DPADM: a novel algorithm for detecting drug-pathway associations based on high-throughput transcriptional response to compounds.

Pathway genes functionally participate in the same biological process. They typically act cooperatively, and none is considered dispensable. The dominant paradigm in drug discovery is the one-to-one strategy, which aims to find the most sensitive drug to act on an individual target. However, many complex diseases, such as cancer, are caused by dysfunction among multiple-gene pathways, not just one. Therefore, identifying pathway genes that are responsive to synthetic compounds in a global physiological environment may be more effective in drug discovery. The high redundancy of crosstalk between biological pathways, though, hints that the covariance matrix, which only connects genes with strong marginal correlations, may miss higher-level interactions, such as group interactions. We herein report the development of DPADM-a Drug-Pathway association Detection Model that infers pathways responsive to specific drugs. This model elucidates higher-level gene-gene interactions by evaluating the conditional dependencies between genes under different drug treatments. The advantage of the proposed method is demonstrated using simulation studies by comparing with another two methods. We applied this model to the Connectivity Map data set (CMap), and demonstrated that DPADM is able to identify many drug-pathway associations, such as mitoxantrone (MTX)- PI3K/AKT association, which targets the topological conditions of DNA transcription. Surprisingly, apart from identifying pathways corresponding to specific drugs, our methodology also revealed new drug-related pathways with functions similarly to those of seed genes.

Identifying local associations in biological time series: algorithms, statistical significance, and applications.

Local associations refer to spatial-temporal correlations that emerge from the biological realm, such as time-dependent gene co-expression or seasonal interactions between microbes. One can reveal the intricate dynamics and inherent interactions of biological systems by examining the biological time series data for these associations. To accomplish this goal, local similarity analysis algorithms and statistical methods that facilitate the local alignment of time series and assess the significance of the resulting alignments have been developed. Although these algorithms were initially devised for gene expression analysis from microarrays, they have been adapted and accelerated for multi-omics next generation sequencing datasets, achieving high scientific impact. In this review, we present an overview of the historical developments and recent advances for local similarity analysis algorithms, their statistical properties, and real applications in analyzing biological time series data. The benchmark data and analysis scripts used in this review are freely available at http://github.com/labxscut/lsareview.

Boosting Clear Cell Renal Carcinoma-Specific Drug Discovery Using a Deep Learning Algorithm and Single-Cell Analysis.

Clear cell renal carcinoma (ccRCC), the most common subtype of renal cell carcinoma, has the high heterogeneity of a highly complex tumor microenvironment. Existing clinical intervention strategies, such as target therapy and immunotherapy, have failed to achieve good therapeutic effects. In this article, single-cell transcriptome sequencing (scRNA-seq) data from six patients downloaded from the GEO database were adopted to describe the tumor microenvironment (TME) of ccRCC, including its T cells, tumor-associated macrophages (TAMs), endothelial cells (ECs), and cancer-associated fibroblasts (CAFs). Based on the differential typing of the TME, we identified tumor cell-specific regulatory programs that are mediated by three key transcription factors (TFs), whilst the TF EPAS1/HIF-2α was identified via drug virtual screening through our analysis of ccRCC's protein structure. Then, a combined deep graph neural network and machine learning algorithm were used to select anti-ccRCC compounds from bioactive compound libraries, including the FDA-approved drug library, natural product library, and human endogenous metabolite compound library. Finally, five compounds were obtained, including two FDA-approved drugs (flufenamic acid and fludarabine), one endogenous metabolite, one immunology/inflammation-related compound, and one inhibitor of DNA methyltransferase (N4-methylcytidine, a cytosine nucleoside analogue that, like zebularine, has the mechanism of inhibiting DNA methyltransferase). Based on the tumor microenvironment characteristics of ccRCC, five ccRCC-specific compounds were identified, which would give direction of the clinical treatment for ccRCC patients.

The application of Caprini Risk Assessment Model in evaluation of deep vein thrombosis for patients with end-stage osteoarthritis before arthroplasty.

BACKGROUND: Deep vein thrombosis (DVT) was a fatal complication of knee arthroplasty. We had neglected the risk factors of preoperative DVT although patients undergoing knee arthroplasty were at high risk for VTE. This study was to determine the risk factors for preoperative DVT and application of Caprini Risk Assessment Model (RAM) in patients with end-stage knee osteoarthritis (OA). METHODS: We retrospectively analyzed 1808 cases with end-stage knee OA undergoing primary knee arthroplasty from May 2015 to December 2020. Based on the results of ultrasonography in lower extremities, all patients were divided into non-DVT group and DVT group. Distribution of risk factors and risk levels were compared using χ2 test between two groups. Binary logistic regression analysis was used to determine the risk factors and relationship of risk levels and preoperative DVT. RESULTS: The incidence of preoperative DVT was 5.53% (n = 100). Distribution of the study population by risk level was low, 4.09%; moderate, 23.95%; high, 66.98%; and highest 4.98%. Female (P = 0.002), age (P = 0.012), swollen legs (P = 0.035) and history of blood clots (P < 0.001) was correlated with preoperative DVT. Difference among four risk levels was significant (P = 0.007). Patients with highest risk level had statistically significant association with preoperative DVT (P = 0.005, OR = 2.93, 95%CI [1.375-6.246]). CONCLUSION: The incidence of preoperative DVT was 5.53% in end-stage knee OA patients. The gender (female) and age were independent risk factors for preoperative DVT. The risk group classification by Caprini RAM was significantly associated with preoperative DVT. The usage of Caprini RAM before knee arthroplasty may be beneficial for prophylaxis of DVT.

The changes of the calf-vein deformation and femoral vein peak velocity during ankle pump exercise with or without graduated compression stockings.

OBJECTIVES: To analyze the changes of lower limb hemodynamics parameters before and after wearing graduated compression stockings (GCS) during ankle pump exercise in patients preparing for arthroplastic surgery. METHOD: The leg veins of 16 patients awaiting arthroplasty were analyzed using a Sonosite M-Turbo ultrasound system during ankle pump exercise with or without GCS. The age of them was 70 ± 7 years (mean ± SD) (range 56-82 years) and body mass index was 25.8 ± 3.0 kg/m2 (range 18.0-30.5 kg/m2). Measured data including the cross-sectional area (CSA), anteroposterior (AP) diameter and lateromedial (LM) diameter of the soleus vein (SV), posterior tibial vein (PTV) and great saphenous vein (GSV). Additionally, the peak velocities of femoral vein (FV) were also measured. RESULTS: GCS could significantly decrease the cross-sectional area of SV, PTV and GSV in supine position at rest and maximum ankle plantar flexion. But the compression effect of GCS to SV and GSV was not observed during maximum ankle dorsiflexion. It was found that GCS application reduced the peak flow velocity of the femoral vein from 61.85 cm/s (95% CI = 50.94-72.75 cm/s) to 38.01 cm/s (95% CI = 28.42-47.59 cm/s) (P < 0.001) during ankle plantar flexion and decreased the femoral vein in these patients from 80.65 cm/s (95% CI = 70.37-90.92 cm/s) to 51.15 cm/s (95% CI = 42.58-59.73 cm/s) (P < 0.001) during ankle dorsiflexion. But this effect was not significant in supine position at rest. CONCLUSIONS: GCS could significantly reduce the peak flow velocity of the femoral vein during ankle pump exercise in the patients preparing for arthroplastic surgery.

Explore mediated co-varying dynamics in microbial community using integrated local similarity and liquid association analysis.

BACKGROUND: Discovering the key microbial species and environmental factors of microbial community and characterizing their relationships with other members are critical to ecosystem studies. The microbial co-occurrence patterns across a variety of environmental settings have been extensively characterized. However, previous studies were limited by their restriction toward pairwise relationships, while there was ample evidence of third-party mediated co-occurrence in microbial communities. METHODS: We implemented and applied the triplet-based liquid association analysis in combination with the local similarity analysis procedure to microbial ecology data. We developed an intuitive scheme to visualize those complex triplet associations along with pairwise correlations. Using a time series from the marine microbial ecosystem as example, we identified pairs of operational taxonomic units (OTUs) where the strength of their associations appeared to relate to the values of a third "mediator" variable. These "mediator" variables appear to modulate the associations between pairs of bacteria. RESULTS: Using this analysis, we were able to assess the OTUs' ability to regulate its functional partners in the community, typically not manifested in the pairwise correlation patterns. For example, we identified Flavobacteria as a multifaceted player in the marine microbial ecosystem, and its clades were involved in mediating other OTU pairs. By contrast, SAR11 clades were not active mediators of the community, despite being abundant and highly correlated with other OTUs. Our results suggested that Flavobacteria are more likely to respond to situations where particles and unusual sources of dissolved organic material are prevalent, such as after a plankton bloom. On the other hand, SAR11s are oligotrophic chemoheterotrophs with inflexible metabolisms, and their relationships with other organisms may be less governed by environmental or biological factors. CONCLUSIONS: By integrating liquid association with local similarity analysis to explore the mediated co-varying dynamics, we presented a novel perspective and a useful toolkit to analyze and interpret time series data from microbial community. Our augmented association network analysis is thus more representative of the true underlying dynamic structure of the microbial community. The analytic software in this study was implemented as new functionalities of the ELSA (Extended local similarity analysis) tool, which is available for free download ( http://bitbucket.org/charade/elsa ).

Integrated metagenomic data analysis demonstrates that a loss of diversity in oral microbiota is associated with periodontitis.

BACKGROUND: Periodontitis is an inflammatory disease affecting the tissues supporting teeth (periodontium). Integrative analysis of metagenomic samples from multiple periodontitis studies is a powerful way to examine microbiota diversity and interactions within host oral cavity. METHODS: A total of 43 subjects were recruited to participate in two previous studies profiling the microbial community of human subgingival plaque samples using shotgun metagenomic sequencing. We integrated metagenomic sequence data from those two studies, including six healthy controls, 14 sites representative of stable periodontitis, 16 sites representative of progressing periodontitis, and seven periodontal sites of unknown status. We applied phylogenetic diversity, differential abundance, and network analyses, as well as clustering, to the integrated dataset to compare microbiological community profiles among the different disease states. RESULTS: We found alpha-diversity, i.e., mean species diversity in sites or habitats at a local scale, to be the single strongest predictor of subjects' periodontitis status (P < 0.011). More specifically, healthy subjects had the highest alpha-diversity, while subjects with stable sites had the lowest alpha-diversity. From these results, we developed an alpha-diversity logistic model-based naive classifier able to perfectly predict the disease status of the seven subjects with unknown periodontal status (not used in training). Phylogenetic profiling resulted in the discovery of nine marker microbes, and these species are able to differentiate between stable and progressing periodontitis, achieving an accuracy of 94.4%. Finally, we found that the reduction of negatively correlated species is a notable signature of disease progression. CONCLUSIONS: Our results consistently show a strong association between the loss of oral microbiota diversity and the progression of periodontitis, suggesting that metagenomics sequencing and phylogenetic profiling are predictive of early periodontitis, leading to potential therapeutic intervention. Our results also support a keystone pathogen-mediated polymicrobial synergy and dysbiosis (PSD) model to explain the etiology of periodontitis. Apart from P. gingivalis, we identified three additional keystone species potentially mediating the progression of periodontitis progression based on pathogenic characteristics similar to those of known keystone pathogens.

Exercise Benefits Coronary Heart Disease.

Coronary heart disease (CHD) is a group of diseases that include: no symptoms, angina, myocardial infarction, ischemia cardiomyopathy and sudden cardiac death. And it results from multiple risks factors consisting of invariable factors (e.g. age, gender, etc.) and variable factors (e.g. dyslipidemia, hypertension, diabetes, smoking, etc.). Meanwhile, CHD could cause impact not only localized in the heart, but also on pulmonary function, whole-body skeletal muscle function, activity ability, psychological status, etc. Nowadays, CHD has been the leading cause of death in the world. However, many clinical researches showed that exercise training plays an important role in cardiac rehabilitation and can bring a lot of benefits for CHD patients.

Exercise Dosing and Prescription-Playing It Safe: Dangers and Prescription.

Cardiac rehabilitation is a comprehensive and multidisciplinary program, and exercise training is extremely crucial in the whole program. In the past decades, many researches have shown the beneficial effects of exercise for cardiovascular disease (CVD) is indisputable Nevertheless, only a well-designed exercise prescription may achieve the ideal benefits. In this chapter, we will have a discussion of what is exercise prescription and how to establish a scientific and appropriate exercise prescription for CVD patients depending on the current scientific evidence and recommendations.

Decreased MiR-155 Level in the Peripheral Blood of Non-Alcoholic Fatty Liver Disease Patients may Serve as a Biomarker and may Influence LXR Activity.

BACKGROUND: Obesity is now a common risk factor for non-alcoholic fatty liver disease (NAFLD). Thus, it is important to explore its underlying mechanisms. METHODS: Total RNA was extracted from peripheral whole blood samples from 50 NAFLD patients and 50 healthy controls. In addition, human liver specimens were obtained through liver biopsies from NAFLD patients and healthy controls. The level of miRNA was studied using real-time PCR. The expression of lipogenic genes was analyzed using western blot, and a dual luciferase reporter assay was conducted to identify the possible target gene. Adenovirus vectors were injected into the tail vein of the high fat diet (HFD)-fed mice to study the role of miR-155 on lipid accumulation in vivo. RESULTS: The level of miR-155 was markedly reduced in the livers and peripheral blood of NAFLD patients compared with healthy controls. Upregulation of miR-155 decreased intracellular lipid content and the SREBP1 and FAS protein levels, while inhibition of miR-155 enhanced the intracellular lipid content. The dual luciferase reporter assay showed that Liver X receptor (LXR)α was the target gene of miR-155, and silencing miR-155 reduced the expression of SREBP1 and FAS. An in vivo study showed that upregulation of miR-155 decreased the hepatic lipid accumulation mainly by suppressing the LXRα-dependent lipogenic signaling pathway. CONCLUSIONS: In summary, decreased expression of miR-155 in the peripheral blood may be utilized as a potential novel biomarker for NAFLD screening mainly by targeting LXRα.

Pseudogene PTENP1 Functions as a Competing Endogenous RNA (ceRNA) to Regulate PTEN Expression by Sponging miR-499-5p.

Increasing evidence has shown that pseudogenes can widely regulate gene expression. However, little is known about the specific role of PTENP1 and miR-499-5p in insulin resistance. The relative transcription level of PTENP1 was examined in db/db mice and high fat diet (HFD)-fed mice by real-time PCR. To explore the effect of PTENP1 on insulin resistance, adenovirus overexpressing or inhibiting vectors were injected through the tail vein. Bioinformatics predictions and a luciferase reporter assay were used to explore the interaction between PTENP1 and miR-499-5p. The relative transcription level of PTENP1 was largely enhanced in db/db mice and HFD-fed mice. Furthermore, the overexpression of PTENP1 resulted in impaired Akt/GSK activation as well as glycogen synthesis, while PTENP1 inhibition led to the improved activation of Akt/GSK and enhanced glycogen contents. More importantly, PTENP1 could directly bind miR-499-5p, thereby becoming a sink for miR-499-5p. PTENP1 overexpression results in the impairment of the insulin-signaling pathway and may function as a competing endogenous RNA for miR-499-5p, thereby contributing to insulin resistance.

Statistical significance approximation in local trend analysis of high-throughput time-series data using the theory of Markov chains.

BACKGROUND: Local trend (i.e. shape) analysis of time series data reveals co-changing patterns in dynamics of biological systems. However, slow permutation procedures to evaluate the statistical significance of local trend scores have limited its applications to high-throughput time series data analysis, e.g., data from the next generation sequencing technology based studies. RESULTS: By extending the theories for the tail probability of the range of sum of Markovian random variables, we propose formulae for approximating the statistical significance of local trend scores. Using simulations and real data, we show that the approximate p-value is close to that obtained using a large number of permutations (starting at time points >20 with no delay and >30 with delay of at most three time steps) in that the non-zero decimals of the p-values obtained by the approximation and the permutations are mostly the same when the approximate p-value is less than 0.05. In addition, the approximate p-value is slightly larger than that based on permutations making hypothesis testing based on the approximate p-value conservative. The approximation enables efficient calculation of p-values for pairwise local trend analysis, making large scale all-versus-all comparisons possible. We also propose a hybrid approach by integrating the approximation and permutations to obtain accurate p-values for significantly associated pairs. We further demonstrate its use with the analysis of the Polymouth Marine Laboratory (PML) microbial community time series from high-throughput sequencing data and found interesting organism co-occurrence dynamic patterns. AVAILABILITY: The software tool is integrated into the eLSA software package that now provides accelerated local trend and similarity analysis pipelines for time series data. The package is freely available from the eLSA website: http://bitbucket.org/charade/elsa.

Efficient statistical significance approximation for local similarity analysis of high-throughput time series data.

MOTIVATION: Local similarity analysis of biological time series data helps elucidate the varying dynamics of biological systems. However, its applications to large scale high-throughput data are limited by slow permutation procedures for statistical significance evaluation. RESULTS: We developed a theoretical approach to approximate the statistical significance of local similarity analysis based on the approximate tail distribution of the maximum partial sum of independent identically distributed (i.i.d.) random variables. Simulations show that the derived formula approximates the tail distribution reasonably well (starting at time points > 10 with no delay and > 20 with delay) and provides P-values comparable with those from permutations. The new approach enables efficient calculation of statistical significance for pairwise local similarity analysis, making possible all-to-all local association studies otherwise prohibitive. As a demonstration, local similarity analysis of human microbiome time series shows that core operational taxonomic units (OTUs) are highly synergetic and some of the associations are body-site specific across samples. AVAILABILITY: The new approach is implemented in our eLSA package, which now provides pipelines for faster local similarity analysis of time series data. The tool is freely available from eLSA's website: http://meta.usc.edu/softs/lsa. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. CONTACT: fsun@usc.edu.

Regularized survival learning and cross-database analysis enabled identification of colorectal cancer prognosis-related immune genes.

Colon adenocarcinoma is the most common type of colorectal cancer. The prognosis of advanced colorectal cancer patients who received treatment is still very poor. Therefore, identifying new biomarkers for prognosis prediction has important significance for improving treatment strategies. However, the power of biomarker analyses was limited by the used sample size of individual database. In this study, we combined Genotype-Tissue Expression (GTEx) and The Cancer Genome Atlas (TCGA) databases to expand the number of healthy tissue samples. We screened differentially expressed genes between the GTEx healthy samples and TCGA tumor samples. Subsequently, we applied least absolute shrinkage and selection operator (LASSO) regression and multivariate Cox analysis to identify nine prognosis-related immune genes: ANGPTL4, IDO1, NOX1, CXCL3, LTB4R, IL1RL2, CD72, NOS2, and NUDT6. We computed the risk scores of samples based on the expression levels of these genes and divided patients into high- and low-risk groups according to this risk score. Survival analysis results showed a significant difference in survival rate between the two risk groups. The high-risk group had a significantly lower overall survival rate and poorer prognosis. We found the receiver operating characteristic based on the risk score was showed to accurately predict patients' prognosis. These prognosis-related immune genes may be potential biomarkers for colorectal cancer diagnosis and treatment. Our open-source code is freely available from GitHub at https://github.com/gutmicrobes/Prognosis-model.git.

Tumor tissue microorganisms are closely associated with tumor immune subtypes.

Studies have found that different immune subtypes are present in the same tumor. Different tumor subtypes have different tumor microenvironments (TME). This means that the efficacy of immunotherapy in actual applications will, therefore, have different results. Existing tumor immune subtype studies have mostly focused on immune cells, stromal cells, genes and molecules without considering the presence of microbes. Some studies have shown that microflora can strongly promote many gastrointestinal cancers. The microbiome has, therefore, become an important biomarker and regulatory factor of cancer progression and therapeutic responses. In addition, the presence of microflora can strongly regulate the host immune system, indirectly affecting tumor growth. Taken together, it is important to study the relationships that develop among tumor tissue microorganisms, tumor immune subtype, and the TME. In this study, correlations between microbial abundance, immune cell infiltration, immune gene expression and tumor immune subtype were studied. To accomplish this, tissue microorganisms and immune cell ratios with significant differences between the different cancers were obtained by comparing 203 gastric cancer and intestinal cancer samples. Two immune subtypes of intestinal samples were obtained by K-means clustering algorithm and tissue microorganisms, immune cell ratios and immune-related genes with significant differences between different immune subtypes were screened through Wilcoxon rank sum test. The results showed that Clostridioides difficile, Aspergillus fumigatus, Yarrowia lipolytica, and Fusarium pseudograminearum were all closely associated with the identified tumor immune subtypes. Our open-source software is freely available from GitHub at https://github.com/gutmicrobes/IMM-subtype.git.

Ksak: A high-throughput tool for alignment-free phylogenetics.

Phylogenetic tools are fundamental to the studies of evolutionary relationships. In this paper, we present Ksak, a novel high-throughput tool for alignment-free phylogenetic analysis. Ksak computes the pairwise distance matrix between molecular sequences, using seven widely accepted k-mer based distance measures. Based on the distance matrix, Ksak constructs the phylogenetic tree with standard algorithms. When benchmarked with a golden standard 16S rRNA dataset, Ksak was found to be the most accurate tool among all five tools compared and was 19% more accurate than ClustalW2, a high-accuracy multiple sequence aligner. Above all, Ksak was tens to hundreds of times faster than ClustalW2, which helps eliminate the computation limit currently encountered in large-scale multiple sequence alignment. Ksak is freely available at https://github.com/labxscut/ksak.

TIMELESS promotes the proliferation and migration of lung adenocarcinoma cells by activating EGFR through AMPK and SPHK1 regulation.

BACKGROUND: Lung adenocarcinoma (LUAD) has high morbidity and is prone to recurrence. TIMELESS (TIM), which regulates circadian rhythms in Drosophila, is highly expressed in various tumors. Its role in LUAD has gained attention, but the detailed function and mechanism have not been clarified completely at present. METHODS: Tumor samples from patients with LUAD patient data from public databases were used to confirm the relationship of TIM expression with lung cancer. LUAD cell lines were used and siRNA of TIM was adopted to knock down TIM expression in LUAD cells, and further cell proliferation, migration and colony formation were analyzed. By using Western blot and qPCR, we detected the influence of TIM on epidermal growth factor receptor (EGFR), sphingosine kinase 1 (SPHK1) and AMP-activated protein kinase (AMPK). With proteomics analysis, we comprehensively inspected the different changed proteins influenced by TIM and did global bioinformatic analysis. RESULTS: We found that TIM expression was elevated in LUAD and that this high expression was positively correlated with more advanced tumor pathological stages and shorter overall and disease-free survival. TIM knockdown inhibited EGFR activation and also AKT/mTOR phosphorylation. We also clarified that TIM regulated the activation of SPHK1 in LUAD cells. And with SPHK1 siRNA to knock down the expression level of SPHK1, we found that EGFR activation were inhibited greatly too. Quantitative proteomics techniques combined with bioinformatics analysis clarified the global molecular mechanisms regulated by TIM in LUAD. The results of proteomics suggested that mitochondrial translation elongation and termination were altered, which were closely related to the process of mitochondrial oxidative phosphorylation. We further confirmed that TIM knockdown reduced ATP content and promoted AMPK activation in LUAD cells. CONCLUSIONS: Our study revealed that siTIM could inhibit EGFR activation through activating AMPK and inhibiting SPHK1 expression, as well as influencing mitochondrial function and altering the ATP level; TIM's high expression in LUAD is an important factor and a potential key target in LUAD.

Predicting CRISPR/Cas9 Repair Outcomes by Attention-Based Deep Learning Framework.

As a simple and programmable nuclease-based genome editing tool, the CRISPR/Cas9 system has been widely used in target-gene repair and gene-expression regulation. The DNA mutation generated by CRISPR/Cas9-mediated double-strand breaks determines its biological and phenotypic effects. Experiments have demonstrated that CRISPR/Cas9-generated cellular-repair outcomes depend on local sequence features. Therefore, the repair outcomes after DNA break can be predicted by sequences near the cleavage sites. However, existing prediction methods rely on manually constructed features or insufficiently detailed prediction labels. They cannot satisfy clinical-level-prediction accuracy, which limit the performance of these models to existing knowledge about CRISPR/Cas9 editing. We predict 557 repair labels of DNA, covering the vast majority of Cas9-generated mutational outcomes, and build a deep learning model called Apindel, to predict CRISPR/Cas9 editing outcomes. Apindel, automatically, trains the sequence features of DNA with the GloVe model, introduces location information through Positional Encoding (PE), and embeds the trained-word vector matrixes into a deep learning model, containing BiLSTM and the Attention mechanism. Apindel has better performance and more detailed prediction categories than the most advanced DNA-mutation-predicting models. It, also, reveals that nucleotides at different positions relative to the cleavage sites have different influences on CRISPR/Cas9 editing outcomes.

CNN-XG: A Hybrid Framework for sgRNA On-Target Prediction.

As the third generation gene editing technology, Crispr/Cas9 has a wide range of applications. The success of Crispr depends on the editing of the target gene via a functional complex of sgRNA and Cas9 proteins. Therefore, highly specific and high on-target cleavage efficiency sgRNA can make this process more accurate and efficient. Although there are already many sophisticated machine learning or deep learning models to predict the on-target cleavage efficiency of sgRNA, prediction accuracy remains to be improved. XGBoost is good at classification as the ensemble model could overcome the deficiency of a single classifier to classify, and we would like to improve the prediction efficiency for sgRNA on-target activity by introducing XGBoost into the model. We present a novel machine learning framework which combines a convolutional neural network (CNN) and XGBoost to predict sgRNA on-target knockout efficacy. Our framework, called CNN-XG, is mainly composed of two parts: a feature extractor CNN is used to automatically extract features from sequences and predictor XGBoost is applied to predict features extracted after convolution. Experiments on commonly used datasets show that CNN-XG performed significantly better than other existing frameworks in the predicted classification mode.

An Entity Relation Extraction Method for Few-Shot Learning on the Food Health and Safety Domain.

In recent years, entity relation extraction has been a critical technique to help people analyze complex structured text data. However, there is no advanced research in food health and safety to help people analyze the complex concepts between food and human health and their relationships. This paper proposes an entity relation extraction method FHER for the few-shot learning in the food health and safety domain. For few-shot learning in the food health and safety domain, we propose three methods that effectively improve the performance of entity relationship extraction. The three methods are applied to the self-built data sets FH and MHD. The experimental results show that the method can effectively extract domain-related entities and their relations in a small sample size environment.