scholarly journals Viability in Multiplex Lexical Networks and Machine Learning Characterizes Human Creativity

2019 ◽  
Vol 3 (3) ◽  
pp. 45 ◽  
Author(s):  
Massimo Stella ◽  
Yoed N. Kenett
Keyword(s):  
Machine Learning ◽  
Individual Differences ◽  
Language Learning ◽  
Conceptual Knowledge ◽  
Area Under The Curve ◽  
Mental Lexicon ◽  
Network Models ◽  
Semantic Fluency ◽  
Multiplex Network ◽  
Fluency Task

Previous studies have shown how individual differences in creativity relate to differences in the structure of semantic memory. However, the latter is only one aspect of the whole mental lexicon, a repository of conceptual knowledge that is considered to simultaneously include multiple types of conceptual similarities. In the current study, we apply a multiplex network approach to compute a representation of the mental lexicon combining semantics and phonology and examine how it relates to individual differences in creativity. This multiplex combination of 150,000 phonological and semantic associations identifies a core of words in the mental lexicon known as viable cluster, a kernel containing simpler to parse, more general, concrete words acquired early during language learning. We focus on low (N = 47) and high (N = 47) creative individuals’ performance in generating animal names during a semantic fluency task. We model this performance as the outcome of a mental navigation on the multiplex lexical network, going within, outside, and in-between the viable cluster. We find that low and high creative individuals differ substantially in their access to the viable cluster during the semantic fluency task. Higher creative individuals tend to access the viable cluster less frequently, with a lower uncertainty/entropy, reaching out to more peripheral words and covering longer multiplex network distances between concepts in comparison to lower creative individuals. We use these differences for constructing a machine learning classifier of creativity levels, which leads to an accuracy of 65 . 0 ± 0 . 9 % and an area under the curve of 68 . 0 ± 0 . 8 % , which are both higher than the random expectation of 50%. These results highlight the potential relevance of combining psycholinguistic measures with multiplex network models of the mental lexicon for modelling mental navigation and, consequently, classifying people automatically according to their creativity levels.

Individual differences in semantic switching flexibility: Effects of handedness

2009 ◽  
Vol 15 (6) ◽  
pp. 1023-1027 ◽  
Author(s):  
VARALAKSHMI SONTAM ◽  
STEPHEN D. CHRISTMAN ◽  
JOHN D. JASPER
Keyword(s):  
Individual Differences ◽  
Cognitive Deficits ◽  
Right Hemisphere ◽  
Assessment Tool ◽  
Normal Population ◽  
Semantic Fluency ◽  
Per Se ◽  
Animal Names ◽  
The Right ◽  
Fluency Task

AbstractThe semantic fluency task is a widely used assessment tool for evaluating memory-related cognitive deficits in neurological and neuropsychiatric disorders. The present study investigates individual differences in performance on this task in a normal population. The aim is to explore handedness differences in switching and clustering tendencies when performing this task. Consistent with our prediction, when asked to produce as many animal names as possible in 1 min, mixed handers demonstrated greater switching between different subcategories of animals than strong handers. These findings are interpreted in terms of the more diffuse spread of activation among conceptual representations in the right hemisphere, and greater access to right hemisphere processes in mixed handers. The findings have implications for the research communities using the semantic fluency task, irrespective of whether or not they are looking at handedness differences per se. (JINS, 2009, 15, 1023–1027.)

Individual differences predict ERP signatures of second language learning of novel grammatical rules

2017 ◽  
Author(s):  
Emily S Nichols ◽  
Marc F Joanisse
Keyword(s):  
Second Language ◽  
Individual Differences ◽  
Language Learning ◽  
First Language ◽  
Event Related Potentials ◽  
Grammatical Gender ◽  
Grammatical Rule ◽  
Related Potentials ◽  
And Gender ◽  
Anterior Negativity

We investigated the extent to which second-language (L2) learning is influenced by the similarity of grammatical features in one’s first language (L1). We used event-related potentials to identify neural signatures of a novel grammatical rule - grammatical gender - in L1 English speakers. Of interest was whether individual differences in L2 proficiency and age of acquisition (AoA) influenced these effects. L2 and native speakers of French read French sentences that were grammatically correct, or contained either a grammatical gender or word order violation. Proficiency and AoA predicted Left Anterior Negativity amplitude, with structure violations driving the proficiency effect and gender violations driving the AoA effect. Proficiency, group, and AoA predicted P600 amplitude for gender violations but not structure violations. Different effects of grammatical gender and structure violations indicate that L2 speakers engage novel grammatical processes differently from L1 speakers and that this varies appreciably based on both AoA and proficiency.

scholarly journals Early Prediction of Seven-Day Mortality in Intensive Care Unit Using a Machine Learning Model: Results from the SPIN-UTI Project

2021 ◽  
Vol 10 (5) ◽  
pp. 992
Author(s):  
Martina Barchitta ◽  
Andrea Maugeri ◽  
Giuliana Favara ◽  
Paolo Marco Riela ◽  
Giovanni Gallo ◽  
...  
Keyword(s):  
Machine Learning ◽  
Intensive Care ◽  
Intensive Care Units ◽  
Learning Algorithm ◽  
Area Under The Curve ◽  
Support Vector ◽  
Icu Admission ◽  
Risk Of Death ◽  
Saps Ii ◽  
Svm Algorithm

Patients in intensive care units (ICUs) were at higher risk of worsen prognosis and mortality. Here, we aimed to evaluate the ability of the Simplified Acute Physiology Score (SAPS II) to predict the risk of 7-day mortality, and to test a machine learning algorithm which combines the SAPS II with additional patients’ characteristics at ICU admission. We used data from the “Italian Nosocomial Infections Surveillance in Intensive Care Units” network. Support Vector Machines (SVM) algorithm was used to classify 3782 patients according to sex, patient’s origin, type of ICU admission, non-surgical treatment for acute coronary disease, surgical intervention, SAPS II, presence of invasive devices, trauma, impaired immunity, antibiotic therapy and onset of HAI. The accuracy of SAPS II for predicting patients who died from those who did not was 69.3%, with an Area Under the Curve (AUC) of 0.678. Using the SVM algorithm, instead, we achieved an accuracy of 83.5% and AUC of 0.896. Notably, SAPS II was the variable that weighted more on the model and its removal resulted in an AUC of 0.653 and an accuracy of 68.4%. Overall, these findings suggest the present SVM model as a useful tool to early predict patients at higher risk of death at ICU admission.

scholarly journals Perceptual insensitivity to the modulation of interoceptive signals in depression, anxiety, and substance use disorders

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Ryan Smith ◽  
◽  
Justin S. Feinstein ◽  
Rayus Kuplicki ◽  
Katherine L. Forthman ◽  
...  
Keyword(s):  
Machine Learning ◽  
Substance Use ◽  
Individual Differences ◽  
Substance Use Disorders ◽  
Breath Hold ◽  
Healthy Individuals ◽  
Performance Improvements ◽  
Heartbeat Perception ◽  
Minimal Improvement ◽  
Healthy Participants

AbstractThis study employed a series of heartbeat perception tasks to assess the hypothesis that cardiac interoceptive processing in individuals with depression/anxiety (N = 221), and substance use disorders (N = 136) is less flexible than that of healthy individuals (N = 53) in the context of physiological perturbation. Cardiac interoception was assessed via heartbeat tapping when: (1) guessing was allowed; (2) guessing was not allowed; and (3) experiencing an interoceptive perturbation (inspiratory breath hold) expected to amplify cardiac sensation. Healthy participants showed performance improvements across the three conditions, whereas those with depression/anxiety and/or substance use disorder showed minimal improvement. Machine learning analyses suggested that individual differences in these improvements were negatively related to anxiety sensitivity, but explained relatively little variance in performance. These results reveal a perceptual insensitivity to the modulation of interoceptive signals that was evident across several common psychiatric disorders, suggesting that interoceptive deficits in the realm of psychopathology manifest most prominently during states of homeostatic perturbation.

scholarly journals IIMLP: integrated information-entropy-based method for LncRNA prediction

2021 ◽  
Vol 22 (S3) ◽  
Author(s):  
Junyi Li ◽  
Huinian Li ◽  
Xiao Ye ◽  
Li Zhang ◽  
Qingzhe Xu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Dna Sequences ◽  
Information Entropy ◽  
Area Under The Curve ◽  
Prediction Method ◽  
Machine Learning Algorithms ◽  
Reading Frame ◽  
Non Coding Rna ◽  
The One ◽  
Long Non Coding Rna

Abstract Background The prediction of long non-coding RNA (lncRNA) has attracted great attention from researchers, as more and more evidence indicate that various complex human diseases are closely related to lncRNAs. In the era of bio-med big data, in addition to the prediction of lncRNAs by biological experimental methods, many computational methods based on machine learning have been proposed to make better use of the sequence resources of lncRNAs. Results We developed the lncRNA prediction method by integrating information-entropy-based features and machine learning algorithms. We calculate generalized topological entropy and generate 6 novel features for lncRNA sequences. By employing these 6 features and other features such as open reading frame, we apply supporting vector machine, XGBoost and random forest algorithms to distinguish human lncRNAs. We compare our method with the one which has more K-mer features and results show that our method has higher area under the curve up to 99.7905%. Conclusions We develop an accurate and efficient method which has novel information entropy features to analyze and classify lncRNAs. Our method is also extendable for research on the other functional elements in DNA sequences.

scholarly journals Predicting bacteriophage hosts based on sequences of annotated receptor-binding proteins

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Dimitri Boeckaerts ◽  
Michiel Stock ◽  
Bjorn Criel ◽  
Hans Gerstmans ◽  
Bernard De Baets ◽  
...  
Keyword(s):  
Machine Learning ◽  
Predictive Model ◽  
Receptor Binding ◽  
Bacterial Infections ◽  
Sequence Data ◽  
Sequence Similarity ◽  
Area Under The Curve ◽  
Local Alignment ◽  
Search Tool ◽  
Different Levels

AbstractNowadays, bacteriophages are increasingly considered as an alternative treatment for a variety of bacterial infections in cases where classical antibiotics have become ineffective. However, characterizing the host specificity of phages remains a labor- and time-intensive process. In order to alleviate this burden, we have developed a new machine-learning-based pipeline to predict bacteriophage hosts based on annotated receptor-binding protein (RBP) sequence data. We focus on predicting bacterial hosts from the ESKAPE group, Escherichia coli, Salmonella enterica and Clostridium difficile. We compare the performance of our predictive model with that of the widely used Basic Local Alignment Search Tool (BLAST). Our best-performing predictive model reaches Precision-Recall Area Under the Curve (PR-AUC) scores between 73.6 and 93.8% for different levels of sequence similarity in the collected data. Our model reaches a performance comparable to that of BLASTp when sequence similarity in the data is high and starts outperforming BLASTp when sequence similarity drops below 75%. Therefore, our machine learning methods can be especially useful in settings in which sequence similarity to other known sequences is low. Predicting the hosts of novel metagenomic RBP sequences could extend our toolbox to tune the host spectrum of phages or phage tail-like bacteriocins by swapping RBPs.

scholarly journals Value of radiomics in differential diagnosis of chromophobe renal cell carcinoma and renal oncocytoma

2019 ◽  
Vol 45 (10) ◽  
pp. 3193-3201 ◽  
Author(s):  
Yajuan Li ◽  
Xialing Huang ◽  
Yuwei Xia ◽  
Liling Long
Keyword(s):  
Machine Learning ◽  
Differential Diagnosis ◽  
Cell Carcinoma ◽  
Area Under The Curve ◽  
Image Features ◽  
Renal Tumors ◽  
Support Vector ◽  
Svm Classifier ◽  
Renal Oncocytoma ◽  
Lasso Regression

Abstract Purpose To explore the value of CT-enhanced quantitative features combined with machine learning for differential diagnosis of renal chromophobe cell carcinoma (chRCC) and renal oncocytoma (RO). Methods Sixty-one cases of renal tumors (chRCC = 44; RO = 17) that were pathologically confirmed at our hospital between 2008 and 2018 were retrospectively analyzed. All patients had undergone preoperative enhanced CT scans including the corticomedullary (CMP), nephrographic (NP), and excretory phases (EP) of contrast enhancement. Volumes of interest (VOIs), including lesions on the images, were manually delineated using the RadCloud platform. A LASSO regression algorithm was used to screen the image features extracted from all VOIs. Five machine learning classifications were trained to distinguish chRCC from RO by using a fivefold cross-validation strategy. The performance of the classifier was mainly evaluated by areas under the receiver operating characteristic (ROC) curve and accuracy. Results In total, 1029 features were extracted from CMP, NP, and EP. The LASSO regression algorithm was used to screen out the four, four, and six best features, respectively, and eight features were selected when CMP and NP were combined. All five classifiers had good diagnostic performance, with area under the curve (AUC) values greater than 0.850, and support vector machine (SVM) classifier showed a diagnostic accuracy of 0.945 (AUC 0.964 ± 0.054; sensitivity 0.999; specificity 0.800), showing the best performance. Conclusions Accurate preoperative differential diagnosis of chRCC and RO can be facilitated by a combination of CT-enhanced quantitative features and machine learning.

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