Table_1_Deep learning-based multimodality classification of chronic mild traumatic brain injury using resting-state functional MRI and PET imaging.DOCX by Faezeh Vedaei (11875157)

“…We proposed a novel deep-learning-based framework, including an autoencoder (AE), to extract high-level latent and rectified linear unit (ReLU) and sigmoid activation functions. Single and multimodality algorithms integrating multiple rs-fMRI metrics and PET data were developed. …”

Daily number of Boko Haram and ISWAP events (<i>e</i>), their casualties (<i>c</i>) and the ratio of casualties per event (<i>c</i>/<i>e</i>) between 1 January 2010 and 20 March 20... by Rafael Prieto-Curiel (16993295)

Image 1_A machine learning-derived immune-related prognostic model identifies PLXNA3 as a functional risk gene in colorectal cancer.tif by Hanzhang Lyu (22163404)

“…</p>Results<p>Plexin-A3 (PLXNA3) emerged as a top risk gene within the ensemble model, which achieved strong predictive performance, surpassing conventional clinical indicators. …”

Phenotypes examined in association analyses of the 511 <i>KCNJ1</i> variants in the UK Biobank. by Nga H. Nguyen (2075920)

Subjects: “…verified computational algorithm…”

Design example using the structure of the LecB lectin <i>Pseudomonas aeruginosa</i> strain PA14 (PDB ID: 5A6Y [67]) and the osprey workflow for fries/<i>EWAK</i>*. by Anna U. Lowegard (8051597)

“…<i>EWAK</i>* generally searches over only a subset of the conformations (green area) that previous <i>K</i>*-based algorithms like <i>BBK</i>* [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1007447#pcbi.1007447.ref032" target="_blank">32</a>] search (orange area). …”

This figure visually presents several examples of mHealth technologies identified in our screen by functionality. by Pascal Geldsetzer (549621)

Phase diagram for global archetype model as a function of temperature. by Giulio Ruffini (383994)

Clustering of sequences from full-length Ty1 elements in <i>Saccharomyces</i> strains with Ty1-H3 mobility phenotypes. by Wioletta Czaja (8479428)

Iteration versus fitness function. by B. Venkatesh (17202747)

Retrieving plant functional traits through time series analysis of satellite observations using machine learning methods by Marian Švik (15947016)

“…<p>Plant functional traits (e.g. leaf pigment and water contents, specific leaf area) serve as important indicators of plant condition, both within a given vegetation season and between years. …”

1D seismic velocity model. by Rutinaldo Aguiar Nascimento (14259594)

Subjects:

Table_1_Bioprospecting Trichoderma: A Systematic Roadmap to Screen Genomes and Natural Products for Biocontrol Applications.XLSX by Tomás A. Rush (11440123)

One-line diagram of standard IEEE 30-bus test network. by Abdullah Khan (377292)

F1 obtained for PFP-WGAN and DeepGO-Seq for GO terms as a function of number of available positive training samples. by Seyyede Fatemeh Seyyedsalehi (10196767)

datasheet1_Algorithmic Probability-Guided Machine Learning on Non-Differentiable Spaces.pdf by Santiago Hernández-Orozco (5070209)

“…In doing so we use examples which enable the two approaches to be compared (small, given the computational power required for estimations of algorithmic complexity). We find and report that 1) machine learning can successfully be performed on a non-smooth surface using algorithmic complexity; 2) that solutions can be found using an algorithmic-probability classifier, establishing a bridge between a fundamentally discrete theory of computability and a fundamentally continuous mathematical theory of optimization methods; 3) a formulation of an algorithmically directed search technique in non-smooth manifolds can be defined and conducted; 4) exploitation techniques and numerical methods for algorithmic search to navigate these discrete non-differentiable spaces can be performed; in application of the (a) identification of generative rules from data observations; (b) solutions to image classification problems more resilient against pixel attacks compared to neural networks; (c) identification of equation parameters from a small data-set in the presence of noise in continuous ODE system problem, (d) classification of Boolean NK networks by (1) network topology, (2) underlying Boolean function, and (3) number of incoming edges.…”

DataSheet1_Distributed algorithm without iterations for an integrated energy system.PDF by Jiaming Tan (12539944)

“…For this reason, the result of optimization will be much worse because of the accuracy of cost functions. The greatest challenge of this issue is to establish an algorithm without iteration. …”

datasheet1_Algorithmic Probability-Guided Machine Learning on Non-Differentiable Spaces.pdf by Santiago Hernández-Orozco (5070209)

“…In doing so we use examples which enable the two approaches to be compared (small, given the computational power required for estimations of algorithmic complexity). We find and report that 1) machine learning can successfully be performed on a non-smooth surface using algorithmic complexity; 2) that solutions can be found using an algorithmic-probability classifier, establishing a bridge between a fundamentally discrete theory of computability and a fundamentally continuous mathematical theory of optimization methods; 3) a formulation of an algorithmically directed search technique in non-smooth manifolds can be defined and conducted; 4) exploitation techniques and numerical methods for algorithmic search to navigate these discrete non-differentiable spaces can be performed; in application of the (a) identification of generative rules from data observations; (b) solutions to image classification problems more resilient against pixel attacks compared to neural networks; (c) identification of equation parameters from a small data-set in the presence of noise in continuous ODE system problem, (d) classification of Boolean NK networks by (1) network topology, (2) underlying Boolean function, and (3) number of incoming edges.…”

Systematic discovery of the functional impact of somatic genome alterations in individual tumors through tumor-specific causal inference by Chunhui Cai (5049908)

“…., transcriptomic, proteomic, or metabolomic changes) within an individual tumor. We applied the TCI algorithm to tumors from The Cancer Genome Atlas (TCGA) and estimated for each tumor the SGAs that causally regulate the differentially expressed genes (DEGs) in that tumor. …”

Comparison between the manual segmentation made by an expert (including a manual definition of the MVO region), and the segmentation made by each of the methods within CMRSegTools. by William A. Romero R. (13783210)

Table_1_Mapping the Peds QLTM 4.0 onto CHU-9D: a cross-sectional study in functional dyspepsia population from China.DOCX by Qiqi Wang (557078)

“…Objective<p>The study aims to develop a mapping algorithm from the Pediatric Quality of Life Inventory™ 4. 0 (Peds QL 4.0) onto Child Health Utility 9D (CHU-9D) based on the cross-sectional data of functional dyspepsia (FD) children and adolescents in China.…”