Simultaneous whole-liver water T 1 $$ {\mathrm{T}}_1 $$ and T 2 $$ {\mathrm{T}}_2 $$ mapping with isotropic resolution during free-breathing.

PURPOSE: To develop and validate a data acquisition scheme combined with a motion-resolved reconstruction and dictionary-matching-based parameter estimation to enable free-breathing isotropic resolution self-navigated whole-liver simultaneous water-specific T 1 $$ {\mathrm{T}}_1 $$ ( wT 1 $$ {\mathrm{wT}}_1 $$ ) and T 2 $$ {\mathrm{T}}_2 $$ ( wT 2 $$ {\mathrm{wT}}_2 $$ ) mapping for the characterization of diffuse and oncological liver diseases. METHODS: The proposed data acquisition consists of a magnetization preparation pulse and a two-echo gradient echo readout with a radial stack-of-stars trajectory, repeated with different preparations to achieve different T 1 $$ {\mathrm{T}}_1 $$ and T 2 $$ {\mathrm{T}}_2 $$ contrasts in a fixed acquisition time of 6 min. Regularized reconstruction was performed using self-navigation to account for motion during the free-breathing acquisition, followed by water-fat separation. Bloch simulations of the sequence were applied to optimize the sequence timing for B 1 $$ {B}_1 $$ insensitivity at 3 T, to correct for relaxation-induced blurring, and to map T 1 $$ {\mathrm{T}}_1 $$ and T 2 $$ {\mathrm{T}}_2 $$ using a dictionary. The proposed method was validated on a water-fat phantom with varying relaxation properties and in 10 volunteers against imaging and spectroscopy reference values. The performance and robustness of the proposed method were evaluated in five patients with abdominal pathologies. RESULTS: Simulations demonstrate good B 1 $$ {B}_1 $$ insensitivity of the proposed method in measuring T 1 $$ {\mathrm{T}}_1 $$ and T 2 $$ {\mathrm{T}}_2 $$ values. The proposed method produces co-registered wT 1 $$ {\mathrm{wT}}_1 $$ and wT 2 $$ {\mathrm{wT}}_2 $$ maps with a good agreement with reference methods (phantom: wT 1 = 1 . 02 wT 1,ref - 8 . 93 ms , R 2 = 0 . 991 $$ {\mathrm{wT}}_1=1.02\kern0.1em {\mathrm{wT}}_{1,\mathrm{ref}}-8.93\kern0.1em \mathrm{ms},{R}^2=0.991 $$ ; wT 2 = 1 . 03 wT 2,ref + 0 . 73 ms , R 2 = 0 . 995 $$ {\mathrm{wT}}_2=1.03\kern0.1em {\mathrm{wT}}_{2,\mathrm{ref}}+0.73\kern0.1em \mathrm{ms},{R}^2=0.995 $$ ). The proposed wT 1 $$ {\mathrm{wT}}_1 $$ and wT 2 $$ {\mathrm{wT}}_2 $$ mapping exhibits good repeatability and can be robustly performed in patients with pathologies. CONCLUSIONS: The proposed method allows whole-liver wT 1 $$ {\mathrm{wT}}_1 $$ and wT 2 $$ {\mathrm{wT}}_2 $$ quantification with high accuracy at isotropic resolution in a fixed acquisition time during free-breathing.

HE2Gene: image-to-RNA translation via multi-task learning for spatial transcriptomics data.

MOTIVATION: Tissue context and molecular profiling are commonly used measures in understanding normal development and disease pathology. In recent years, the development of spatial molecular profiling technologies (e.g. spatial resolved transcriptomics) has enabled the exploration of quantitative links between tissue morphology and gene expression. However, these technologies remain expensive and time-consuming, with subsequent analyses necessitating high-throughput pathological annotations. On the other hand, existing computational tools are limited to predicting only a few dozen to several hundred genes, and the majority of the methods are designed for bulk RNA-seq. RESULTS: In this context, we propose HE2Gene, the first multi-task learning-based method capable of predicting tens of thousands of spot-level gene expressions along with pathological annotations from H&E-stained images. Experimental results demonstrate that HE2Gene is comparable to state-of-the-art methods and generalizes well on an external dataset without the need for re-training. Moreover, HE2Gene preserves the annotated spatial domains and has the potential to identify biomarkers. This capability facilitates cancer diagnosis and broadens its applicability to investigate gene-disease associations. AVAILABILITY AND IMPLEMENTATION: The source code and data information has been deposited at https://github.com/Microbiods/HE2Gene.

Multilabel Feature Selection via Shared Latent Sublabel Structure and Simultaneous Orthogonal Basis Clustering.

Multilabel feature selection solves the dimension distress of high-dimensional multilabel data by selecting the optimal subset of features. Noisy and incomplete labels of raw multilabel data hinder the acquisition of label-guided information. In existing approaches, mapping the label space to a low-dimensional latent space by semantic decomposition to mitigate label noise is considered an effective strategy. However, the decomposed latent label space contains redundant label information, which misleads the capture of potential label relevance. To eliminate the effect of redundant information on the extraction of latent label correlations, a novel method named SLOFS via shared latent sublabel structure and simultaneous orthogonal basis clustering for multilabel feature selection is proposed. First, a latent orthogonal base structure shared (LOBSS) term is engineered to guide the construction of a redundancy-free latent sublabel space via the separated latent clustering center structure. The LOBSS term simultaneously retains latent sublabel information and latent clustering center structure. Moreover, the structure and relevance information of nonredundant latent sublabels are fully explored. The introduction of graph regularization ensures structural consistency in the data space and latent sublabels, thus helping the feature selection process. SLOFS employs a dynamic sublabel graph to obtain a high-quality sublabel space and uses regularization to constrain label correlations on dynamic sublabel projections. Finally, an effective convergence provable optimization scheme is proposed to solve the SLOFS method. The experimental studies on the 18 datasets demonstrate that the presented method performs consistently better than previous feature selection methods.

Identification of mIDH1 R132C/S280F Inhibitors from Natural Products by Integrated Molecular Docking, Pharmacophore Modeling and Molecular Dynamics Simulations.

Mutant isocitrate dehydrogenase 1 (mIDH1) is a common driving factor in acute myeloid leukemia (AML), with the R132 mutation accounting for a high proportion. The U.S. Food and Drug Administration (FDA) approved Ivosidenib, a molecular entity that targets IDH1 with R132 mutations, as a promising therapeutic option for AML with mIDH1 in 2018. It was of concern that the occurrence of disease resistance or recurrence, attributed to the IDH1 R132C/S280F second site mutation, was observed in certain patients treated with Ivosidenib within the same year. Furthermore, it should be noted that most mIDH1 inhibitors demonstrated limited efficacy against mutations at this specific site. Therefore, there is an urgent need to investigate novel inhibitors targeting mIDH1 for combating resistance caused by IDH1 R132C/S280F mutations in AML. This study aimed to identify novel mIDH1 R132C/S280F inhibitors through an integrated strategy of combining virtual screening and dynamics simulations. First, 2000 hits were obtained through structure-based virtual screening of the COCONUT database, and hits with better scores than -10.67 kcal/mol were obtained through molecular docking. A total of 12 potential small molecule inhibitors were identified through pharmacophore modeling screening and Prime MM-GBSA. Dynamics simulations were used to study the binding modes between the positive drug and the first three hits and IDH1 carrying the R132C/S280F mutation. RMSD showed that the four dynamics simulation systems remained stable, and RMSF and Rg showed that the screened molecules have similar local flexibility and tightness to the positive drug. Finally, the lowest energy conformation, hydrogen bond analysis, and free energy decomposition results indicate that in the entire system the key residues LEU120, TRP124, TRP267, and VAL281 mainly contribute van der Waals forces to the interaction, while the key residues VAL276 and CYS379 mainly contribute electrostatic forces.

Autonomous closed-loop mechanistic investigation of molecular electrochemistry via automation.

Electrochemical research often requires stringent combinations of experimental parameters that are demanding to manually locate. Recent advances in automated instrumentation and machine-learning algorithms unlock the possibility for accelerated studies of electrochemical fundamentals via high-throughput, online decision-making. Here we report an autonomous electrochemical platform that implements an adaptive, closed-loop workflow for mechanistic investigation of molecular electrochemistry. As a proof-of-concept, this platform autonomously identifies and investigates an EC mechanism, an interfacial electron transfer (E step) followed by a solution reaction (C step), for cobalt tetraphenylporphyrin exposed to a library of organohalide electrophiles. The generally applicable workflow accurately discerns the EC mechanism's presence amid negative controls and outliers, adaptively designs desired experimental conditions, and quantitatively extracts kinetic information of the C step spanning over 7 orders of magnitude, from which mechanistic insights into oxidative addition pathways are gained. This work opens opportunities for autonomous mechanistic discoveries in self-driving electrochemistry laboratories without manual intervention.

Harnessing natural product polysaccharides against lung cancer and revisit its novel mechanism.

The incidence and mortality of lung cancer are on the rise worldwide. However, the benefit of clinical treatment in lung cancer is limited. Owning to important sources of drug development, natural products have received constant attention around the world. Main ingredient polysaccharides in natural products have been found to have various activities in pharmacological research. In recent years, more and more scientists are looking for the effects and mechanisms of different natural product polysaccharides on lung cancer. In this review, we focus on the following aspects: First, natural product polysaccharides have been discovered to directly suppress the growth of lung cancer cells, which can be effective in limiting tumor progression. Additionally, polysaccharides have been considered to enhance immune function, which can play a pivotal role in fighting lung cancer. Lastly, polysaccharides can improve the efficacy of drugs in lung cancer treatment by regulating the gut microbiota. Overall, the research of natural product polysaccharides in the treatment of lung cancer is a promising area that has the potential to lead to new clinical treatments. With better understanding, natural product polysaccharides have the potential to become important components of future lung cancer treatments.

SC-AIR-BERT: a pre-trained single-cell model for predicting the antigen-binding specificity of the adaptive immune receptor.

Accurately predicting the antigen-binding specificity of adaptive immune receptors (AIRs), such as T-cell receptors (TCRs) and B-cell receptors (BCRs), is essential for discovering new immune therapies. However, the diversity of AIR chain sequences limits the accuracy of current prediction methods. This study introduces SC-AIR-BERT, a pre-trained model that learns comprehensive sequence representations of paired AIR chains to improve binding specificity prediction. SC-AIR-BERT first learns the 'language' of AIR sequences through self-supervised pre-training on a large cohort of paired AIR chains from multiple single-cell resources. The model is then fine-tuned with a multilayer perceptron head for binding specificity prediction, employing the K-mer strategy to enhance sequence representation learning. Extensive experiments demonstrate the superior AUC performance of SC-AIR-BERT compared with current methods for TCR- and BCR-binding specificity prediction.

Government subsidies and innovation in new energy vehicle companies: An empirical study of new energy vehicle listed companies based on Shanghai and Shenzhen A-shares.

The panel data of 50 new energy vehicle enterprises in Shanghai and Shenzhen A-shares from 2012 to 2021 are selected to empirically analyze the impact of government subsidies on the innovation of new energy vehicle enterprises and to further discuss the differences between such an impact in different forms and regions. The study finds that, first, government subsidies have a certain promotion effect on the innovation of new energy vehicle enterprises, and an inverted U-shaped relationship exists thereof. Second, at the enterprise level, government subsidies have a significant effect on the innovation of non-state enterprises, downstream vehicle enterprises, and enterprises with lower establishment years, and the inverted-U trend is evident. Third, at the regional level, government subsidies have a more significant effect on the innovation of enterprises in non-eastern regions and low-environmental regulation regions, and the inverted-U-shaped relationship trend is more apparent. The study establishes the nonlinear relationship between government subsidies and the innovation of new energy vehicle enterprises through empirical research, which expands the theory of enterprise innovation and has a certain guiding significance for improving the innovation capability of new energy vehicle enterprises in the future.

Local Community Detection Algorithm Based on Alternating Strategy of Strong Fusion and Weak Fusion.

Existing fusion-based local community detection algorithms have achieved good results. However, when assigning a node to a community, similarity functions are sometimes used, which only use node information, while ignoring connection information within the community. These algorithms sometimes fail to find influential nodes, which eventually leads to the failure to find a complete local community. To address these problems, a new local community detection algorithm is proposed in this article. Two strategies, of strong fusion followed by weak fusion, are used alternately to fuse nodes. Compared with using two fusion strategies alone, the alternating loop method can improve the solution of the algorithm in each stage. In strong fusion, we propose a new membership function that considers both node information and connection information in the local community. This improves the quality of the fused node while preserving the structure of the current community. In weak fusion, we propose a parameter-based similarity measure, which can detect influential nodes for a local community. We also propose a local community evaluation metric, which does not require true division to determine the optimal local community under different parameters. Experiments, compared to six state-of-the-art algorithms, show that the proposed algorithm improves accuracy and stability, and also demonstrate the effectiveness of the new local community evaluation metrics in parameter selection.

Electrochemical Mechanistic Analysis from Cyclic Voltammograms Based on Deep Learning.

For decades, employing cyclic voltammetry for mechanistic investigation has demanded manual inspection of voltammograms. Here, we report a deep-learning-based algorithm that automatically analyzes cyclic voltammograms and designates a probable electrochemical mechanism among five of the most common ones in homogeneous molecular electrochemistry. The reported algorithm will aid researchers' mechanistic analyses, utilize otherwise elusive features in voltammograms, and experimentally observe the gradual mechanism transitions encountered in electrochemistry. An automated voltammogram analysis will aid the analysis of complex electrochemical systems and promise autonomous high-throughput research in electrochemistry with minimal human interference.

Explore the mechanism and substance basis of Mahuang FuziXixin Decoction for the treatment of lung cancer based on network pharmacology and molecular docking.

BACKGROUND: Mahuang FuziXixin Decoction (MFXD) is a classic Chinese herbal formula for the treatment of lung cancer. However, its mechanisms of action are unclear. In present study, network pharmacology and molecular docking technology were employed to investigate the molecular mechanism and substance basis of MFXD for the treatment of lung cancer. METHOD: The active compounds and corresponding targets of MFXD were collected through the TCMSP database. OMIM and GeneCards databases were applied to filter the targets of lung cancer. The protein-protein interaction (PPI) were acquired through the STRING platform. Metascape and the Bioinformatics server were used for the visualization of GO and KEGG analysis. The tissue and organ distribution of targets was evaluated based on the BioGPS database. The binding affinity between potential targets and active compounds was evaluated by molecular docking. RESULT: A total of 51 active compounds and 118 targets of MFXD were collected. The target with a higher degree were identified through the PPI network, namely AR, RELA, NCOA1, EGFR, FOS, CCND1, ESR1 and HSP90AA1. GO and KEGG analysis suggested that MFXD treatment of lung cancer mainly involves hormone and response to inorganic substance, transcription regular complex, transcription factor binding and Pathways in cancer. Experimental validation showed that MFXD treatment inhibited the proliferation of NSCLC cells through downregulation the expression of EGFR, HIF1A, NCOA1 and RELA. Moreover, molecular docking revealed that hydrogen bond and hydrophobic interaction contribute to the binding of the compounds to targets. CONCLUSION: Our findings comprehensively elucidated the actives, potential targets, and molecular mechanisms of MFXD against lung cancer, providing a promising strategy for the scientific basis and therapeutic mechanism of traditional Chinese medicine prescriptions for the treatment of the disease.

Recent advances of IDH1 mutant inhibitor in cancer therapy.

Isocitrate dehydrogenase (IDH) is the key metabolic enzyme that catalyzes the conversion of isocitrate to α-ketoglutarate (α-KG). Two main types of IDH1 and IDH2 are present in humans. In recent years, mutations in IDH have been observed in several tumors, including glioma, acute myeloid leukemia, and chondrosarcoma. Among them, the frequency of IDH1 mutations is higher than IDH2. IDH1 mutations have been shown to increase the conversion of α-KG to 2-hydroxyglutarate (2-HG). IDH1 mutation-mediated accumulation of 2-HG leads to epigenetic dysregulation, altering gene expression, and impairing cell differentiation. A rapidly emerging therapeutic approach is through the development of small molecule inhibitors targeting mutant IDH1 (mIDH1), as evidenced by the recently approved of the first selective IDH1 mutant inhibitor AG-120 (ivosidenib) for the treatment of IDH1-mutated AML. This review will focus on mIDH1 as a therapeutic target and provide an update on IDH1 mutant inhibitors in development and clinical trials.

A Novel Cuprotosis-Related Gene FDX1 Signature for Overall Survival Prediction in Clear Cell Renal Cell Carcinoma Patients.

Background: Ferredoxin 1 (FDX1) is a newly discovered gene regulating cuprotosis. However, the effect of FDX1 expression on clear renal cell carcinoma (ccRCC) is unknown. Methods: Gene expression profiles and clinical data of ccRCC patients were downloaded from the Cancer Genome Atlas (TCGA) database. The differences in FDX1 expression between ccRCC and nonneoplastic tissues adjacent to cancer were analyzed by R software. The results were validated by GEO data, quantitative real-time polymerase chain reaction (qRT-PCR), western blotting (WB), and immunohistochemistry (IHC). Chi-square test was used to analyze the clinical pathological parameters. Kaplan-Meier survival analysis and Cox proportional hazard regression model selection were used to evaluate the effect of FDX1 expression on overall survival. Protein interaction networks were used to analyze other proteins that interact with FDX1. Signal pathway analysis was performed for possible FDX1 enrichment using GSEA and ssGSEA algorithms. Pan-cancer analysis of FDX1 was carried out through TCGA database. Results: The FDX1 expression in nontumor tissues was significantly higher than that in ccRCC, and the expression difference was verified by GEO data, qRT-PCR, WB, and IHC. The high expression of FDX1 was significantly related to the well overall survival rate (P < 0.05). The chi-square test showed that the high expression of FDX1 was related to gender, TNM stage, T stage, lymph node metastasis, and pathological grade. Additionally, the FDX1 expression level was different in groups classified based on pathological grade, gender, TNM stage, T stage, lymph node metastasis, and distant metastasis (P < 0.05). The multivariate analysis revealed the high expression of FDX1 as an important independent predictor for overall survival. STRING database results showed that LIAS and LIPT1 may interact with FDX1 in the PPI network, which are also involved in the regulation of cuprotosis. The GSEA and ssGSEA results showed that the FDX1 was enriched in the anticancer pathway. The FDX1 high expression is associated with better prognosis in many cancers, as revealed by pan-cancer analysis. Conclusion: FDX1 may play a role in the progression of ccRCC as a tumor suppressor gene. It can be used as a potential prognostic indicator and therapeutic target of ccRCC. However, the cuprotosis regulatory role in the development of ccRCC needs to be further verified.

Living Donor Intestinal Transplantation: Recipient Outcomes.

OBJECTIVE: To examine outcomes of living-donor intestinal transplant (LDITx) recipients. BACKGROUND: LDITx is not routinely performed because of surgical risks to the donor and the potential inferior physiologic performance of the segmental graft. However, data on the effectiveness of LDITx are scarce. DESIGN: This retrospective cohort study included patients undergoing LDITx between May 1999 and December 2021 in intestinal transplant programs in 2 university-affiliated hospitals in China. RESULTS: Actuarial survival rates were 80%, 72.7%, 66.7% for patient and 72.4%, 63.6%, 60% for graft at 1, 3, and 5 years, respectively. Recipients with >3/6 HLA-matched grafts had superior patient and graft survival rates than those with ≤3/6 HLA-matched grafts ( P <0.05). There were 12 deaths among the recipients, with infection being the leading cause (41.7%), followed by rejection (33.3%), surgical complications (16.7%), and others (8.3%). There were 16 graft losses among the recipients, with acute cellular rejection being the predominant cause (37.5%), followed by infection (25%), technical failure (12.5%), chronic rejection (12.5%), and others (12.5%). With an average follow-up of 3.7 (range, 0.6-23) years, the rates of acute and chronic rejection were 35% and 5%, and the rate of cytomegalovirus disease and post-transplant lymphoproliferative disease were 5% and 2.5%, respectively. Of the 40 patients, 28 (70%) are currently alive and have achieved enteral autonomy. CONCLUSIONS: LDITx is a valuable treatment option for patients with end-stage intestinal failure. Improved immunosuppression, better HLA matching, and shorter cold ischemia times were associated with reduced rates of rejection, viral-mediated infection and improved graft survival.

Mini-review: Recent advances in post-translational modification site prediction based on deep learning.

Post-translational modifications (PTMs) are closely linked to numerous diseases, playing a significant role in regulating protein structures, activities, and functions. Therefore, the identification of PTMs is crucial for understanding the mechanisms of cell biology and diseases therapy. Compared to traditional machine learning methods, the deep learning approaches for PTM prediction provide accurate and rapid screening, guiding the downstream wet experiments to leverage the screened information for focused studies. In this paper, we reviewed the recent works in deep learning to identify phosphorylation, acetylation, ubiquitination, and other PTM types. In addition, we summarized PTM databases and discussed future directions with critical insights.

Association of PD-1/PD-L1 expression and Epstein--Barr virus infection in patients with invasive breast cancer.

PURPOSE: Causative factors of breast cancer include infections, such as Epstein-Barr virus (EBV) infection. The aim of this study was to analyze the clinicopathological features of EBV-positive (IBC) and determine if EBV affects programmed cell death receptor 1 (PD-1)/PD ligand 1 (PD-L1) expression in IBC, similar to other EBV-infected tumors with PD-L1/PD-1 expression. METHODS: We collected 140 samples of IBC tissues and 25 samples of adjacent tissues. All patients were followed-up by telephone from the day of surgery to December 2020. Chromogenic in-situ hybridization was performed to evaluate EBV-encoded RNA (EBER). Immunohistochemistry was performed to evaluate PD-L1 and PD-1 expressions. The correlation between PD1/PDL1 expression and clinicopathological features was also analyzed. RESULTS: EBER was detected in 57 of 140 (40.7%) IBC tissues and not detected in any adjacent tissue (P < 0.05). Clinicopathologic features of patients were consistent with EBV-associated IBC. EBV infection was correlated with the mass size, menopausal status, axillary lymph node metastasis, vascular invasion, Ki-67 index, clinical stage, and estrogen receptor and progesterone receptor expressions (all P < 0.05), but not with the histological type, invasive ductal carcinoma histological grade, or human epidermal growth factor receptor 2 (HER2) expression (all P > 0.05). The positive rate of PD-1/PD-L1 expression was higher in the EBV-positive group than in the EBV-negative group (P < 0.05). The Kaplan-Meier univariate survival analysis showed that EBV was associated with poor disease-free survival and overall survival in patients with IBC. PD-L1/PD-1 expression could predict a poor prognosis. CONCLUSIONS: In this study, clinicopathologic characteristics of patients were consistent with EBV-infected IBC. Patients with EBV-positive breast cancer were more likely to have elevated PD-1/PDL-1 expression compared to those with EBV-negative breast cancer. This finding could serve as a basis to explore therapeutic targets, particularly immunotherapy, for patients with IBC.

The comprehensive and systematic identification of BLCA-specific SF-regulated, survival-related AS events.

Bladder urothelial carcinoma (BLCA) is a complex disease with high morbidity and mortality. Changes in alternative splicing (AS) and splicing factor (SF) can affect gene expression, thus playing an essential role in tumorigenesis. This study downloaded 412 patients' clinical information and 433 samples of transcriptome profiling data from TCGA. And we collected 48 AS signatures from SpliceSeq. LASSO and Cox analyses were used for identifying survival-related AS events in BLCA. Finally, 1,645 OS-related AS events in 1,129 genes were validated by Kaplan-Meier (KM) survival analysis, ROC analysis, risk curve analysis, and independent prognostic analysis. Finally, our survey provides an AS-SF regulation network consisting of five SFs and 46 AS events. In the end, we profiled genes that AS occurred in pan-cancer and five SFs' expression in tumor and normal samples in BLCA. We selected CLIP-seq data for validation the interaction regulated by RBP. Our study paves the way for potential therapeutic targets of BLCA.

Diagnosis of an extremely rare case of malignant adenomyoepithelioma in pleomorphic adenoma: A case report.

BACKGROUND: Pleomorphic adenoma (PA) is the most common type of salivary gland tumor, and its common sites are parotid gland, sinus, nasal septum and cleft palate. PA is an uncommon benign type of tumor occurring in the breast, and there are few reports of cases in Asia. CASE SUMMARY: An 84-year-old woman found a mass in the upper outer quadrant of the right breast > 1 year ago. The patient underwent a right breast lumpectomy and sentinel lymph node biopsy. The pathological diagnosis was PA in the upper outer quadrant of the right breast, and the malignant component was malignant adenomyoepithelioma. The postoperative course was uneventful, and no chemotherapy was administered. At 18 mo of follow-up, the patient is alive and well, with no evidence of recurrent disease. CONCLUSION: Patients with breast PA should first undergo extended excision of breast masses followed by pathological examination. If malignancy is confirmed or the surgical margin is positive, modified radical mastectomy should be performed.

Human disease prediction from microbiome data by multiple feature fusion and deep learning.

Human disease prediction from microbiome data has broad implications in metagenomics. It is rare for the existing methods to consider abundance profiles from both known and unknown microbial organisms, or capture the taxonomic relationships among microbial taxa, leading to significant information loss. On the other hand, deep learning has shown unprecedented advantages in classification tasks for its feature-learning ability. However, it encounters the opposite situation in metagenome-based disease prediction since high-dimensional low-sample-size metagenomic datasets can lead to severe overfitting; and black-box model fails in providing biological explanations. To circumvent the related problems, we developed MetaDR, a comprehensive machine learning-based framework that integrates various information and deep learning to predict human diseases. Experimental results indicate that MetaDR achieves competitive prediction performance with a reduction in running time, and effectively discovers the informative features with biological insights.

SRG-Vote: Predicting Mirna-Gene Relationships via Embedding and LSTM Ensemble.

Targeted therapy for one for a set of genes has made it possible to apply precision medicine for different patients due to the existence of tumor heterogeneity. However, how to regulate those genes are still problematic. One of the natural regulators of genes is microRNAs. Thus, a better understanding of the miRNA-gene interaction mechanism might contribute to future diagnosis, prevention, and cancer therapy. The interactions between microRNA and genes play an essential role in molecular genetics. The in-vivo experiments validating the relationships between them are time-consuming, money-costly, and labor-intensive. With the development of high-throughput technology, we dealt with tons of biological data. However, extracting features from tremendous raw data and making a mathematical model is still a challenging topic. Machine learning and deep learning algorithms have become powerful tools in dealing with biological data. Inspired by this, in this paper, we propose a model that combines features/embedding extraction methods, deep learning algorithms, and a voting system. We leverage doc2vec to generate sequential embedding from molecular sequences. The role2vec, GCN, and GMM for geometrical embedding were generated from the complex network from similarity and pair-wise datasets. For the deep learning algorithms, we leveraged LSTM and Bi-LSTM according to different embedding and features. Finally, we adopted a voting system to balance results from different data sources. The results have shown that our voting system could achieve a higher AUC than the existing benchmark. The case studies demonstrate that our model could reveal potential relationships between miRNAs and genes. The source code, features, and predictive results can be downloaded at https://github.com/Xshelton/SRG-vote.