Search

Drug-drug interaction (DDI) prediction is one of the most important tasks in drug discovery. Prediction of potential DDIs helps to reduce unexpected side effects in the lifecycle of drugs, and is important for the drug safety surveillance. Here, we formulate the drug-drug interaction prediction as a matrix completion task, and project drugs in the interaction space into a low-dimensional space. We consider drug features, i.e., substructures, targets, enzymes, transporters, pathways, indications, side effects, and off side effects, to calculate drug-drug similarities, and assume them as manifolds in feature spaces. In this paper, we present a novel computational method named "Manifold Regularized Matrix Factorization" (MRMF) to predict potential drug-drug interactions, by introducing the drug feature-based manifold regularization into the matrix factorization. In the computational experiments, the MRMF models, which utilize known drug-drug interactions and the drug feature-based manifold, produce the area under precision-recall curves (AUPR) up to 0.7963. We test manifold regularizations based on different drug features, and the MRMF models can produce robust performances. Compared with other state-of-the-art methods, the MRMF models can produce better performances in the cross validation and case study. The manifold regularization is the critical factor for the high-accuracy performances of our method. MRMF is promising and effective for the prediction of drug-drug interactions.

A vast amount of vital clinical data is available within unstructured texts such as discharge summaries and procedure notes in Electronic Medical Records (EMRs). Automatically transforming such unstructured data into structured units is crucial for effective data analysis in the field of clinical informatics. Recognizing phrases that reveal important medical information in a concise and thorough manner is a fundamental step in this process. Existing systems that are built for opendomain texts are designed to detect mostly non-medical phrases, while tools designed specifically for extracting concepts from clinical texts are not scalable to large corpora and often leave out essential context surrounding those detected clinical concepts. We address these issues by proposing a framework, CliniPhrase, which adapts domain-specific deep neural network based language models (such as ClinicalBERT) to effectively and efficiently extract high-quality phrases from clinical documents with a limited amount of training data. Experimental results on the MIMIC-III dataset show that our method can outperform the current state-of-the-art techniques by up to 18% in terms of F 1 measure while being very efficient (up to 48 times faster).11Our source code, pre-trained models and documentations are available online at: https://github.com/kaushikmani/PhraseMiningLM

De-identification is the task of identifying protected health information (PHI) in the clinical text. Existing neural de-identification models often fail to generalize to a new dataset. We propose a simple yet effective data augmentation method PHICON to alleviate the generalization issue. PHICON consists of PHI augmentation and Context augmentation, which creates augmented training corpora by replacing PHI entities with named-entities sampled from external sources, and by changing background context with synonym replacement or random word insertion, respectively. Experimental results on the i2b2 2006 and 2014 de-identification challenge datasets show that PHICON can help three selected de-identification models boost F1-score (by at most 8.6%) on cross-dataset test setting. We also discuss how much augmentation to use and how each augmentation method influences the performance., Accepted by The 3rd ClinicalNLP Workshop at EMNLP'20

Background Globally, although the jellyfish population has increased in recent years, ocular jellyfish stings remain an uncommon ophthalmic emergency, and have been rarely reported. According to a few previous reports, ocular jellyfish stings may cause anterior segment disorders, and most of these injuries were self-limited and spontaneously resolved within 24 to 48 h. Case summary A brother and sister both presented with severe fundus complications several years after ocular jellyfish stings and both had prolonged blurred vision. To our knowledge, such fundus lesions induced by jellyfish stings have not been reported previously. Conclusion The fundus status of patients following ocular jellyfish stings should be carefully monitored in cases of irreversible ocular damage.

MicroRNAs (miRNAs) play crucial roles in multifarious biological processes associated with human diseases. Identifying potential miRNA-disease associations contributes to understanding the molecular mechanisms of miRNA-related diseases. Most of the existing computational methods mainly focus on predicting whether a miRNA-disease association exists or not. However, the roles of miRNAs in diseases are prominently diverged, for instance, Genetic variants of miRNA (mir-15) may affect the expression level of miRNAs leading to B cell chronic lymphocytic leukemia, while circulating miRNAs (including mir-1246, mir-1307-3p, etc.) have potentials to detecting breast cancer in the early stage. In this paper, we aim to predict multi-type miRNA-disease associations instead of taking them as binary. To this end, we innovatively represent miRNA-disease-type triples as a tensor and introduce tensor decomposition methods to solve the prediction task. Experimental results on two widely-adopted miRNA-disease datasets: HMDD v2.0 and HMDD v3.2 show that tensor decomposition methods improve a recent baseline in a large scale (up to $38\%$ in Top-1F1). We then propose a novel method, Tensor Decomposition with Relational Constraints (TDRC), which incorporates biological features as relational constraints to further the existing tensor decomposition methods. Compared with two existing tensor decomposition methods, TDRC can produce better performance while being more efficient.

The objective of this study was to investigate the hypoglycemic and hypolipidemic effects of phlorizin on sweet tea in rats with diabetes. Diabetic rat model was established by feeding with HFD (high‐fat diet) and then treating with intraperitoneal injection of STZ (streptozocin). The experiments were divided into therapeutic and preventive experiments. In both experiments, rats were divided into normal, diabetic control, positive control, and phlorizin groups. Symptoms of diabetes, fasting blood glucose (FBG) levels, serum lipid parameters, and pathological changes in the pancreas and liver were evaluated. It was found that the symptoms of diabetes were improved by phlorizin treatment. In addition, phlorizin could decrease FBG, improve serum lipid levels, protect against damaged pancreas islet, and decrease fat deposition in hepatic cells. These effects of phlorizin can be shown only attain to a certain dosage. It can be concluded that phlorizin has the therapeutic and preventive effects on hyperlipidemia and hyperglycemia in diabetes rats., This study has confirmed that phlorizin from sweet tea has the therapeutic and preventive effects on hyperlipidemia and hyperglycemia in diabetes rats, and phlorizin can be used as a natural health drink to prevent diabetes.

Machine reading comprehension has made great progress in recent years owing to large-scale annotated datasets. In the clinical domain, however, creating such datasets is quite difficult due to the domain expertise required for annotation. Recently, Pampari et al. (EMNLP'18) tackled this issue by using expert-annotated question templates and existing i2b2 annotations to create emrQA, the first large-scale dataset for question answering (QA) based on clinical notes. In this paper, we provide an in-depth analysis of this dataset and the clinical reading comprehension (CliniRC) task. From our qualitative analysis, we find that (i) emrQA answers are often incomplete, and (ii) emrQA questions are often answerable without using domain knowledge. From our quantitative experiments, surprising results include that (iii) using a small sampled subset (5%-20%), we can obtain roughly equal performance compared to the model trained on the entire dataset, (iv) this performance is close to human expert's performance, and (v) BERT models do not beat the best performing base model. Following our analysis of the emrQA, we further explore two desired aspects of CliniRC systems: the ability to utilize clinical domain knowledge and to generalize to unseen questions and contexts. We argue that both should be considered when creating future datasets., Accepted by ACL 2020

BACKGROUND: Cutaneous epithelioid angiomatous nodules (CEAN) are rare, benign, vascular lesions characterized by benign proliferation of endothelial cells with prominent epithelioid features, which can be easily confused with benign and malignant vascular tumors. However, the etiology of CEAN remains unclear, and no association with infection, trauma, or immunosuppression has been described. This case study indicated that CEAN is closely related to the patient’s impaired immune status and may be induced by cyclosporine. CASE SUMMARY: A 19-year-old boy with nephrotic syndrome (NS) developed large CEAN on the left foot during treatment for NS. He had repeated relapses of edema in the past 6 years and different types of immunosuppressants were administered including methylprednisolone, mycophenolate mofetil, tacrolimus and cyclosporine; the dosages of these drugs were frequently adjusted. The patient had been receiving cyclosporine and methylprednisolone for 7 mo before he developed CEAN. Cyclosporine was discontinued due to its side effects on skin. After cessation of cyclosporine and 16 mo follow-up, the nodules gradually disappeared without any other treatment for the CEAN. CONCLUSION: Impaired immune status is proposed to be a risk factor for CEAN, which may be induced by cyclosporine.

Drug-disease associations provide important information for drug discovery and drug repositioning. Drug-disease associations can induce different effects, and the therapeutic effect attracts wide spread interest. Therefore, developing drug-disease association prediction methods is an important task, and differentiating therapeutic associations from other associations is also very important. In this paper, we formulate the known drug-disease associations as a bipartite network, and then present a novel representation for drugs and diseases based on the bipartite network and linear neighborhood similarity. Thus, we propose the network topological similarity-based inference method (NTSIM) to predict unobserved drug-disease associations. Further, we extend the work to the association classification, and propose the network topological similarity-based classification method (NTSIM-C) to differentiate therapeutic associations from others. Compared with existing drug-disease association prediction methods, NTSIM can produce superior performances in predicting drug-disease associations, and NTSIM-C can accurately classify drug-disease associations. Further, we analyze the capability of proposed methods by using several case studies. The studies show the usefulness of NTSIM and NTSIM-C in the real applications. In conclusion, NTSIM and NTSIM-C are promising for predicting drug-disease associations and their therapeutic functions.

AIM To explore the features and prognostic value of lymph node metastasis in patients with T1-stage colorectal cancer (CRC). METHODS In all, 321 cases of T1-stage CRC were selected from 10132 patients with CRC who received surgical therapy in six large-scale hospitals in China and were retrospectively analyzed. Univariate and multivariate analyses were performed to analyze the risk factors for lymphatic metastasis. A survival analysis was then performed to analyze the prognostic value of lymph node metastasis. RESULTS The occurrence rate of T1 stage was 3.17% (321/10132); of these patients, the lymph node metastasis rate was 8.41% (27/321), and the non-lymph node metastasis rate was 91.59% (294/321). Univariate analysis showed that preoperative serum CEA, preoperative serum CA199, preoperative serum CA724, vascular invasion, and degree of differentiation were associated with lymph node metastasis in T1-stage CRC (P < 0.05 for all). Multivariate analysis indicated that preoperative serum CA724, vascular invasion, and degree of differentiation were closely related to lymph node metastasis (P < 0.05 for all). Log-rank survival analysis showed that age, preoperative serum CEA, preoperative serum CA199, vascular invasion, degree of differentiation, and lymph node metastasis (χ2 = 24.180, P < 0.001) were predictors of 5-year overall survival (OS) (P < 0.05 for all). COX regression analysis demonstrated that preoperative serum CA199 and lymph node metastasis (HR = 5.117; P < 0.05; 95%CI: 0.058-0.815) were independent prognostic indicators of 5-year OS in patients with T1-stage CRC (P < 0.05 for both). CONCLUSION The morbidity of T1-stage CRC was 3.17% for all CRC cases. Preoperative serum CA724, vascular invasion, and degree of differentiation are independent risk factors for lymph node metastasis. Lymph node metastasis is an independent prognostic factor for OS in patients with T1-stage CRC.

Background Drug side effects are one of main concerns in the drug discovery, which gains wide attentions. Investigating drug side effects is of great importance, and the computational prediction can help to guide wet experiments. As far as we known, a great number of computational methods have been proposed for the side effect predictions. The assumption that similar drugs may induce same side effects is usually employed for modeling, and how to calculate the drug-drug similarity is critical in the side effect predictions. Results In this paper, we present a novel measure of drug-drug similarity named “linear neighborhood similarity”, which is calculated in a drug feature space by exploring linear neighborhood relationship. Then, we transfer the similarity from the feature space into the side effect space, and predict drug side effects by propagating known side effect information through a similarity-based graph. Under a unified frame based on the linear neighborhood similarity, we propose method “LNSM” and its extension “LNSM-SMI” to predict side effects of new drugs, and propose the method “LNSM-MSE” to predict unobserved side effect of approved drugs. Conclusions We evaluate the performances of LNSM and LNSM-SMI in predicting side effects of new drugs, and evaluate the performances of LNSM-MSE in predicting missing side effects of approved drugs. The results demonstrate that the linear neighborhood similarity can improve the performances of side effect prediction, and the linear neighborhood similarity-based methods can outperform existing side effect prediction methods. More importantly, the proposed methods can predict side effects of new drugs as well as unobserved side effects of approved drugs under a unified frame.

Bacterial strains 4M-Z03T, 4M-K16T and DHG59T were isolated from forest soil samples collected from the Dinghushan Biosphere Reserve, Guangdong Province, PR China (112° 31′ E 23° 10′ N). The three strains grew well at 28 °C, pH 5.0–6.0 on R2A medium. On the basis of 16S rRNA gene sequence analysis, the three strains, together with Dyella humi DHG40T, formed a distinct phyletic clade within the genus Dyella , and the sequence similarities between any strains of the clade ranged from 97.8 to 98.5 %. Sequence analysis of concatenated partial gyr B, lep A and rec A gene sequences also strongly suggested that the three strains represented three novel species of the genus Dyella . The respiratory lipoquinone of the three strains was ubiquinone-8, and their DNA G+C content was 58.2–59.0 mol%. The fatty acid profiles differed substantially among these three strains, although they had two common major fatty acids, iso-C15 : 0 and iso-C17 : 1ω9c. The DNA–DNA relatedness among the three strains and the type strains of the closest species of the genus Dyella examined was lower than 50 %. The results of genotypic and phenotypic characterization presented above demonstrate that the three strains examined represent three novel species of the genus Dyella , for which the names Dyella acidisoli sp. nov. (type strain 4M-Z03T=NBRC 111980T=KCTC 52131T), Dyella flagellata sp. nov. (type strain 4M-K16T=NBRC 111981T=KCTC 52130T) and Dyella nitratireducens sp. nov. (type strain DHG59T=NBRC 111472T=LMG 29201T=CGMCC 1.15439T) are proposed.

Weak gradient magnetic field detection is significant. This paper presents a novel micro-fabricated spin-exchange-relaxation-free (SERF) atomic magnetometer for weak gradient magnetic field detection, and the atomic vapor cell is fabricated by micro electro mechanical systems (MEMS) technology. Compared to the single-channel magnetometer, the novel magnetometer's signal-to-noise ratio has increased by 3.63 times. The magnetic field gradient noise of the novel magnetometer reaches 45.33 fToHz-1/2ocm-1 in the frequency range of 8 to 15 Hz, and the novel magnetometer has a CMRR (common mode rejection ratio) of 6.1. At the same time, the relationship between the magnitude of the magnetic field gradient and the peak of the dispersive signal has been verified, which provides an idea for the detection of weak gradient magnetic field.

Background In the field of drug repositioning, it is assumed that similar drugs may treat similar diseases, therefore many existing computational methods need to compute the similarities of drugs and diseases. However, the calculation of similarity depends on the adopted measure and the available features, which may lead that the similarity scores vary dramatically from one to another, and it will not work when facing the incomplete data. Besides, supervised learning based methods usually need both positive and negative samples to train the prediction models, whereas in drug-disease pairs data there are only some verified interactions (positive samples) and a lot of unlabeled pairs. To train the models, many methods simply treat the unlabeled samples as negative ones, which may introduce artificial noises. Herein, we propose a method to predict drug-disease associations without the need of similarity information, and select more likely negative samples. Results In the proposed EMP-SVD (Ensemble Meta Paths and Singular Value Decomposition), we introduce five meta paths corresponding to different kinds of interaction data, and for each meta path we generate a commuting matrix. Every matrix is factorized into two low rank matrices by SVD which are used for the latent features of drugs and diseases respectively. The features are combined to represent drug-disease pairs. We build a base classifier via Random Forest for each meta path and five base classifiers are combined as the final ensemble classifier. In order to train out a more reliable prediction model, we select more likely negative ones from unlabeled samples under the assumption that non-associated drug and disease pair have no common interacted proteins. The experiments have shown that the proposed EMP-SVD method outperforms several state-of-the-art approaches. Case studies by literature investigation have found that the proposed EMP-SVD can mine out many drug-disease associations, which implies the practicality of EMP-SVD. Conclusions The proposed EMP-SVD can integrate the interaction data among drugs, proteins and diseases, and predict the drug-disease associations without the need of similarity information. At the same time, the strategy of selecting more reliable negative samples will benefit the prediction.

Graph embedding learning that aims to automatically learn low-dimensional node representations, has drawn increasing attention in recent years. To date, most recent graph embedding methods are evaluated on social and information networks and are not comprehensively studied on biomedical networks under systematic experiments and analyses. On the other hand, for a variety of biomedical network analysis tasks, traditional techniques such as matrix factorization (which can be seen as a type of graph embedding methods) have shown promising results, and hence there is a need to systematically evaluate the more recent graph embedding methods (e.g. random walk-based and neural network-based) in terms of their usability and potential to further the state-of-the-art. We select 11 representative graph embedding methods and conduct a systematic comparison on 3 important biomedical link prediction tasks: drug-disease association (DDA) prediction, drug-drug interaction (DDI) prediction, protein-protein interaction (PPI) prediction; and 2 node classification tasks: medical term semantic type classification, protein function prediction. Our experimental results demonstrate that the recent graph embedding methods achieve promising results and deserve more attention in the future biomedical graph analysis. Compared with three state-of-the-art methods for DDAs, DDIs and protein function predictions, the recent graph embedding methods achieve competitive performance without using any biological features and the learned embeddings can be treated as complementary representations for the biological features. By summarizing the experimental results, we provide general guidelines for properly selecting graph embedding methods and setting their hyper-parameters for different biomedical tasks., Comment: Published in Bioinformatics

To explore the image quality (IQ) and diagnostic value of 70 kVp turbo high-pitch coronary CT angiography (THP-CCTA) using automated tube voltage selection (ATVS) and 30 mL of low-concentration contrast agent. Patients who underwent 70 kVp THP-CCTA using ATVS with 30 mL of contrast agent (group A) were prospectively enrolled, and those who underwent conventional CCTA (100/120 kVp, prospective sequential mode with 65–75 mL of contrast agent) (group B) were retrospectively selected for study. IQ was assessed subjectively on a 5-point scale, and diagnostic value was assessed based on invasive coronary angiography as the gold standard. Heart rate (HR), HR fluctuation (HRF), body mass index (BMI), effective radiation dose (ED), and iodine uptake (IU) were recorded. A total of 796 patients (398/398 in groups A/B) were included. Between-group differences in age, gender, BMI, HR, HRF, and IQ values were not significant. The ED/IU values were 0.3 ± 0.1 mSv/9.0 ± 0.0 g and 5.8 ± 1.8 mSv/22.9 ± 1.0 g in groups A and B, respectively (p

Predicting drug-disease associations using computational methods benefits drug repositioning. Drug-disease associations are events that drugs exert effects on diseases, there are different effects about drug-disease associations. For example, drug-disease associations are annotated as therapeutic or marker/mechanism (non-therapeutic) in Comparative Toxicogenomics database (CTD). However, existing association prediction methods ignore effects that drugs exert on diseases. In this paper, we propose a signed network-based nonnegative matrix factorization method (SNNMF) to predict drug-disease associations and their effects. First, drug-disease associations are represented as a signed bipartite network with two types of links for therapeutic effects and non-therapeutic effects. After decomposing the network into two subnetworks, SNNMF aims to approximate the association matrix of each subnetwork by two nonnegative matrices, which are low-dimensional latent representations for drugs and diseases respectively, and diseases in two subnetworks share the same latent representations. In the computational experiments, SNNMF performs well in predicting effects of drug-disease associations. Moreover, SNNMF accurately predicts drug-disease associations and outperforms existing association prediction methods. Case studies show that SNNMF helps to find out novel drug-disease associations that are not included in CTD, and simultaneously predicts their therapeutic effects.

LncRNA-protein interactions play important roles in post-transcriptional gene regulation, poly-adenylation, splicing and translation. Identification of lncRNA-protein interactions helps to understand lncRNA-related activities. Existing computational methods utilize multiple lncRNA features or multiple protein features to predict lncRNA-protein interactions, but features are not available for all lncRNAs or proteins; most of existing methods are not capable of predicting interacting proteins (or lncRNAs) for new lncRNAs (or proteins), which don’t have known interactions. In this paper, we propose the sequence-based feature projection ensemble learning method, “SFPEL-LPI”, to predict lncRNA-protein interactions. First, SFPEL-LPI extracts lncRNA sequence-based features and protein sequence-based features. Second, SFPEL-LPI calculates multiple lncRNA-lncRNA similarities and protein-protein similarities by using lncRNA sequences, protein sequences and known lncRNA-protein interactions. Then, SFPEL-LPI combines multiple similarities and multiple features with a feature projection ensemble learning frame. In computational experiments, SFPEL-LPI accurately predicts lncRNA-protein associations and outperforms other state-of-the-art methods. More importantly, SFPEL-LPI can be applied to new lncRNAs (or proteins). The case studies demonstrate that our method can find out novel lncRNA-protein interactions, which are confirmed by literature. Finally, we construct a user-friendly web server, available at http://www.bioinfotech.cn/SFPEL-LPI/., Author summary LncRNA-protein interactions play important roles in post-transcriptional gene regulation, poly-adenylation, splicing and translation. Identification of lncRNA-protein interactions helps to understand lncRNA-related activities. In this paper, we propose a novel computational method “SFPEL-LPI” to predict lncRNA-protein interactions. SFPEL-LPI makes use of lncRNA sequences, protein sequences and known lncRNA-protein associations to extract features and calculate similarities for lncRNAs and proteins, and then combines them with a feature projection ensemble learning frame. SFPEL-LPI can predict unobserved interactions between lncRNAs and proteins, and also can make predictions for new lncRNAs (or proteins), which have no interactions with any proteins (or lncRNAs). SFPEL-LPI produces high-accuracy performances on the benchmark dataset when evaluated by five-fold cross validation, and outperforms state-of-the-art methods. The case studies demonstrate that SFPEL-LPI can find out novel associations, which are confirmed by literature. To facilitate the lncRNA-protein interaction prediction, we develop a user-friendly web server, available at http://www.bioinfotech.cn/SFPEL-LPI/.

Background Bacterial small non-coding RNAs (sRNAs) have emerged as important elements in diverse physiological processes, including growth, development, cell proliferation, differentiation, metabolic reactions and carbon metabolism, and attract great attention. Accurate prediction of sRNAs is important and challenging, and helps to explore functions and mechanism of sRNAs. Results In this paper, we utilize a variety of sRNA sequence-derived features to develop ensemble learning methods for the sRNA prediction. First, we compile a balanced dataset and four imbalanced datasets. Then, we investigate various sRNA sequence-derived features, such as spectrum profile, mismatch profile, reverse compliment k-mer and pseudo nucleotide composition. Finally, we consider two ensemble learning strategies to integrate all features for building ensemble learning models for the sRNA prediction. One is the weighted average ensemble method (WAEM), which uses the linear weighted sum of outputs from the individual feature-based predictors to predict sRNAs. The other is the neural network ensemble method (NNEM), which trains a deep neural network by combining diverse features. In the computational experiments, we evaluate our methods on these five datasets by using 5-fold cross validation. WAEM and NNEM can produce better results than existing state-of-the-art sRNA prediction methods. Conclusions WAEM and NNEM have great potential for the sRNA prediction, and are helpful for understanding the biological mechanism of bacteria.

Drug-disease associations provide important information for the drug discovery. Wet experiments that identify drug-disease associations are time-consuming and expensive. However, many drug-disease associations are still unobserved or unknown. The development of computational methods for predicting unobserved drug-disease associations is an important and urgent task. In this paper, we proposed a similarity constrained matrix factorization method for the drug-disease association prediction (SCMFDD), which makes use of known drug-disease associations, drug features and disease semantic information. SCMFDD projects the drug-disease association relationship into two low-rank spaces, which uncover latent features for drugs and diseases, and then introduces drug feature-based similarities and disease semantic similarity as constraints for drugs and diseases in low-rank spaces. Different from the classic matrix factorization technique, SCMFDD takes the biological context of the problem into account. In computational experiments, the proposed method can produce high-accuracy performances on benchmark datasets, and outperform existing state-of-the-art prediction methods when evaluated by five-fold cross validation and independent testing. We developed a user-friendly web server by using known associations collected from the CTD database, available at http://www.bioinfotech.cn/SCMFDD/ . The case studies show that the server can find out novel associations, which are not included in the CTD database.

Recent studies show that drug-disease associations provide important information for drug discovery and drug repositioning. Wet experimental identification of drug-disease associations is time-consuming and labor-intensive. Therefore, the development of computational methods that predict drug-disease associations is an urgent task. In this paper, we propose a novel computational method named NTSIM, which only uses known drug-disease associations to predict unobserved associations. First of all, known drug-disease associations are represented as a drug-disease bipartite network, and a novel similarity measure named linear neighborhood similarity (LNS) is proposed to calculate drug-drug similarity and disease-disease similarity based on the bipartite network. Then, we predict unobserved drug-disease associations in the similarity-based graph by using label propagation process. In the computational experiments, this proposed method achieves high-accuracy performances, and outperforms representative state-of-the-art methods: PREDICT, TL-HGBI and LRSSL. Our studies reveal that known drug-disease associations can provide enough information to build the high-accuracy prediction models; linear neighbor similarity (LNS) can lead to better performances than other similarity measures such as Jaccard similarity, Gauss similarity and cosine similarity; the bipartite network-derived features outperform the drug biological features and disease semantic features.

Bacterial small non-coding RNAs (sRNAs) play important roles in various physiological processes, and predicting sRNAs is an important task. In this paper, we develop a computational method for the sRNA prediction by using sRNA sequence-derived features. We investigate a variety of sRNA sequence-derived features, and evaluate the usefulness of features for the sRNA prediction. Then, we develop the sequence learning ensemble method, which uses the linear weighted sum of outputs from the individual feature-based predictors to predict sRNAs, and the genetic algorithm is adopted to optimize the parameters in the ensemble system. In the computational experiments, we compile a balanced dataset and four imbalanced datasets, and evaluate our method on these datasets by using 5-fold cross validation. The sequence learning ensemble method can achieve AUC scores greater than 0.9, and outperforms existing state-of-the-art sRNA prediction methods. In conclusion, the proposed method has a great potential for sRNA prediction. The source codes, datasets and supplementary are available in http://www.bioinfotech.cn/BIBM2017/SLEM.

Studies reported that miR-590-3p was involved in human cancer progression. However, its roles of oncogene or anti-oncogene in malignancies still remain elusive. This study was aimed to investigate the effect of miR-590-3p on the cell proliferation and metastasis via Hippo pathway in colorectal cancer (CRC). In our study, miR-590-3p was demonstrated highly expressed in CRC tissues, compared with adjacent normal tissues (P

A Gram-stain-negative, aerobic, motile, rod-shaped bacterium, designated 7M-Z19T, was isolated from a soil sample collected from a Pinus massoniana forest of Dinghushan Biosphere Reserve, Guangdong Province, PR China. Strain 7M-Z19T grew at pH 4.5–7.5 (optimum pH 6.0–6.5), 10 to 37 °C (optimum 28 °C) and NaCl concentration up to 2.0 % (optimum 0 %, w/v). iso-C17 : 0, C18 : 1 ω7c and C19 : 0 ω8c cyclo were the major fatty acids (>10 %) while ubiquinone-10 was the only respiratory quinone detected in 7M-Z19T. The polar lipids of the strain consisted of phosphatidylethanolamine, phosphatidyldimethylethanolamine, diphosphatidylglycerol, phosphatidylglycerol, six unidentified aminophospholipids, three unidentified phospholipids, six unidentified lipids and a glycolipid. The DNA G+C content was 65.8 mol%. Phylogenetic analysis based on 16S rRNA gene sequences revealed that the isolate formed a distinct lineage with Dongia mobilis and Dongia rigui within the family Rhodospirillaceae , but with a low sequence similarity of 92.7 and 92.0 %, respectively. On the basis of phylogenetic, phenotypic, physiological and chemotaxonomic distinctiveness, strain 7M-Z19T should be placed in the family Rhodospirillaceae as a representative of a novel genus and species, for which the name Aliidongia dinghuensis gen. nov., sp. nov., is proposed. The type strain of the type species is 7M-Z19T (=NBRC 112240T=KCTC 52134T=CGMCC 1.15725T).