Trained Words Research Articles

Deep learning improves the ability of sgRNA off-target propensity prediction

BackgroundCRISPR/Cas9 system, as the third-generation genome editing technology, has been widely applied in target gene repair and gene expression regulation. Selection of appropriate sgRNA can improve the on-target knockout efficacy of CRISPR/Cas9 system with high sensitivity and specificity. However, when CRISPR/Cas9 system is operating, unexpected cleavage may occur at some sites, known as off-target. Presently, a number of prediction methods have been developed to predict the off-target propensity of sgRNA at specific DNA fragments. Most of them use artificial feature extraction operations and machine learning techniques to obtain off-target scores. With the rapid expansion of off-target data and the rapid development of deep learning theory, the existing prediction methods can no longer satisfy the prediction accuracy at the clinical level.ResultsHere, we propose a prediction method named CnnCrispr to predict the off-target propensity of sgRNA at specific DNA fragments. CnnCrispr automatically trains the sequence features of sgRNA-DNA pairs with GloVe model, and embeds the trained word vector matrix into the deep learning model including biLSTM and CNN with five hidden layers. We conducted performance verification on the data set provided by DeepCrispr, and found that the auROC and auPRC in the “leave-one-sgRNA-out” cross validation could reach 0.957 and 0.429 respectively (the Pearson value and spearman value could reach 0.495 and 0.151 respectively under the same settings).ConclusionOur results show that CnnCrispr has better classification and regression performance than the existing states-of-art models. The code for CnnCrispr can be freely downloaded from https://github.com/LQYoLH/CnnCrispr.

Evaluation of a digital game-based reading training for German children with reading disorder

Technological tools have the potential to efficiently support learning performance in children and are therefore recognized as being beneficial for children with special needs, such as reading disorders. The present study investigated the effectivity of a novel digital game-based reading training in second- and third-grade children with reading disorder (N = 50) carried out in home environment over a short training period of eight to eleven weeks. Children were randomly assigned to either a training group (N = 25) receiving the digital game-based reading training or a control group performing digitalized mind games (N = 25). Results showed significant improvement of reading performance for trained word material and a trend for transfer effects to untrained words. The digital game-based reading training was also found to be highly motivating and feasible for use in home environment. This study extends existing knowledge about digital game-based reading support and underlines that using a multi-component reading approach combined with flow principles is beneficial for children with reading disorder. Furthermore, the evaluated program seems to be a promising addition for children who do not have access to individual reading support.

Sistem deteksi ketepatan pembacaan surah al-Kautsar berbasis kata menggunakan mel frequency cepstrum coefficient dan cosine similarity

In this study, a system has been developed to help detect the accuracy of the reading of the Koran in the Surah Al-Kautsar based on the accuracy of the number and pronunciation of words in one complete surah. This system is very dependent on the accuracy of word segmentation based on envelope signals. The feature extraction method used was Mel Frequency Cepstrum Coefficients (MFCC), while the Cosine Similarity method was used to detect the accuracy of the reading. From 60 data, 30 data were used for training, while the rest were for testing. From each of the 30 training and test data, 15 data were correct readings, and 15 other data were incorrect readings. System accuracy was measured by word-for-word recognition, which results in 100 % of recall and 98.96 % of precision for the training word data, and 100 % of recall and 99.65 % of precision for the test word data. For the overall reading of the surah, there were 15 correct readings and 14 incorrect readings that were recognized correctly.

Disentangling the far transfer of language comprehension gains using latent mediation models.

While we know that interventions targeting oral language can be effective, little is known about what drives these effects. In this study, we examine whether gains in transfer measures are mediated through the specific words that are trained in a language intervention. Based on a large-scale randomized controlled trial of language intervention in 4- to 5-year-old children, latent mediation models were used to disentangle oral language gains in transfer measures. The results first showed that the effects of the language intervention and the transfer effects are generated through expressive rather than receptive measures of language. Second, we found that the effects of the intervention on intermediate transfer measures of language were mediated through the ability to define the trained words. Third, and critically, for far transfer measures that did not contain any of the trained words, the effects were mediated through the trained words. The findings relate to theories of transfer and support the idea that far transfer is possible, at least within the same domain. In addition, it seems that effects on receptive language skills are difficult to obtain and that what is improved is instead the children's ability to express themselves and use procedures to explain words. Thus, to optimize intervention effects, future studies should focus on expressive language.

Neural bases of learning and recognition of statistical regularities.

Statistical learning is a set of cognitive mechanisms allowing for extracting regularities from the environment and segmenting continuous sensory input into discrete units. The current study used functional magnetic resonance imaging (fMRI) (N=25) in conjunction with an artificial language learning paradigm to provide new insight into the neural mechanisms of statistical learning, considering both the online process of extracting statistical regularities and the subsequent offline recognition of learned patterns. Notably, prior fMRI studies on statistical learning have not contrasted neural activation during the learning and recognition experimental phases. Here, we found that learning is supported by the superior temporal gyrus and the anterior cingulate gyrus, while subsequent recognition relied on the left inferior frontal gyrus. Besides, prior studies only assessed the brain response during the recognition of trained words relative to novel nonwords. Hence, a further key goal of this study was to understand how the brain supports recognition of discrete constituents from the continuous input versus recognition of mere statistical structure that is used to build new constituents that are statistically congruent with the ones from the input. Behaviorally, recognition performance indicated that statistically congruent novel tokens were less likely to be endorsed as parts of the familiar environment than discrete constituents. fMRI data showed that the left intraparietal sulcus and angular gyrus support the recognition of old discrete constituents relative to novel statistically congruent items, likely reflecting an additional contribution from memory representations for trained items.

Brain Dynamics Reflects Phonological And Semantic Top-Down Influences During Orthographic Processing Of Novel Word Forms

The acquisition of new vocabulary is usually mediated by previous experience with language. In visual domain, orthographically unfamiliar word-forms may already have corresponding phonological or even conceptual representations in linguistic system, which facilitates orthographic learning. The neural correlates of this advantage were investigated by recording EEG during reading novel and familiar words across three different experiments (n=22 each), manipulating the availability of previous knowledge for the novel written words. In Experiment 1, participants received no previous training before reading these stimuli; Experiments 2 and 3, however, provided a previous training (six exposures) in the phonology of novel words (auditory exposure) and in both phonology and meaning (auditory exposure + picture), respectively. During reading, a different pattern of ERP responses was found for the novel written words depending on their previous training, resembling cross-level top-down interactive effects during the process of vocabulary acquisition. Thus, whereas phonological experience produced a different modulation at early lexical (~180 ms) and post lexical (~520 ms) stages of visual recognition of novel written words, additional semantic training influenced their lexical processing at a later stage (~320 ms). Importantly, a clear lexicality effect was found at the early peak in experiment 1 (no previous training); however, neural responses for trained and familiar words were found indistinguishable at this latency regardless of the phonological or semantic nature of training, reflecting similar orthographic processing and word-form access. These results suggest the key role of phonology in orthographically transparent reading systems, in which bidirectional decoding mechanisms contribute to the rapid formation of novel word-form representations even in the absence of visual exposure.

Biomedical named entity recognition based on Glove-BLSTM-CRF model

Named entity recognition is a fundamental task of natural language processing. The number of biomedical named entities is huge, the naming rules are not uniform, and the entity word formation is complex, which brings great difficulties to the biomedical named entity recognition. Traditional machine learning algorithms rely heavily on manual extraction of features. The quality of feature extraction directly affects the accuracy of entity recognition. In the biomedical domain, the cost of manually extracting features and annotating data sets is enormous. In recent years, deep learning methods that do not rely on artificial features have made great progress in many domains. This paper proposes a model based on Glove-BLSTM-CRF for biomedical named entity recognition. Firstly, the Glove model is used to train word vector with semantic features, and BLSTM is used to train word vector with character morphological features. The two are combined as the final representation of the word, then input into the BLSTM-CRF deep learning model to recognize the entity categories. The experimental results show that the model has achieved a better result in the JNLPBA 2004 biomedical named entity recognition task without relying on any artificial features and rules, and the F1 value reaches 75.62%.

Punctuation and Parallel Corpus Based Word Embedding Model for Low-Resource Languages

To overcome the data sparseness in word embedding trained in low-resource languages, we propose a punctuation and parallel corpus based word embedding model. In particular, we generate the global word-pair co-occurrence matrix with the punctuation-based distance attenuation function, and integrate it with the intermediate word vectors generated from the small-scale bilingual parallel corpus to train word embedding. Experimental results show that compared with several widely used baseline models such as GloVe and Word2vec, our model improves the performance of word embedding for low-resource language significantly. Trained on the restricted-scale English-Chinese corpus, our model has improved by 0.71 percentage points in the word analogy task, and achieved the best results in all of the word similarity tasks.

Spelling Checker of Words in Rejang Language Using the N-Gram and Euclidean Distance Methods

Spelling mistakes of words in writing Rejang words are often found so it is difficult to understand. The method used in correcting word errors (spelling checkers) has been carried out by several researchers. In this research, words were improved in Rejang words based on the morphology of the Reajang language using the N-gram and Euclidean Distance methods. The process begins with forming the word practice with the N-gram method in cutting a number of words. In the testing process, the pre-process stages are carried out first and the training words are checked based on the existing dictionary. Words that are assumed to be wrong are corrected by looking for words similar to Euclidean Distance. The results of the lowest word resemblance are adjusted to the word training, if it is not appropriate then the word with the highest similarity is considered the correct word to be improved. In this study the experimental results of the words tested produce similarity levels of 20 words and the smallest 3. The results from the calculation of similarity can be directly to correct the wrong word. The results of the study can be seen that word improvement is very dependent on the dictionary word unigram and existing training words. This shows that the N-gram and Euclidean Distance methods are good in spelling checker Rejang language.

WEDeepT3: predicting type III secreted effectors based on word embedding and deep learning

BackgroundThe type III secreted effectors (T3SEs) are one of the indispensable proteins in the growth and reproduction of Gram‐negative bacteria. In particular, the pathogenesis of Gram‐negative bacteria depends on the type III secreted effectors, and by injecting T3SEs into a host cell, the host cell’s immunity can be destroyed. The high diversity of T3SE sequences and the lack of defined secretion signals make it difficult to identify and predict. Moreover, the related study of the pathological system associated with T3SE remains a hot topic in bioinformatics. Some computational tools have been developed to meet the growing demand for the recognition of T3SEs and the studies of type III secretion systems (T3SS). Although these tools can help biological experiments in certain procedures, there is still room for improvement, even for the current best model, as the existing methods adopt hand‐designed feature and traditional machine learning methods.MethodsIn this study, we propose a powerful predictor based on deep learning methods, called WEDeepT3. Our work consists mainly of three key steps. First, we train word embedding vectors for protein sequences in a large‐scale amino acid sequence database. Second, we combine the word vectors with traditional features extracted from protein sequences, like PSSM, to construct a more comprehensive feature representation. Finally, we construct a deep neural network model in the prediction of type III secreted effectors.ResultsThe feature representation of WEDeepT3 consists of both word embedding and position‐specific features. Working together with convolutional neural networks, the new model achieves superior performance to the state‐of‐the‐art methods, demonstrating the effectiveness of the new feature representation and the powerful learning ability of deep models.ConclusionWEDeepT3 exploits both semantic information of k‐mer fragments and evolutional information of protein sequences to accurately differentiate between T3SEs and non‐T3SEs. WEDeepT3 is available at bcmi.sjtu.edu.cn/~yangyang/WEDeepT3.html.

Evaluating Word Similarity Measure of Embeddings Through Binary Classification

We consider the following problem: given neural language models (embeddings) each of which is trained on an unknown data set, how can we determine which model would provide a better result when used for feature representation in a downstream task such as text classification or entity recognition? In this paper, we assess the word similarity measure through analyzing its impact on word embeddings learned from various datasets and how they perform in a simple classification task. Word representations were learned and assessed under the same conditions. For training word vectors, we used the implementation of Continuous Bag of Words described in [1]. To assess the quality of the vectors, we applied the analogy questions test for word similarity described in the same paper. Further, to measure the retrieval rate of an embedding model, we introduced a new metric (Average Retrieval Error) which measures the percentage of missing words in the model. We observe that scoring a high accuracy of syntactic and semantic similarities between word pairs is not an indicator of better classification results. This observation can be justified by the fact that a domain-specific corpus contributes to the performance better than a general-purpose corpus. For reproducibility, we release our experiments scripts and results.

Cognitive and language performance predicts effects of spelling intervention and tDCS in Primary Progressive Aphasia

Predictors of treatment effects allow individual tailoring of treatment characteristics, thereby saving resources and optimizing outcomes. Electrical stimulation coupled with language intervention has shown promising results in improving language performance in individuals with Primary Progressive Aphasia (PPA). The current study aimed to identify language and cognitive variables associated with response to therapy consisting of language intervention combined with transcranial direct current stimulation (tDCS). Forty individuals with PPA received written naming/spelling intervention combined with anodal tDCS or Sham, using a between-subjects, randomized design, with intervention delivered over a period of 3 weeks. Participants were assessed using a battery of neuropsychological tests before and after each phase. We measured letter accuracy during spelling of trained and untrained words, before, immediately after, 2 weeks, and 2 months after therapy. We used step-wise regression methods to identify variables amongst the neuropsychological measures and experimental factors that were significantly associated with therapy outcomes at each time-point. For trained words, improvement was related to pre-therapy scores, in RAVLT (5 trials sum), pseudoword spelling, object naming, digit span backward, spatial span backward and years post symptom onset. Regarding generalization to untrained words, improvement in spelling was associated with pseudoword spelling, RAVLT proactive interference, RAVLT immediate recall. Generalization effects were larger under tDCS compared to Sham at the 2-month post training measurement. We conclude that, for trained words, patients who improve the most are those who retain for longer language skills such as sublexical spelling processes (phoneme-to-grapheme correspondences) and word retrieval, and other cognitive functions such as executive functions and working memory, and those who have a better learning capacity. Generalization to untrained words occurs through improvement in knowledge of phoneme-to-grapheme correspondences. Furthermore, tDCS enhances the generalizability and duration of therapy effects.

In-home auditory training using audiovisual stimuli on a tablet computer: Feasibility and preliminary results

ObjectiveTo examine the feasibility and possible effects of in-home auditory training using audiovisual speech stimuli on a tablet computer for patients with hearing loss using a hearing aid (HA) or a cochlear implant (CI). MethodsIn total, 11 patients with hearing loss (mean age, 60.2 ± 13.7 years) who had been using an HA or CI for more than 1 year were examined. As auditory training, the participants listened repeatedly to audiovisual speech stimuli on a tablet computer for 3 months. Speech intelligibility for trained words, untrained words, and monosyllables presented at a sound pressure level of 70 dB were assessed before and at 1, 2, and 3 months after training. ResultsEight out the 11 patients completed 3 months of in-home auditory training. Three of these patients withdrew from the training before completing the protocol, mainly because of “boredom from recurring simple tasks”. Significant improvements in speech intelligibility were found for the trained and untrained words after the 3-month training period (p < 0.05), but no significant differences were found for monosyllables. ConclusionIn-home auditory training using a tablet computer could help improve auditory quality of life in patients with hearing loss using an HA or CI. But the further comparative studies using other existing method will be necessary to establish the practical importance of the present method.

The effect of linguistic comprehension instruction on generalized language and reading comprehension skills: A systematic review.

The effect of linguistic comprehension instruction on generalized language and reading comprehension skills: A systematic review.

Word reordering algorithm for poetry analysis

Automatic poetry analysis is usually examined as subtask of natural language processing but approaches which are good for prose texts are ineffective for poetry due to free semantic characteristics. It allows us to use large corpus for training words order and rather small text corpus amount. In this paper we suggest the way to adapt poetry texts to prose ones by changing word order without loosing of model and also use word2vec features to improve the classifier work.

Representing Multiword Chemical Terms through Phrase-Level Preprocessing and Word Embedding.

In recent years, data-driven methods and artificial intelligence have been widely used in chemoinformatic and material informatics domains, for which the success is critically determined by the availability of training data with good quality and large quantity. A potential approach to break this bottleneck is by leveraging the chemical literature such as papers and patents as alternative data resources to high throughput experiments and simulation. Compared to other domains where natural language processing techniques have established successes, the chemical literature contains a large portion of phrases of multiple words that create additional challenges for accurate identification and representation. Here, we introduce a chemistry domain suitable approach to identify multiword chemical terms and train word representations at the phrase level. Through a series of special-designed experiments, we demonstrate that our multiword identifying and representing method effectively and accurately identifies multiword chemical terms from 119, 166 chemical patents and is more robust and precise to preserve the semantic meaning of chemical phrases compared to the conventional approach, which represents constituent single words first and combine them afterward. Because the accurate representation of chemical terms is the first and essential step to provide learning features for downstream natural language processing tasks, our results pave the road to utilize the large volume of chemical literature in future data-driven studies.

Reduction o f Time f or Speech Recognition System b y Shrinking Adaptive Layer s o f ADAG f or Real Time System u sing ADAT SVM

Recently speech recognition becomes very major area for large vocabulary real time applications. In the existing work research was formulated for large number of words spoken by same speaker using Adaptive Directed Acyclic Graph (ADAG) with support vector machines. In today’s era the emphasis is given to processing large vocabulary data process and recognition. In existing system, when spoken words are recognizing by number of adaptive layers causes increase in testing time for recognition of data occur. The aim of the proposed Reduced Adaptive Directed Acyclic technique (RADAT) is to develop a system to recognize test word in less amount of time than existing System [1]. In our current system it is possible to remove unnecessary finding distance of test word with number of times with training word. The proposed RADAT system handles this by applying reduction in number of adaptive layers to recognize any testing word and which result reduction in time. The experiment results of current system reduce time complexity without loss of recognition accuracy for any data of speaker dependent system. The results are obtained for various dataset like large vocabulary speech record as well for small size data to large vocabulary dataset. In our paper we use same feature extracted samples for training and testing. This method is work for various speech dataset like machine operation command in English or required language to operate computer system in real time application as well applied for offline application such as start, logoff, login, notepad, open, close, shutdown etc [5].

Emotion analysis of Arabic tweets using deep learning approach

Nowadays, sharing moments on social networks have become something widespread. Sharing ideas, thoughts, and good memories to express our emotions through text without using a lot of words. Twitter, for instance, is a rich source of data that is a target for organizations for which they can use to analyze people’s opinions, sentiments and emotions. Emotion analysis normally gives a more profound overview of the feelings of an author. In Arabic Social Media analysis, nearly all projects have focused on analyzing the expressions as positive, negative or neutral. In this paper we intend to categorize the expressions on the basis of emotions, namely happiness, anger, fear, and sadness. Different approaches have been carried out in the area of automatic textual emotion recognition in the case of other languages, but only a limited number were based on deep learning. Thus, we present our approach used to classify emotions in Arabic tweets. Our model implements a deep Convolutional Neural Networks (CNN) trained on top of trained word vectors specifically on our dataset for sentence classification tasks. We compared the results of this approach with three other machine learning algorithms which are SVM, NB and MLP. The architecture of our deep learning approach is an end-to-end network with word, sentence, and document vectorization steps. The deep learning proposed approach was evaluated on the Arabic tweets dataset provided by SemiEval for the EI-oc task, and the results-compared to the traditional machine learning approaches-were excellent.

Novel Word Learning: Event-Related Brain Potentials Reflect Pure Lexical and Task-Related Effects.

Previous research has pointed out that the combination of orthographic and semantic-associative training is a more advantageous strategy for the lexicalization of novel written word-forms than their single orthographic training. However, paradigms used previously involve explicit stimuli categorization (lexical decision), which likely influence word learning. In the present study, we used a more automatic task (silent reading) to determine the advantage of the associative training, by comparing the brain electrical signals elicited in combined (orthographic and semantic) and single (only orthographic) training conditions. In addition, the learning effect (in terms of similar neurophysiological activity between novel and known words) was also tested under a categorization paradigm, enabling determination of the possible influence of the training task in the lexicalization process. Results indicated that novel words repeatedly associated with meaningful cues showed a higher attenuation of N400 responses than those trained in the single orthographic condition, confirming the higher facilitation in the lexico-semantic processing of these stimuli, as a consequence of semantic associations. Moreover, only when the combined training was carried out in the reading task did novel words show similar N400 responses to those elicited by known words, suggesting the achievement of a similar lexical processing to known words. Crucially, when the training is carried out under a demanding task context (lexical decision), known words exhibited positive enhancement within the N400 time window, contributing to maintaining N400 differences with novel trained words and confounding the outcome of the learning. Such deflection—compatible with the modulation of the categorization-related P300 component—suggests that novel word learning could be influenced by the activation of categorization-related processes. Thus, the use of low-demand tasks arises as a more appropriate approach to study novel word learning, enabling the build-up process of mental representations, which probably depends on pure lexical and semantic factors rather than being guided by categorization demands.

288 Predicting Potential Bouncebacks to the Emergency Department: A Machine Learning Approach

Review of patients who return to the emergency department (ED) within 72 hours (ie, bounceback) is a common quality assurance process. Current practice of studying bouncebacks, which relies on manual chart extraction and review, is labor-intensive and limited. In this study, we leverage machine learning methods to automatically extract features from electronic health record (EHR) data, learn patterns among patients who “bounceback,” and develop an EHR-driven risk prediction model. This is a retrospective analysis of EHR data and provider notes of all adult patients who presented to a single-center, urban emergency department (ED) over 4.25 years (1/1/2015—4/1/2019). Two different deep learning approaches have been employed to analyze a combination of structured variables (demographics and vital signs at the time of ED admission) as well as unstructured EHR free text (ED provider notes) to predict patients’ risk of bounceback. Both approaches involved training word embeddings using the EHR free text notes. In the first approach each EHR note was converted to a sequence of word embedding vectors and the sequence was used to train a stacked Long Short-Term Memory Recurrent Neural Network (LSTM-RNN), whereas in the second approach each EHR note was abstracted to a single embedding vector using a Doc2Vec algorithm and it was passed through a fully connected (dense) deep network architecture. The resultant vector in each approach was then concatenated to a vector of structured predictors and passed through multiple fully connected network layers to predict the outcome. The accuracy of predicting the bounceback target variable is characterized by sensitivity, specificity, and the area under the receiver operating characteristic curve (AUROC). Of the adult patients (n = 330,407) who presented to the ED during this time period, 4.7% (n = 15,529) subsequently returned within 72 hours. The LSTM model produced a sensitivity and specificity of 66.3% and 59%, respectively (AUROC = 0.686). The sensitivity and specificity improved with the Doc2Vec approach, to 67.9% and 66.8%, respectively. The resulting AUROC was found to be 0.755, indicating the higher distinctive power of the second approach. In this retrospective analysis of EHR data, deep learning models demonstrated promising results as a proof of concept for uncovering predictive local motifs in the ED provider note texts among patients who return within 72 hours. Once fully developed and deployed, these predictive models could aid clinical quality review of bouncebacks in the ED and serve as adjuncts to clinical decision support.