Joseph D. Valencia (17138419), David A. Hendrix (11992487) · Figshare · 2023
<p>Several versions of bioseq2seq, bioseq2start and bioseq2class models were trained on a dataset consisting of sequences from eight mammalian species. Our lowest and highest performing models are shown in this table alongside several top-performing machine learning models also trained on our dataset for comparison. For bioseq2seq-wt (LFN), predictions were made using the leading ‘classification’ token 〈<i>PC</i>〉 or 〈<i>NC</i>〉 of the first beam, terminating inference before the peptide prediction. For our models and RNAsamba, multiple replicates were trained with different random seeds. Evaluation metrics were calculated with 〈<i>PC</i>〉 as the positive class and listed as mean ± std. dev. where multiple replicates are available.</p>
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Karin Schwarzbauer (122589), Ulrich Bodenhofer (122592), Sepp Hochreiter (122596) · Figshare · 2015
<p>The percentages are two-fold cross validation accuracies for the classification of LNFRs showing remodeling versus randomly selected non-LNFR DNA sequences from the human genome. Altogether four sets of remodeled LNFRs were considered: GC-rich and AT-rich remodeled LNFRs, both for resting and activated T cells. Each row corresponds to one choice of , the sub-sequence length parameter of the spectrum kernel. GC-rich remodeling LNFRs (third and fifth column) can be classified with higher accuracy than AT-rich remodeling LNFRs (second and fourth column). The highest accuracy for GC-rich remodeling LNFRs in resting T cells is 68.6% and is achieved for .</p>
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Vittorio Fortino (633002), Pia Kinaret (633003), Nanna Fyhrquist (140718) et al. · Figshare · 2015
<p>The mean accuracy values obtained over the 30 bootstrap iterations. Acc – is the overall accuracy, F – is the F-score, G – is the G-score. The highest values are highlighted in bold. NOTE: all the corresponding standard deviations are less than 0.02.</p><p>Classification performance.</p>
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Marek Śmieja (5659822), Dawid Warszycki (499180) · Figshare · 2016
<p>The relationship between the number of bits selected by AIC-Max algorithm and associated MCC score for every receptor averaged over all folds reported on a test set.</p>
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Kay H. Brodersen (216270), Thomas M. Schofield (216275), Alexander P. Leff (216279) et al. · Figshare · 2015
<p>Classification based on generative embedding using the model shown in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002079#pcbi-1002079-g003" target="_blank">Figure 3</a> was compared to ten alternative methods: anatomical feature selection, contrast feature selection, searchlight feature selection, PCA-based dimensionality reduction, regional correlations based on region means, regional correlations based on eigenvariates, regional z-transformed correlations based on eigenvariates, as well as generative embedding using three biologically unlikely alternative models (see inset legends for abbreviations). (a) The balanced accuracy and its central 95% posterior probability interval show that all methods performed significantly better than chance (50%) with the exception of classification with anatomical feature selection and generative embedding using a nonsensical model. Differences between activation-based methods (light grey) and correlation-based methods (dark grey) were largely statistically indistinguishable. By contrast, using the full model shown in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002079#pcbi-1002079-g003" target="_blank">Figure 3</a>, generative embedding (blue) significantly outperformed all other methods, except when used with biologically unlikely models (<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002079#pcbi-1002079-g005" target="_blank">Figure 5</a>). (b) Receiver-operating characteristic (ROC) curves of the eleven methods illustrate the trade-off between true positive rate (sensitivity) and false positive rate (1 – specificity) across the entire range of detection thresholds. A larger area under the curve is better. (c) Precision-recall (PR) curves illustrate the trade-off between positive prediction value (precision) and true positive rate (recall). A larger area under the curve is better. Smooth ROC and PR curves were obtained using a binormal assumption on the underlying decision values <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002079#pcbi.1002079-Brodersen3" target="_blank">[97]</a>. For a numerical summary of all results, see <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002079#pcbi-1002079-t002" target="_blank">Table 2</a>.</p>
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Steffen Grünewälder (256681), Femke Broekhuis (298113), David Whyte Macdonald (298114) et al. · Figshare · 2015
<p>The table shows the cross-validation performance of our method on the labeled data points. The method is used to classify all data points. Row number 1 shows the overall accuracy. Rows 2–4 show the individual accuracy of detecting the three different classes correctly. Rows 5–7 show the false positive rates, that is the probability that a data point is labeled to belong to class X while it is actually not class X. The number of labeled data points are shown in row 8–11 and line 12 shows the number of data points that we classified with the trained method.</p>
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Nicolò Gozzi (8837522), Daniela Perrotta (3633034), Daniela Paolotti (672356) et al. · Figshare · 2020
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Chao Zhang (51048), Shunfu Xu (164439), Dong Xu (21616) · Figshare · 2015
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Xinyu Zhang (14029), Yoo Jung Oh (14849610), Yunhan Zhang (5901209) et al. · Figshare · 2025
<div> The digital age has fueled a surge in ADHD self-diagnosis as people turn to online platforms for mental health information. However, the relationship between validation-seeking behaviors and self-perception in these online communities and users’ self-perception has received limited scholarly focus. Drawing on self-verification theory and utilizing natural language processing to analyze 452,026 posts from the r/ADHD subreddit, our study uncovers distinct patterns in validation-seeking behaviors. Results show that (a) self-diagnosed individuals with ADHD are more likely to seek social validation and media validation and to report higher levels of negative self-image and internalized stigma than clinically diagnosed individuals, (b) social validation was strongly associated with both positive and negative self-perceptions; and (c) diagnosis status significantly moderated these relationships, such that the effects of social validation on self-image and stigma were consistently weaker for the self-diagnosed group. Theoretically, this study extends self-verification theory by demonstrating that professional verification hierarchically moderates self-verification effectiveness. This implies a practical need for clinicians to acknowledge online validation seeking and for digital communities to affirm user experiences while mitigating stigma. </div>
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Wenhui Chen (3208248), Lianrong Zheng (3343868), Kunyang Li (1806298) et al. · Figshare · 2016
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Tommy Nyberg (469219), Ida Hed Myrberg (3190425), Pernilla Omerov (593705) et al. · Figshare · 2016
<p>Receiver operating characteristic (ROC) curves, and corresponding areas under the curves (AUC) with 95% confidence intervals (CI), for (a) entire cohort, unadjusted and for 100 repetitions of ten-fold stratified cross-validation (SCV), and (b) for each of the four time-frames after cross-validation against a model derived from data in the other three time-frames. The ten-fold SCV adjusted values of AUC and CI limits are the corresponding mean values among the 100 repetitions, and the solid black line is a LOESS smoothed curve for the 100 SCV adjusted ROC curves outlined in gray.</p>
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Minseung Kim (702444), Violeta Zorraquino (702445), Ilias Tagkopoulos (325592) · Figshare · 2015
<p><b>(A)</b> Balanced accuracy in testing set for the 8 classification tasks as a function of number of genes selected. Genes (x-axis) are ordered by the mutual information of their expression to the predictor variable. For each classifier, the optimal number of features (derived from the training data) and the minimum number of genes at near-optimal (within 2%) classification are shown in the legend (first and second value, respectively). <b>(B)</b> Leave-one-batch-out cross-validation, with the training and testing balanced accuracy for each classifier is compared with the baseline. The baseline is estimated by dividing the maximum accuracy (100) by the number of classes for any given characteristic. <b>(C)</b> Combined multi-modal predictions using a set of individual classifiers. The parameter k represents the number of characteristics to be classified (two antibiotics, <i>aerobic or anaerobic respiration, medium, phase</i> and <i>strain</i>), represents all possible combinations and increases from 2 to 7 (x-axis). The average accuracy for each combination of k characteristics to be predicted is reported. <b>(D)</b> ROC curve (left) and PR curve (right) for predictor of each characteristic (TPR; true-positive rate, FPR; false-positive rate, E-Exp; early exponential phase, M/L-Exp; mid/late exponential phase, Stat; stationary phase).</p>
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Alexander Churbanov (10986), Brook Milligan (114191) · Figshare · 2015
<p>Classification performance for subsets of RNA-Seq reads from samples corresponding to various bTB post infection periods against control (TCT1), one month progression (TCT3) and two months progression (TCT5) trained profiles.</p>
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Carlos Ciller (3873340), Sandro De Zanet (3873343), Konstantinos Kamnitsas (3873346) et al. · Figshare · 2017
<p>(a) ROC curve depicting the effect of varying amounts of (training/test) data for RF classification with and without EPSF. (b) ROC curve for both experiments (CNN / RF) with STD and with EPSF. (c) DSC tumor segmentation results for STD vs. EPSF. The latter shows better results for both cases (** = <i>p</i> < 0.01).</p>
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Andreas Leha (7548410), Kristian Hellenkamp (3441212), Bernhard Unsöld (7548413) et al. · Figshare · 2019
<p><b>Random forest of regression trees shows performance comparable to the best of several established PH prediction methods by Aduen et al.</b> (A) Area under the ROC curve (AUC) for all methods with estimated 95% confidence intervals. (B) Consensus ROC curves of the five machine learning algorithms under consideration as well as the ROC curve of the method by Aduen et al. (light blue).</p>
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Yoichiro Takayanagi (216578), Tsutomu Takahashi (216581), Lina Orikabe (216585) et al. · Figshare · 2015
<p>FPR, false positive rate; HC, healthy control; NPV, negative predictive value; PPV, Positive predictive value; SZ, schizophrenia.</p>
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Nikolaos Papandrianos (9243578), Elpiniki Papageorgiou (9243581), Athanasios Anagnostis (9243584) et al. · Figshare · 2020
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Artemy Kolchinsky (737266), Anália Lourenço (317569), Heng-Yi Wu (737267) et al. · Figshare · 2015
<p>Performance for both unigram and bigram runs on non-transformed features. Left: F1 measure. Middle: MCC measure. Right: iAUC measure. The best classifier configuration, and configurations not significantly different (<i>p</i>>0.05, one-tailed test) from it, marked with asterisk ‘*’.</p>
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Chen Liu (75209) · Figshare · 2024
<div><p>In this study, we used unidirectional and bidirectional long short-term memory (LSTM) deep learning networks for Chinese news classification and characterized the effects of contextual information on text classification, achieving a high level of accuracy. A Chinese glossary was created using jieba—a word segmentation tool—stop-word removal, and word frequency analysis. Next, word2vec was used to map the processed words into word vectors, creating a convenient lookup table for word vectors that could be used as feature inputs for the LSTM model. A bidirectional LSTM (BiLSTM) network was used for feature extraction from word vectors to facilitate the transfer of information in both the backward and forward directions to the hidden layer. Subsequently, an LSTM network was used to perform feature integration on all the outputs of the BiLSTM network, with the output from the last layer of the LSTM being treated as the mapping of the text into a feature vector. The output feature vectors were then connected to a fully connected layer to construct a feature classifier using the integrated features, finally classifying the news articles. The hyperparameters of the model were optimized based on the loss between the true and predicted values using the adaptive moment estimation (Adam) optimizer. Additionally, multiple dropout layers were added to the model to reduce overfitting. As text classification models for Chinese news articles, the Bi-LSTM and unidirectional LSTM models obtained f1-scores of 94.15% and 93.16%, respectively, with the former outperforming the latter in terms of feature extraction.</p></div>
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Doaa Sami Khafaga (21463870), El-Sayed M. El-kenawy (14581088), Faris H. Rizk (21755909) et al. · Figshare · 2025
<div> Manual diagnosis of hematological cancers like leukemia through bone marrow smear analysis is labor-intensive, prone to errors, and highly dependent on expert knowledge. To overcome these limitations, this study introduces a comprehensive deep learning framework enhanced with the innovative bio-inspired Ocotillo Optimization Algorithm (OcOA), designed to improve the accuracy and efficiency of bone marrow cell classification. The contributions include developing a baseline Convolutional Neural Network (CNN) that achieves an initial accuracy of 86.29%, surpassing existing state-of-the-art deep learning models. Further integrate the binary variant of OcOA (bOcOA) for effective feature selection, which reduces the average classification error to 0.4237 and increases CNN accuracy to 93.48%. Additionally, utilize the continuous version of OcOA for hyperparameter optimization, further enhancing CNN performance to a maximum accuracy of 98.24%. Crucially, this optimization also results in a substantial clinical performance gain, with sensitivity increasing from 86.02% to 98.34% (+12.32%), specificity rising from 86.53% to 98.14% (+11.61%), and the false negative rate being significantly reduced, thereby enhancing diagnostic reliability in critical scenarios. These findings highlight the potential of metaheuristic optimization techniques to improve the effectiveness of deep learning models in clinical diagnostics quantifiably. The proposed approach demonstrates measurable gains in automated cytology technology, offering a scalable, interpretable, and accurate solution for hematological screening applications. </div>
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