Results 71 to 80 of about 169,825 (305)
Neurosymbolic (NeSy) AI studies the integration of neural networks (NNs) and symbolic reasoning based on logic. Usually, NeSy techniques focus on learning the neural, probabilistic and/or fuzzy parameters of NeSy models. Learning the symbolic or logical structure of such models has, so far, received less attention.
Matthias Möller +3 more
openaire +2 more sources
A Modified Random Forest Based on Kappa Measure and Binary Artificial Bee Colony Algorithm
Random forest (RF) is an ensemble classifier method, all decision trees participate in voting, some low-quality decision trees will reduce the accuracy of random forest.
Chen Zhang +5 more
doaj +1 more source
The dFoCC pipeline starts with observed DED and resting‐state coordinates, which are then used to generate a library of triggered states. Correlation analysis of the calculated DED features of each candidate vs observed DED permits quantitative evaluation of candidate structural quality.
Meng Iao Fong +3 more
wiley +1 more source
The Exposition of Fuzzy Decision Trees and their Application in Biology
This chapter employs the fuzzy decision tree classification technique in a series of biological based application problems. With its employment in a fuzzy environment, the results, in the form of fuzzy ‘if ..
Kirsty Park +3 more
core +1 more source
Small RNA pathways in mammalian oocytes
Three distinct small RNA pathways operate in mammalian oocytes: RNAi interference (RNAi), the microRNA (miRNA) pathway, and the PIWI‐associated RNA (piRNA) pathway. These pathways use small RNAs to guide sequence‐specific repression and contribute to oocyte biology by targeting genes and mobile elements or appear insignificant since different ...
Petr Svoboda, Josef Pasulka
wiley +1 more source
Aging Is a Key Driver for Adult Acute Myeloid Leukemia
Acute myeloid leukemia (AML) is a classical age‐related hematologic malignancy, and a key driver of AML is aging, which profoundly regulates intrinsic factors such as genomic instability, epigenetic reprogramming, and metabolic dysregulation, and alters bone marrow microenvironment.
Rong Yin, Haojian Zhang
wiley +1 more source
Deep learning systems, especially in critical fields like medicine, suffer from a significant drawback, their black box nature, which lacks mechanisms for explaining or interpreting their decisions.
Jose Sigut +4 more
doaj +1 more source
ABSTRACT Background and Objectives Multiple sclerosis (MS) exhibits racially disparate rates of disease progression. Black people with MS (B‐PwMS) experience a more severe disease course than non‐Hispanic White people with MS (NHW‐PwMS). Here we investigated structural and functional connectivity as well as structure–function decoupling in the ...
Emilio Cipriano +11 more
wiley +1 more source
MAPTree: Beating “Optimal” Decision Trees with Bayesian Decision Trees
Decision trees remain one of the most popular machine learning models today, largely due to their out-of-the-box performance and interpretability. In this work, we present a Bayesian approach to decision tree induction via maximum a posteriori inference of a posterior distribution over trees.
Colin Sullivan +2 more
openaire +2 more sources
A Two‐Stage Questionnaire and Actigraphy Screening for iRBD in a Multicenter Retrospective Cohort
ABSTRACT Objective Isolated rapid‐eye‐movement sleep behavior disorder is a prodromal marker of synucleinopathies. However, most cases remain undiagnosed due to the insufficient predictive value of questionnaires and limited access to confirmatory video‐polysomnography. We assessed a two‐stage screening strategy combining a brief questionnaire on rapid‐
Caleb A. Massimi +17 more
wiley +1 more source

