Moussab/ORKG-training-evaluation-set huggingface dataset:
Research field: Bioinformatics
Title: Learning Spatial–Spectral–Temporal EEG Features With Recurrent 3D Convolutional Neural Networks for Cross-Task Mental Workload Assessment
Abstract: Mental workload assessment is essential for maintaining human health and preventing accidents. Most research on this issue is limited to a single task. However, cross-task assessment is indispensable for extending a pre-trained model to new workload conditions. Because brain dynamics are complex across different tasks, it is difficult to propose efficient human-designed features based on prior knowledge. Therefore, this paper proposes a concatenated structure of deep recurrent and 3D convolutional neural networks (R3DCNNs) to learn EEG features across different tasks without prior knowledge. First, this paper adds frequency and time dimensions to EEG topographic maps based on a Morlet wavelet transformation. Then, R3DCNN is proposed to simultaneously learn EEG features from the spatial, spectral, and temporal dimensions. The proposed model is validated based on the EEG signals collected from 20 subjects. This paper employs a binary classification of low and high mental workload across spatial n-back and arithmetic tasks. The results show that the R3DCNN achieves an average accuracy of 88.9%, which is a significant increase compared with that of the state-of-the-art methods. In addition, the visualization of the convolutional layers demonstrates that the deep neural network can extract detailed features. These results indicate that R3DCNN is capable of identifying the mental workload levels for cross-task conditions.
Extracted statement — Used models: CNN
Research field: Bioinformatics
Title: Assessing the severity of positive valence symptoms in initial psychiatric evaluation records: Should we use convolutional neural networks?
Abstract: Background and objective Efficiently capturing the severity of positive valence symptoms could aid in risk stratification for adverse outcomes among patients with psychiatric disorders and identify optimal treatment strategies for patient subgroups. Motivated by the success of convolutional neural networks (CNNs) in classification tasks, we studied the application of various CNN architectures and their performance in predicting the severity of positive valence symptoms in patients with psychiatric disorders based on initial psychiatric evaluation records. Methods Psychiatric evaluation records contain unstructured text and semi-structured data such as question–answer pairs. For a given record, we tokenise and normalise the semi-structured content. Pre-processed tokenised words are represented as one-hot encoded word vectors. We then apply different configurations of convolutional and max pooling layers to automatically learn important features from various word representations. We conducted a series of experiments to explore the effect of different CNN architectures on the classification of psychiatric records. Results Our best CNN model achieved a mean absolute error (MAE) of 0.539 and a normalized MAE of 0.785 on the test dataset, which is comparable to the other well-known text classification algorithms studied in this work. Our results also suggest that the normalisation step has a great impact on the performance of the developed models. Conclusions We demonstrate that normalisation of the semi-structured contents can improve the MAE among all CNN configurations. Without advanced feature engineering, CNN-based approaches can provide a comparable solution for classifying positive valence symptom severity in initial psychiatric evaluation records. Although word embedding is well known for its ability to capture relatively low-dimensional similarity between words, our experimental results show that pre-trained embeddings do not improve the classification performance. This phenomenon may be due to the inability of word embeddings to capture problem specific contextual semantic information implying the quality of the employing embedding is critical for obtaining an accurate CNN model.
Extracted statement — Used models: CNN
Research field: Bioinformatics
Title: DeepBipolar: Identifying genomic mutations for bipolar disorder via deep learning
Abstract: Bipolar disorder, also known as manic depression, is a brain disorder that affects the brain structure of a patient. It results in extreme mood swings, severe states of depression, and overexcitement simultaneously. It is estimated that roughly 3% of the population of the United States (about 5.3 million adults) suffers from bipolar disorder. Recent research efforts like the Twin studies have demonstrated a high heritability factor for the disorder, making genomics a viable alternative for detecting and treating bipolar disorder, in addition to the conventional lengthy and costly postsymptom clinical diagnosis. Motivated by this study, leveraging several emerging deep learning algorithms, we design an end‐to‐end deep learning architecture (called DeepBipolar) to predict bipolar disorder based on limited genomic data. DeepBipolar adopts the Deep Convolutional Neural Network (DCNN) architecture that automatically extracts features from genotype information to predict the bipolar phenotype. We participated in the Critical Assessment of Genome Interpretation (CAGI) bipolar disorder challenge and DeepBipolar was considered the most successful by the independent assessor. In this work, we thoroughly evaluate the performance of DeepBipolar and analyze the type of signals we believe could have affected the classifier in distinguishing the case samples from the control set.
Extracted statement — Used models: CNN
Research field: Bioinformatics
Title: DepAudioNet: An Efficient Deep Model for Audio based Depression Classification
Abstract: This paper presents a novel and effective audio based method on depression classification. It focuses on two important issues, \emph{i.e.} data representation and sample imbalance, which are not well addressed in literature. For the former one, in contrast to traditional shallow hand-crafted features, we propose a deep model, namely DepAudioNet, to encode the depression related characteristics in the vocal channel, combining Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) to deliver a more comprehensive audio representation. For the latter one, we introduce a random sampling strategy in the model training phase to balance the positive and negative samples, which largely alleviates the bias caused by uneven sample distribution. Evaluations are carried out on the DAIC-WOZ dataset for the Depression Classification Sub-challenge (DCC) at the 2016 Audio-Visual Emotion Challenge (AVEC), and the experimental results achieved clearly demonstrate the effectiveness of the proposed approach.
Extracted statement — Used models: CNN
Research field: Bioinformatics
Title: DeepBreath: Deep learning of breathing patterns for automatic stress recognition using low-cost thermal imaging in unconstrained settings
Abstract: We propose DeepBreath, a deep learning model which automatically recognises people's psychological stress level (mental overload) from their breathing patterns. Using a low cost thermal camera, we track a person's breathing patterns as temperature changes around his/her nostril. The paper's technical contribution is threefold. First of all, instead of creating handcrafted features to capture aspects of the breathing patterns, we transform the uni-dimensional breathing signals into two dimensional respiration variability spectrogram (RVS) sequences. The spectrograms easily capture the complexity of the breathing dynamics. Second, a spatial pattern analysis based on a deep Convolutional Neural Network (CNN) is directly applied to the spectrogram sequences without the need of hand-crafting features. Finally, a data augmentation technique, inspired from solutions for over-fitting problems in deep learning, is applied to allow the CNN to learn with a small-scale dataset from short-term measurements (e.g., up to a few hours). The model is trained and tested with data collected from people exposed to two types of cognitive tasks (Stroop Colour Word Test, Mental Computation test) with sessions of different difficulty levels. Using normalised self-report as ground truth, the CNN reaches 84.59% accuracy in discriminating between two levels of stress and 56.52% in discriminating between three levels. In addition, the CNN outperformed powerful shallow learning methods based on a single layer neural network. Finally, the dataset of labelled thermal images will be open to the community.
Extracted statement — Used models: CNN
Research field: Bioinformatics
Title: DCNN and DNN based multi-modal depression recognition
Abstract: In this paper, we propose an audio visual multimodal depression recognition framework composed of deep convolutional neural network (DCNN) and deep neural network (DNN) models. For each modality, corresponding feature descriptors are input into a DCNN to learn high-level global features with compact dynamic information, which are then fed into a DNN to predict the PHQ-8 score. For multi-modal depression recognition, the predicted PHQ-8 scores from each modality are integrated in a DNN for the final prediction. In addition, we propose the Histogram of Displacement Range as a novel global visual descriptor to quantify the range and speed of the facial landmarks' displacements. Experiments have been carried out on the Distress Analysis Interview Corpus-Wizard of Oz (DAIC-WOZ) dataset for the Depression Sub-challenge of the Audio-Visual Emotion Challenge (AVEC 2016), results show that the proposed multi-modal depression recognition framework obtains very promising results on both the development set and test set, which outperforms the state-of-the-art results.
Extracted statement — Used models: CNN
Research field: Bioinformatics
Title: User-level psychological stress detection from social media using deep neural network
Abstract: It is of significant importance to detect and manage stress before it turns into severe problems. However, existing stress detection methods usually rely on psychological scales or physiological devices, making the detection complicated and costly. In this paper, we explore to automatically detect individuals' psychological stress via social media. Employing real online micro-blog data, we first investigate the correlations between users' stress and their tweeting content, social engagement and behavior patterns. Then we define two types of stress-related attributes: 1) low-level content attributes from a single tweet, including text, images and social interactions; 2) user-scope statistical attributes through their weekly micro-blog postings, leveraging information of tweeting time, tweeting types and linguistic styles. To combine content attributes with statistical attributes, we further design a convolutional neural network (CNN) with cross autoencoders to generate user-scope content attributes from low-level content attributes. Finally, we propose a deep neural network (DNN) model to incorporate the two types of user-scope attributes to detect users' psychological stress. We test the trained model on four different datasets from major micro-blog platforms including Sina Weibo, Tencent Weibo and Twitter. Experimental results show that the proposed model is effective and efficient on detecting psychological stress from micro-blog data. We believe our model would be useful in developing stress detection tools for mental health agencies and individuals.
Extracted statement — Used models: CNN
Research field: Bioinformatics
Title: Intrinsic Functional Connectivity in Salience and Default Mode Networks and Aberrant Social Processes in Youth at Ultra-High Risk for Psychosis
Abstract: Social processes are key to navigating the world, and investigating their underlying mechanisms and cognitive architecture can aid in understanding disease states such as schizophrenia, where social processes are highly impacted. Evidence suggests that social processes are impaired in individuals at ultra high-risk for the development of psychosis (UHR). Understanding these phenomena in UHR youth may clarify disease etiology and social processes in a period that is characterized by significantly fewer confounds than schizophrenia. Furthermore, understanding social processing deficits in this population will help explain these processes in healthy individuals. The current study examined resting state connectivity of the salience (SN) and default mode networks (DMN) in association with facial emotion recognition (FER), an integral aspect of social processes, as well as broader social functioning (SF) in UHR individuals and healthy controls. Consistent with the existing literature, UHR youth were impaired in FER and SF when compared with controls. In the UHR group, we found increased connectivity between the SN and the medial prefrontal cortex, an area of the DMN relative to controls. In UHR youth, the DMN exhibited both positive and negative correlations with the somatosensory cortex/cerebellum and precuneus, respectively, which was linked with better FER performance. For SF, results showed that sensory processing links with the SN might be important in allowing for better SF for both groups, but especially in controls where sensory processing is likely to be unimpaired. These findings clarify how social processing deficits may manifest in psychosis, and underscore the importance of SN and DMN connectivity for social processing more generally.
Extracted statement — uses: CONN
Research field: Bioinformatics
Title: dcGOR: An R Package for Analysing Ontologies and Protein Domain Annotations
Abstract: I introduce an open-source R package ‘dcGOR’ to provide the bioinformatics community with the ease to analyse ontologies and protein domain annotations, particularly those in the dcGO database. The dcGO is a comprehensive resource for protein domain annotations using a panel of ontologies including Gene Ontology. Although increasing in popularity, this database needs statistical and graphical support to meet its full potential. Moreover, there are no bioinformatics tools specifically designed for domain ontology analysis. As an add-on package built in the R software environment, dcGOR offers a basic infrastructure with great flexibility and functionality. It implements new data structure to represent domains, ontologies, annotations, and all analytical outputs as well. For each ontology, it provides various mining facilities, including: (i) domain-based enrichment analysis and visualisation; (ii) construction of a domain (semantic similarity) network according to ontology annotations; and (iii) significance analysis for estimating a contact (statistical significance) network. To reduce runtime, most analyses support high-performance parallel computing. Taking as inputs a list of protein domains of interest, the package is able to easily carry out in-depth analyses in terms of functional, phenotypic and diseased relevance, and network-level understanding. More importantly, dcGOR is designed to allow users to import and analyse their own ontologies and annotations on domains (taken from SCOP, Pfam and InterPro) and RNAs (from Rfam) as well. The package is freely available at CRAN for easy installation, and also at GitHub for version control. The dedicated website with reproducible demos can be found at http://supfam.org/dcGOR.
Extracted statement — uses: CRAN
Research field: Bioinformatics
Title: Diagnosis of attention deficit hyperactivity disorder using deep belief network based on greedy approach
Abstract: Attention deficit hyperactivity disorder creates conditions for the child as s/he cannot sit calm and still, control his/her behavior and focus his/her attention on a particular issue. Five out of every hundred children are affected by the disease. Boys are three times more than girls at risk for this complication. The disorder often begins before age seven, and parents may not realize their children problem until they get older. Children with hyperactivity and attention deficit are at high risk of conduct disorder, antisocial personality, and drug abuse. Most children suffering from the disease will develop a feeling of depression, anxiety and lack of self-confidence. Given the importance of diagnosis the disease, Deep Belief Networks (DBNs) were used as a deep learning model to predict the disease. In this system, in addition to FMRI images features, sophisticated features such as age and IQ as well as functional characteristics, etc. were used. The proposed method was evaluated by two standard data sets of ADHD-200 Global Competitions, including NeuroImage and NYU data sets, and compared with state-of-the-art algorithms. The results showed the superiority of the proposed method rather than other systems. The prediction accuracy has improved respectively as +12.04 and +27.81 over NeuroImage and NYU datasets compared to the best proposed method in the ADHD-200 Global competition.
Extracted statement — Used models: DBN
Research field: Bioinformatics
Title: DOGS: Reaction-Driven de novo Design of Bioactive Compounds
Abstract: We present a computational method for the reaction-based de novo design of drug-like molecules. The software DOGS (Design of Genuine Structures) features a ligand-based strategy for automated ‘in silico’ assembly of potentially novel bioactive compounds. The quality of the designed compounds is assessed by a graph kernel method measuring their similarity to known bioactive reference ligands in terms of structural and pharmacophoric features. We implemented a deterministic compound construction procedure that explicitly considers compound synthesizability, based on a compilation of 25'144 readily available synthetic building blocks and 58 established reaction principles. This enables the software to suggest a synthesis route for each designed compound. Two prospective case studies are presented together with details on the algorithm and its implementation. De novo designed ligand candidates for the human histamine H4 receptor and γ-secretase were synthesized as suggested by the software. The computational approach proved to be suitable for scaffold-hopping from known ligands to novel chemotypes, and for generating bioactive molecules with drug-like properties.
Extracted statement — creates: DOGS
Research field: Bioinformatics
Title: ENCORE: Software for Quantitative Ensemble Comparison
Abstract: There is increasing evidence that protein dynamics and conformational changes can play an important role in modulating biological function. As a result, experimental and computational methods are being developed, often synergistically, to study the dynamical heterogeneity of a protein or other macromolecules in solution. Thus, methods such as molecular dynamics simulations or ensemble refinement approaches have provided conformational ensembles that can be used to understand protein function and biophysics. These developments have in turn created a need for algorithms and software that can be used to compare structural ensembles in the same way as the root-mean-square-deviation is often used to compare static structures. Although a few such approaches have been proposed, these can be difficult to implement efficiently, hindering a broader applications and further developments. Here, we present an easily accessible software toolkit, called ENCORE, which can be used to compare conformational ensembles generated either from simulations alone or synergistically with experiments. ENCORE implements three previously described methods for ensemble comparison, that each can be used to quantify the similarity between conformational ensembles by estimating the overlap between the probability distributions that underlie them. We demonstrate the kinds of insights that can be obtained by providing examples of three typical use-cases: comparing ensembles generated with different molecular force fields, assessing convergence in molecular simulations, and calculating differences and similarities in structural ensembles refined with various sources of experimental data. We also demonstrate efficient computational scaling for typical analyses, and robustness against both the size and sampling of the ensembles. ENCORE is freely available and extendable, integrates with the established MDAnalysis software package, reads ensemble data in many common formats, and can work with large trajectory files.
Extracted statement — creates: ENCORE
Research field: Bioinformatics
Title: ENCORE: Software for Quantitative Ensemble Comparison
Abstract: There is increasing evidence that protein dynamics and conformational changes can play an important role in modulating biological function. As a result, experimental and computational methods are being developed, often synergistically, to study the dynamical heterogeneity of a protein or other macromolecules in solution. Thus, methods such as molecular dynamics simulations or ensemble refinement approaches have provided conformational ensembles that can be used to understand protein function and biophysics. These developments have in turn created a need for algorithms and software that can be used to compare structural ensembles in the same way as the root-mean-square-deviation is often used to compare static structures. Although a few such approaches have been proposed, these can be difficult to implement efficiently, hindering a broader applications and further developments. Here, we present an easily accessible software toolkit, called ENCORE, which can be used to compare conformational ensembles generated either from simulations alone or synergistically with experiments. ENCORE implements three previously described methods for ensemble comparison, that each can be used to quantify the similarity between conformational ensembles by estimating the overlap between the probability distributions that underlie them. We demonstrate the kinds of insights that can be obtained by providing examples of three typical use-cases: comparing ensembles generated with different molecular force fields, assessing convergence in molecular simulations, and calculating differences and similarities in structural ensembles refined with various sources of experimental data. We also demonstrate efficient computational scaling for typical analyses, and robustness against both the size and sampling of the ensembles. ENCORE is freely available and extendable, integrates with the established MDAnalysis software package, reads ensemble data in many common formats, and can work with large trajectory files.
Extracted statement — deposits: ENCORE