Results 61 to 70 of about 1,084,163 (283)
Mapping the evolution of mitochondrial complex I through structural variation
FEBS Letters, EarlyView.Respiratory complex I (CI) is crucial for bioenergetic metabolism in many prokaryotes and eukaryotes. It is composed of a conserved set of core subunits and additional accessory subunits that vary depending on the organism. Here, we categorize CI subunits from available structures to map the evolution of CI across eukaryotes. Respiratory complex I (CI)
Dong‐Woo Shin +2 more
wiley +1 more source
GENESIM : genetic extraction of a single, interpretable model [PDF]
, 2016 Models obtained by decision tree induction techniques excel in being interpretable.However, they can be prone to overfitting, which results in a low predictive performance. Ensemble techniques are able to achieve a higher accuracy. However, this comes at
De Turck, Filip +4 more
core +2 more sources
Decision Tree Algorithm Considering Distances Between Classes
IEEE Access, 2022 Decision tree algorithm (DT) is a commonly used data mining method for classification and regression. DT repeatedly divides a dataset into pure subsets based on impurity measurements such as entropy and Gini.
Sangyong Lee +3 more
doaj +1 more source
Plasmodium falciparum gametogenesis essential protein 1 (GEP1) is a transmission‐blocking target
FEBS Letters, EarlyView.This study shows Plasmodium falciparum GEP1 is vital for activating sexual stages of malarial parasites even independently of a mosquito factor. Knockout parasites completely fail gamete formation even when a phosphodiesterase inhibitor is added. Two single‐nucleotide polymorphisms (V241L and S263P) are found in 12%–20% of field samples.
Frederik Huppertz +5 more
wiley +1 more source
Binary Decision Diagrams: from Tree Compaction to Sampling
, 2020 Any Boolean function corresponds with a complete full binary decision tree. This tree can in turn be represented in a maximally compact form as a direct acyclic graph where common subtrees are factored and shared, keeping only one copy of each unique ...
A Genitrini +9 more
core +1 more source
Engineering and Applied Science Research, 2022 Decision Trees are a common approach used for classifying unseen data into defined classes. The Information Gain is usually applied as splitting criteria in the node selection process for constructing the decision tree.
Sirichanya Chanmee, Kraisak Kesorn
doaj
Molecular Oncology, EarlyView.This study investigates gene expression differences between two major pediatric acute lymphoblastic leukemia (ALL) subtypes, B‐cell precursor ALL, and T‐cell ALL, using a data‐driven approach consisting of biostatistics and machine learning methods. Following analysis of a discovery dataset, we find a set of 14 expression markers differentiating the ...
Mona Nourbakhsh +8 more
wiley +1 more source
A Chi-MIC Based Adaptive Multi-Branch Decision Tree
IEEE Access, 2021 Since the decision trees (DTs) have an advantage over “black-box” models, such as neural nets or support vector machines, in terms of comprehensibility, such that it might merit improvement for further optimization.
Jiahao Ye +6 more
doaj +1 more source
Molecular Oncology, EarlyView.This study presents a novel AI‐based diagnostic approach—comprehensive serum glycopeptide spectra analysis (CSGSA)—that integrates tumor markers and enriched glycopeptides from serum. Using a neural network model, this method accurately distinguishes liver and pancreatic cancers from healthy individuals.
Motoyuki Kohjima +6 more
wiley +1 more source
Decision tree analysis for prostate cancer prediction [PDF]
Srpski Arhiv za Celokupno Lekarstvo, 2019 Introduction/Objective. The use of serum prostate-specific antigen (PSA) test has dramatically increased the number of men undergoing prostate biopsy. However, the best possible strategies for selecting appropriate patients for prostate biopsy have yet ...
Stojadinović Miroslav M. +2 more
doaj +1 more source