reduction using DLR depends on the slice thickness, tube current, and imaging object in addition to the level of DLR, which should be considered in the clinical use of DLR. DLR may be particularly beneficial for thin-slice imaging.

PMID:39728909 | DOI:10.3390/tomography10120147

Tomography. 2024 Dec 12;10(12):2038-2057. doi: 10.3390/tomography10120145.

ABSTRACT

Background/Objectives: Breast cancer is a leading cause of mortality among women in Taiwan and globally. Non-invasive imaging methods, such as mammography and ultrasound, are critical for early detection, yet standalone modalities have limitations in regard to their diagnostic accuracy. This study aims to enhance breast cancer detection through a cross-modality fusion approach combining mammography and ultrasound imaging, using advanced convolutional neural network (CNN) architectures. Materials and Methods: Breast images were sourced from public datasets, including the RSNA, the PAS, and Kaggle, and categorized into malignant and benign groups. Data augmentation techniques were used to address imbalances in the ultrasound dataset. Three models were developed: (1) pre-trained CNNs integrated with machine learning classifiers, (2) transfer learning-based CNNs, and (3) a custom-designed 17-layer CNN for direct classification. The performance of the models was evaluated using metrics such as accuracy and the Kappa score. Results: The custom 17-layer CNN outperformed the other models, achieving an accuracy of 0.964 and a Kappa score of 0.927. The transfer learning model achieved moderate performance (accuracy 0.846, Kappa 0.694), while the pre-trained CNNs with machine learning classifiers yielded the lowest results (accuracy 0.780, Kappa 0.559). Cross-modality fusion proved effective in leveraging the complementary strengths of mammography and ultrasound imaging. Conclusions: This study demonstrates the potential of cross-modality imaging and tailored CNN architectures to significantly improve diagnostic accuracy and reliability in breast cancer detection. The custom-designed model offers a practical solution for early detection, potentially reducing false positives and false negatives, and improving patient outcomes through timely and accurate diagnosis.

PMID:39728907 | DOI:10.3390/tomography10120145

Tomography. 2024 Nov 30;10(12):1930-1946. doi: 10.3390/tomography10120140.

ABSTRACT

Background: Accurate reconstruction of internal temperature fields from surface temperature data is critical for applications such as non-invasive thermal imaging, particularly in scenarios involving small temperature gradients, like those in the human body. Methods: In this study, we employed 3D convolutional neural networks (CNNs) to predict internal temperature fields. The network's performance was evaluated under both ideal and non-ideal conditions, incorporating noise and background temperature variations. A physics-informed loss function embedding the heat equation was used in conjunction with statistical uncertainty during training to simulate realistic scenarios. Results: The CNN achieved high accuracy for small phantoms (e.g., 10 cm in diameter). However, under non-ideal conditions, the network's predictive capacity diminished in larger domains, particularly in regions distant from the surface. The introduction of physical constraints in the training processes improved the model's robustness in noisy environments, enabling accurate reconstruction of hot-spots in deeper regions where traditional CNNs struggled. Conclusions: Combining deep learning with physical constraints provides a robust framework for non-invasive thermal imaging and other applications requiring high-precision temperature field reconstruction, particularly under non-ideal conditions.

PMID:39728902 | DOI:10.3390/tomography10120140

Tomography. 2024 Nov 28;10(12):1915-1929. doi: 10.3390/tomography10120139.

ABSTRACT

Background: Assessment of skeletal maturity is a common clinical practice to investigate adolescent growth and endocrine disorders. The distal radius and ulna (DRU) maturity classification is a practical and easy-to-use scheme that was designed for adolescent idiopathic scoliosis clinical management and presents high sensitivity in predicting the growth peak and cessation among adolescents. However, time-consuming and error-prone manual assessment limits DRU in clinical application. Methods: In this study, we propose a multi-task learning framework with an attention mechanism for the joint segmentation and classification of the distal radius and ulna in hand X-ray images. The proposed framework consists of two sub-networks: an encoder-decoder structure with attention gates for segmentation and a slight convolutional network for classification. Results: With a transfer learning strategy, the proposed framework improved DRU segmentation and classification over the single task learning counterparts and previously reported methods, achieving an accuracy of 94.3% and 90.8% for radius and ulna maturity grading. Findings: Our automatic DRU assessment platform covers the whole process of growth acceleration and cessation during puberty. Upon incorporation into advanced scoliosis progression prognostic tools, clinical decision making will be potentially improved in the conservative and operative management of scoliosis patients.

PMID:39728901 | DOI:10.3390/tomography10120139

Tomography. 2024 Nov 28;10(12):1895-1914. doi: 10.3390/tomography10120138.

ABSTRACT

Background: Early diagnosis of depression is crucial for effective treatment and suicide prevention. Traditional methods rely on self-report questionnaires and clinical assessments, lacking objective biomarkers. Combining functional magnetic resonance imaging (fMRI) with artificial intelligence can enhance depression diagnosis using neuroimaging indicators, but depression-specific fMRI datasets are often small and imbalanced, posing challenges for classification models. New Method: We propose the Spatio-Temporal Aggregation Network (STANet) for diagnosing depression by integrating convolutional neural networks (CNN) and recurrent neural networks (RNN) to capture both temporal and spatial features of brain activity. STANet comprises the following steps: (1) Aggregate spatio-temporal information via independent component analysis (ICA). (2) Utilize multi-scale deep convolution to capture detailed features. (3) Balance data using the synthetic minority over-sampling technique (SMOTE) to generate new samples for minority classes. (4) Employ the attention-Fourier gate recurrent unit (AFGRU) classifier to capture long-term dependencies, with an adaptive weight assignment mechanism to enhance model generalization. Results: STANet achieves superior depression diagnostic performance, with 82.38% accuracy and a 90.72% AUC. The Spatio-Temporal Feature Aggregation module enhances classification by capturing deeper features at multiple scales. The AFGRU classifier, with adaptive weights and a stacked Gated Recurrent Unit (GRU), attains higher accuracy and AUC. SMOTE outperforms other oversampling methods. Additionally, spatio-temporal aggregated features achieve better performance compared to using only temporal or spatial features. Comparison with existing methods: STANet significantly outperforms traditional classifiers, deep learning classifiers, and functional connectivity-based classifiers. Conclusions: The successful performance of STANet contributes to enhancing the diagnosis and treatment assessment of depression in clinical settings on imbalanced and small fMRI.

PMID:39728900 | DOI:10.3390/tomography10120138

Nurs Rep. 2024 Dec 20;14(4):4162-4172. doi: 10.3390/nursrep14040303.

ABSTRACT

BACKGROUND: Optimum efficiency and responsiveness to callers of mental health helplines can only be achieved if call priority is accurately identified. Currently, call operators making a triage assessment rely heavily on their clinical judgment and experience. Due to the significant morbidity and mortality associated with mental illness, there is an urgent need to identify callers to helplines who have a high level of distress and need to be seen by a clinician who can offer interventions for treatment. This study delves into the potential of using machine learning (ML) to estimate call priority from the properties of the callers' voices rather than evaluating the spoken words.

METHOD: Phone callers' speech is first isolated using existing APIs, then features or representations are extracted from the raw speech. These are then fed into a series of deep learning neural networks to classify priority level from the audio representation.

RESULTS: Development of a deep learning neural network architecture that instantly determines positive and negative levels in the input speech segments. A total of 459 call records from a mental health helpline were investigated. The final ML model achieved a balanced accuracy of 92% correct identification of both positive and negative instances of call priority.

CONCLUSIONS: The priority level provides an estimate of voice quality in terms of positive or negative demeanor that can be simultaneously displayed using a web interface on a computer or smartphone.

PMID:39728664 | DOI:10.3390/nursrep14040303

J Xenobiot. 2024 Dec 4;14(4):1901-1918. doi: 10.3390/jox14040101.

ABSTRACT

In the field of computational chemistry, computer models are quickly and cheaply constructed to predict toxicology hazards and results, with no need for test material or animals as these computational predictions are often based on physicochemical properties of chemical structures. Multiple methodologies are employed to support in silico assessments based on machine learning (ML) and deep learning (DL). This review introduces the development of computational toxicology, focusing on ML and DL and emphasizing their importance in the field of toxicology. A fine balance between target potency, selectivity, absorption, distribution, metabolism, excretion, toxicity (ADMET) and clinical safety properties should be achieved to discover a potential new drug. It is advantageous to perform virtual predictions as early as possible in drug development processes, even before a molecule is synthesized. Currently, there are numerous commercially available and free web-based programs for toxicity prediction, which can be used to construct various predictive models. The key features of the QSAR method are also outlined, and the selection of appropriate physicochemical descriptors is a prerequisite for robust predictions. In addition, examples of open-source tools applied to toxicity prediction are included, as well as examples of the application of different computational methods for the prediction of toxicity in drug design and environmental toxicology.

PMID:39728409 | DOI:10.3390/jox14040101

J Imaging. 2024 Dec 23;10(12):333. doi: 10.3390/jimaging10120333.

ABSTRACT

Alzheimer's disease (AD), a degenerative condition affecting the central nervous system, has witnessed a notable rise in prevalence along with the increasing aging population. In recent years, the integration of cutting-edge medical imaging technologies with forefront theories in artificial intelligence has dramatically enhanced the efficiency of identifying and diagnosing brain diseases such as AD. This paper presents an innovative two-stage automatic auxiliary diagnosis algorithm for AD, based on an improved 3D DenseNet segmentation model and an improved MobileNetV3 classification model applied to brain MR images. In the segmentation network, the backbone network was simplified, the activation function and loss function were replaced, and the 3D GAM attention mechanism was introduced. In the classification network, firstly, the CA attention mechanism was added to enhance the model's ability to capture positional information of disease features; secondly, dilated convolutions were introduced to extract richer features from the input feature maps; and finally, the fully connected layer of MobileNetV3 was modified and the idea of transfer learning was adopted to improve the model's feature extraction capability. The results of the study showed that the proposed approach achieved classification accuracies of 97.85% for AD/NC, 95.31% for MCI/NC, 93.96% for AD/MCI, and 92.63% for AD/MCI/NC, respectively, which were 3.1, 2.8, 2.6, and 2.8 percentage points higher than before the improvement. Comparative and ablation experiments have validated the proposed classification performance of this method, demonstrating its capability to facilitate an accurate and efficient automated auxiliary diagnosis of AD, offering a deep learning-based solution for it.

PMID:39728230 | DOI:10.3390/jimaging10120333

Physiol Plant. 2025 Jan-Feb;177(1):e70011. doi: 10.1111/ppl.70011.

ABSTRACT

As an important source of pollution in the papermaking process, the presence of lignin in poplar can seriously affect the quality and process of pulping. During lignin synthesis, Caffeoyl-CoA-O methyltransferase (CCoAOMT), as a specialized catalytic transferase, can effectively regulate the methylation of caffeoyl-coenzyme A (CCoA) to feruloyl-coenzyme A. Targeting CCoAOMT, this study investigated the substrate recognition mechanism and the possible reaction mechanism, the key residues of lignin binding were mutated and the lignin content was validated by deep convolutional neural-network model based on genome-wide prediction (DCNGP). The molecular mechanics results indicate that the binding of S-adenosyl methionine (SAM) and CCoA is sequential, with SAM first binding and inducing inward constriction of the CCoAOMT; then CCoA binds to the pocket, and this process closes the outer channel, preventing contamination by impurities and ensuring that the reaction proceeds. Next, the key residues in the recognition process of SAM (F69 and D91) and CCoA (I40, N170, Y188 and D218) were analyzed, and we identified that K146 as a base catalyst is important for inducing the methylation reaction. Immediately after that, the possible methylation reaction mechanism was deduced by the combination of Restrained Electrostatic Potential (RESP) and Independent Gradient Model (IGM) analysis, focusing on the catalytic center electron cloud density and RESP charge distribution. Finally, the DCNGP results verified that the designed mutant groups were all able to effectively reduce the lignin content and increase the S-lignin content/ G-lignin content ratio, which was beneficial for the subsequent lignin removal. Multifaceted consideration of factors that reduce lignin content and combined deep learning to screen for favorable mutations in target traits provides new ideas for targeted breeding of low-lignin poplars.

PMID:39727026 | DOI:10.1111/ppl.70011

Brief Bioinform. 2024 Nov 22;26(1):bbae672. doi: 10.1093/bib/bbae672.

ABSTRACT

Identifying phage-host interactions (PHIs) is a crucial step in developing phage therapy, which is the promising solution to addressing the issue of antibiotic resistance in superbugs. However, the lifestyle of phages, which strongly depends on their host for life activities, limits their cultivability, making the study of predicting PHIs time-consuming and labor-intensive for traditional wet lab experiments. Although many deep learning (DL) approaches have been applied to PHIs prediction, most DL methods are predominantly based on sequence information, failing to comprehensively model the intricate relationships within PHIs. Moreover, most existing approaches are limited for sub-optimal performance, due to the potential risk of overfitting induced by the highly data sparsity in the task of PHIs prediction. In this study, we propose a novel approach called MI-RGC, which introduces mutual information for feature augmentation and employs regional graph convolution to learn meaningful representations. Specifically, MI-RGC treats the presence status of phages in environmental samples as random variables, and derives the mutual information between these random variables as the dependency relationships among phages. Consequently, a mutual information-based heterogeneous network is construted as feature augmentation for sequence information of phages, which is utilized for building a sequence information-based heterogeneous network. By considering the different contributions of neighboring nodes at varying distances, a regional graph convolutional model is designed, in which the neighboring nodes are segmented into different regions and a regional-level attention mechanism is employed to derive node embeddings. Finally, the embeddings learned from these two networks are aggregated through an attention mechanism, on which the prediction of PHIs is condcuted accordingly. Experimental results on three benchmark datasets demonstrate that MI-RGC derives superior performance over other methods on the task of PHIs prediction.

PMID:39727002 | DOI:10.1093/bib/bbae672

Proc (IEEE Int Conf Healthc Inform). 2024 Jun;2024:305-311. doi: 10.1109/ichi61247.2024.00046. Epub 2024 Aug 22.

ABSTRACT

Delirium is an acute decline or fluctuation in attention, awareness, or other cognitive function that can lead to serious adverse outcomes. Despite the severe outcomes, delirium is frequently unrecognized and uncoded in patients' electronic health records (EHRs) due to its transient and diverse nature. Natural language processing (NLP), a key technology that extracts medical concepts from clinical narratives, has shown great potential in studies of delirium outcomes and symptoms. To assist in the diagnosis and phenotyping of delirium, we formed an expert panel to categorize diverse delirium symptoms, composed annotation guidelines, created a delirium corpus with diverse delirium symptoms, and developed NLP methods to extract delirium symptoms from clinical notes. We compared 5 state-of-the-art transformer models including 2 models (BERT and RoBERTa) from the general domain and 3 models (BERT_MIMIC, RoBERTa_MIMIC, and GatorTron) from the clinical domain. GatorTron achieved the best strict and lenient F1 scores of 0.8055 and 0.8759, respectively. We conducted an error analysis to identify challenges in annotating delirium symptoms and developing NLP systems. To the best of our knowledge, this is the first large language model-based delirium symptom extraction system. Our study lays the foundation for the future development of computable phenotypes and diagnosis methods for delirium.

PMID:39726986 | PMC:PMC11670120 | DOI:10.1109/ichi61247.2024.00046

Front Bioeng Biotechnol. 2024 Dec 11;12:1486789. doi: 10.3389/fbioe.2024.1486789. eCollection 2024.

ABSTRACT

This scoping review summarizes two emerging electrical impedance technologies: electrical impedance myography (EIM) and electrical impedance tomography (EIT). These methods involve injecting a current into tissue and recording the response at different frequencies to understand tissue properties. The review discusses basic methods and trends, particularly the use of electrodes: EIM uses electrodes for either injection or recording, while EIT uses them for both. Ag/AgCl electrodes are prevalent, and current injection is preferred over voltage injection due to better resistance to electrode wear and impedance changes. Advances in digital processing and integrated circuits have shifted EIM and EIT toward digital acquisition, using voltage-controlled current sources (VCCSs) that support multiple frequencies. The review details powerful processing algorithms and reconstruction tools for EIT and EIM, examining their strengths and weaknesses. It also summarizes commercial devices and clinical applications: EIT is effective for detecting cancerous tissue and monitoring pulmonary issues, while EIM is used for neuromuscular disease detection and monitoring. The role of machine learning and deep learning in advancing diagnosis, treatment planning, and monitoring is highlighted. This review provides a roadmap for researchers on device evolution, algorithms, reconstruction tools, and datasets, offering clinicians and researchers information on commercial devices and clinical studies for effective use and innovative research.

PMID:39726983 | PMC:PMC11670078 | DOI:10.3389/fbioe.2024.1486789

Front Artif Intell. 2024 Dec 11;7:1312115. doi: 10.3389/frai.2024.1312115. eCollection 2024.

ABSTRACT

The ability to accurately predict the yields of different crop genotypes in response to weather variability is crucial for developing climate resilient crop cultivars. Genotype-environment interactions introduce large variations in crop-climate responses, and are hard to factor in to breeding programs. Data-driven approaches, particularly those based on machine learning, can help guide breeding efforts by factoring in genotype-environment interactions when making yield predictions. Using a new yield dataset containing 93,028 records of soybean hybrids across 159 locations, 28 states, and 13 years, with 5,838 distinct genotypes and daily weather data over a 214-day growing season, we developed two convolutional neural network (CNN) models: one that integrates CNN and fully-connected neural networks (CNN model), and another that incorporates a long short-term memory (LSTM) layer after the CNN component (CNN-LSTM model). By applying the Generalized Ensemble Method (GEM), we combined the CNN-based models and optimized their weights to improve overall predictive performance. The dataset provided unique genotype information on seeds, enabling an investigation into the potential of planting different genotypes based on weather variables. We employed the proposed GEM model to identify the best-performing genotypes across various locations and weather conditions, making yield predictions for all potential genotypes in each specific setting. To assess the performance of the GEM model, we evaluated it on unseen genotype-location combinations, simulating real-world scenarios where new genotypes are introduced. By combining the base models, the GEM ensemble approach provided much better prediction accuracy compared to using the CNN-LSTM model alone and slightly better accuracy than the CNN model, as measured by both RMSE and MAE on the validation and test sets. The proposed data-driven approach can be valuable for genotype selection in scenarios with limited testing years. In addition, we explored the impact of incorporating state-level soil data alongside the weather, location, genotype and year variables. Due to data constraints, including the absence of latitude and longitude details, we used uniform soil variables for all locations within the same state. This limitation restricted our spatial information to state-level knowledge. Our findings suggested that integrating state-level soil variables did not substantially enhance the predictive capabilities of the models. We also performed a feature importance analysis using RMSE change to identify crucial predictors. Location showed the highest RMSE change, followed by genotype and year. Among weather variables, maximum direct normal irradiance (MDNI) and average precipitation (AP) displayed higher RMSE changes, indicating their importance.

PMID:39726891 | PMC:PMC11670329 | DOI:10.3389/frai.2024.1312115

Front Artif Intell. 2024 Dec 12;7:1474932. doi: 10.3389/frai.2024.1474932. eCollection 2024.

ABSTRACT

The construction industry is rapidly adopting Industry 4.0 technologies, creating new opportunities to address persistent environmental and operational challenges. This review focuses on how Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) are being leveraged to tackle these issues. It specifically explores AI's role in predicting air pollution, improving material quality, monitoring worker health and safety, and enhancing Cyber-Physical Systems (CPS) for construction. This study evaluates various AI and ML models, including Artificial Neural Networks (ANNs) and Support Vector Machines SVMs, as well as optimization techniques like whale and moth flame optimization. These tools are assessed for their ability to predict air pollutant levels, improve concrete quality, and monitor worker safety in real time. Research papers were also reviewed to understand AI's application in predicting the compressive strength of materials like cement mortar, fly ash, and stabilized clay soil. The performance of these models is measured using metrics such as coefficient of determination (R 2), Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE). Furthermore, AI has shown promise in predicting and reducing emissions of air pollutants such as PM2.5, PM10, NO2, CO, SO2, and O3. In addition, it improves construction material quality and ensures worker safety by monitoring health indicators like standing postures, electrocardiogram, and galvanic skin response. It is also concluded that AI technologies, including Explainable AI and Petri Nets, are also making advancements in CPS for the construction industry. The models' performance metrics indicate they are well-suited for real-time construction operations. The study highlights the adaptability and effectiveness of these technologies in meeting current and future construction needs. However, gaps remain in certain areas of research, such as broader AI integration across diverse construction environments and the need for further validation of models in real-world applications. Finally, this research underscores the potential of AI and ML to revolutionize the construction industry by promoting sustainable practices, improving operational efficiency, and addressing safety concerns. It also provides a roadmap for future research, offering valuable insights for industry stakeholders interested in adopting AI technologies.

PMID:39726889 | PMC:PMC11669660 | DOI:10.3389/frai.2024.1474932