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General General

A Machine-Based Prediction Model of ADHD Using CPT Data.

In Frontiers in human neuroscience ; h5-index 79.0

Despite the popularity of the continuous performance test (CPT) in the diagnosis of attention-deficit/hyperactivity disorder (ADHD), its specificity, sensitivity, and ecological validity are still debated. To address some of the known shortcomings of traditional analysis and interpretation of CPT data, the present study applied a machine learning-based model (ML) using CPT indices for the Prediction of ADHD.Using a retrospective factorial fitting, followed by a bootstrap technique, we trained, cross-validated, and tested learning models on CPT performance data of 458 children aged 6-12 years (213 children with ADHD and 245 typically developed children). We used the MOXO-CPT version that included visual and auditory stimuli distractors. Results showed that the ML proposed model performed better and had a higher accuracy than the benchmark approach that used clinical data only. Using the CPT total score (that included all four indices: Attention, Timeliness, Hyperactivity, and Impulsiveness), as well as four control variables [age, gender, day of the week (DoW), time of day (ToD)], provided the most salient information for discriminating children with ADHD from their typically developed peers. This model had an accuracy rate of 87%, a sensitivity rate of 89%, and a specificity rate of 84%. This performance was 34% higher than the best-achieved accuracy of the benchmark model. The ML detection model could classify children with ADHD with high accuracy based on CPT performance. ML model of ADHD holds the promise of enhancing, perhaps complementing, behavioral assessment and may be used as a supportive measure in the evaluation of ADHD.

Slobodin Ortal, Yahav Inbal, Berger Itai


attention-deficit/hyperactivity disorder, children, continuous performance test, machine learning, prediction

General General

Evolution of Sequence-based Bioinformatics Tools for Protein-protein Interaction Prediction.

In Current genomics

Protein-protein interactions (PPIs) are the physical connections between two or more proteins via electrostatic forces or hydrophobic effects. Identification of the PPIs is pivotal, which contributes to many biological processes including protein function, disease incidence, and therapy design. The experimental identification of PPIs via high-throughput technology is time-consuming and expensive. Bioinformatics approaches are expected to solve such restrictions. In this review, our main goal is to provide an inclusive view of the existing sequence-based computational prediction of PPIs. Initially, we briefly introduce the currently available PPI databases and then review the state-of-the-art bioinformatics approaches, working principles, and their performances. Finally, we discuss the caveats and future perspective of the next generation algorithms for the prediction of PPIs.

Khatun Mst Shamima, Shoombuatong Watshara, Hasan Md Mehedi, Kurata Hiroyuki


PPIs database, Protein-protein interactions, bioinformatics, feature selection, machine learning, sequence features

General General

Assessing the susceptibility of schools to flood events in Iran.

In Scientific reports ; h5-index 158.0

Catastrophic floods cause deaths, injuries, and property damages in communities around the world. The losses can be worse among those who are more vulnerable to exposure and this can be enhanced by communities' vulnerabilities. People in undeveloped and developing countries, like Iran, are more vulnerable and may be more exposed to flood hazards. In this study we investigate the vulnerabilities of 1622 schools to flood hazard in Chaharmahal and Bakhtiari Province, Iran. We used four machine learning models to produce flood susceptibility maps. The analytic hierarchy process method was enhanced with distance from schools to create a school-focused flood-risk map. The results indicate that 492 rural schools and 147 urban schools are in very high-risk locations. Furthermore, 54% of rural students and 8% of urban students study schools in locations of very high flood risk. The situation should be examined very closely and mitigating actions are urgently needed.

Yousefi Saleh, Pourghasemi Hamid Reza, Emami Sayed Naeim, Rahmati Omid, Tavangar Shahla, Pouyan Soheila, Tiefenbacher John P, Shamsoddini Shahbaz, Nekoeimehr Mohammad


Dermatology Dermatology

Fast, large area multiphoton exoscope (FLAME) for macroscopic imaging with microscopic resolution of human skin.

In Scientific reports ; h5-index 158.0

We introduce a compact, fast large area multiphoton exoscope (FLAME) system with enhanced molecular contrast for macroscopic imaging of human skin with microscopic resolution. A versatile imaging platform, FLAME combines optical and mechanical scanning mechanisms with deep learning image restoration to produce depth-resolved images that encompass sub-mm2 to cm2 scale areas of tissue within minutes and provide means for a comprehensive analysis of live or resected thick human skin tissue. The FLAME imaging platform, which expands on a design recently introduced by our group, also features time-resolved single photon counting detection to uniquely allow fast discrimination and 3D virtual staining of melanin. We demonstrate its performance and utility by fast ex vivo and in vivo imaging of human skin. With the ability to provide rapid access to depth resolved images of skin over cm2 area and to generate 3D distribution maps of key sub-cellular skin components such as melanocytic dendrites and melanin, FLAME is ready to be translated into a clinical imaging tool for enhancing diagnosis accuracy, guiding therapy and understanding skin biology.

Fast Alexander, Lal Akarsh, Durkin Amanda F, Lentsch Griffin, Harris Ronald M, Zachary Christopher B, Ganesan Anand K, Balu Mihaela


Radiology Radiology

Volumetric breast density estimation on MRI using explainable deep learning regression.

In Scientific reports ; h5-index 158.0

To purpose of this paper was to assess the feasibility of volumetric breast density estimations on MRI without segmentations accompanied with an explainability step. A total of 615 patients with breast cancer were included for volumetric breast density estimation. A 3-dimensional regression convolutional neural network (CNN) was used to estimate the volumetric breast density. Patients were split in training (N = 400), validation (N = 50), and hold-out test set (N = 165). Hyperparameters were optimized using Neural Network Intelligence and augmentations consisted of translations and rotations. The estimated densities were evaluated to the ground truth using Spearman's correlation and Bland-Altman plots. The output of the CNN was visually analyzed using SHapley Additive exPlanations (SHAP). Spearman's correlation between estimated and ground truth density was ρ = 0.81 (N = 165, P < 0.001) in the hold-out test set. The estimated density had a median bias of 0.70% (95% limits of agreement = - 6.8% to 5.0%) to the ground truth. SHAP showed that in correct density estimations, the algorithm based its decision on fibroglandular and fatty tissue. In incorrect estimations, other structures such as the pectoral muscle or the heart were included. To conclude, it is feasible to automatically estimate volumetric breast density on MRI without segmentations, and to provide accompanying explanations.

van der Velden Bas H M, Janse Markus H A, Ragusi Max A A, Loo Claudette E, Gilhuijs Kenneth G A


General General

Predicting Deep Learning Based Multi-Omics Parallel Integration Survival Subtypes in Lung Cancer Using Reverse Phase Protein Array Data.

In Biomolecules

Mortality attributed to lung cancer accounts for a large fraction of cancer deaths worldwide. With increasing mortality figures, the accurate prediction of prognosis has become essential. In recent years, multi-omics analysis has emerged as a useful survival prediction tool. However, the methodology relevant to multi-omics analysis has not yet been fully established and further improvements are required for clinical applications. In this study, we developed a novel method to accurately predict the survival of patients with lung cancer using multi-omics data. With unsupervised learning techniques, survival-associated subtypes in non-small cell lung cancer were first detected using the multi-omics datasets from six categories in The Cancer Genome Atlas (TCGA). The new subtypes, referred to as integration survival subtypes, clearly divided patients into longer and shorter-surviving groups (log-rank test: p = 0.003) and we confirmed that this is independent of histopathological classification (Chi-square test of independence: p = 0.94). Next, an attempt was made to detect the integration survival subtypes using only one categorical dataset. Our machine learning model that was only trained on the reverse phase protein array (RPPA) could accurately predict the integration survival subtypes (AUC = 0.99). The predicted subtypes could also distinguish between high and low risk patients (log-rank test: p = 0.012). Overall, this study explores novel potentials of multi-omics analysis to accurately predict the prognosis of patients with lung cancer.

Takahashi Satoshi, Asada Ken, Takasawa Ken, Shimoyama Ryo, Sakai Akira, Bolatkan Amina, Shinkai Norio, Kobayashi Kazuma, Komatsu Masaaki, Kaneko Syuzo, Sese Jun, Hamamoto Ryuji


deep learning and machine learning, lung cancer, multi-omics analysis