Results 1 to 10 of about 6,150 (177)

Ovarian Cancer Diagnosis and Chemoresistance Prediction Model Based on cfRNA Molecular Signature

open access: yesAdvanced Science, EarlyView.
A deep learning model analyzes cfRNA profiles extracted from the blood of OVCA patients. This innovative approach distinguishes OVCA from healthy controls with high accuracy. Crucially, it reliably predicts patient response to chemotherapy (sensitive versus resistant subgroups).
Qinhao Guo   +14 more
wiley   +1 more source

Global discriminative learning for higher-accuracy computational gene prediction. [PDF]

open access: yesPLoS Computational Biology, 2007
Most ab initio gene predictors use a probabilistic sequence model, typically a hidden Markov model, to combine separately trained models of genomic signals and content.
Axel Bernal   +3 more
doaj   +1 more source

Building phenotypic character matrices for phylogenetic inference: exploration of 35 years of practice

open access: yesBiological Reviews, EarlyView.
ABSTRACT Recent methodological development in phylogenetic inference has focused predominantly on molecular data. However, renewed interest in other data types, particularly morphological data, has followed from the increased recognition of the power of total evidence and tip‐dating approaches, including fossil data, for inference of time‐scaled trees ...
Melanie J. Hopkins   +9 more
wiley   +1 more source

Antarctic soil microbiomes encode structurally conserved and phylogenetically diverse beta‐lactamases

open access: yesiMetaOmics, EarlyView.
An integrative metagenomic framework combining sequence, structural, and functional inference reveals a phylogenetically diverse and structurally conserved repertoire of putative beta‐lactamases across Antarctic soil microbiomes, with predominance of class A and subclass B3 enzymes and limited but detectable associations with mobile genetic elements ...
José Coche‐Miranda   +8 more
wiley   +1 more source

The Hierarchical Dirichlet Process Hidden Semi-Markov Model

open access: yesCoRR, 2010
There is much interest in the Hierarchical Dirichlet Process Hidden Markov Model (HDP-HMM) as a natural Bayesian nonparametric extension of the traditional HMM. However, in many settings the HDP-HMM's strict Markovian constraints are undesirable, particularly if we wish to learn or encode non-geometric state durations.
Johnson, Matthew James, Willsky, Alan S
openaire   +4 more sources

Extending the hyper‐logistic model to the random setting: New theoretical results with real‐world applications

open access: yesMathematical Methods in the Applied Sciences, EarlyView.
We develop a full randomization of the classical hyper‐logistic growth model by obtaining closed‐form expressions for relevant quantities of interest, such as the first probability density function of its solution, the time until a given fixed population is reached, and the population at the inflection point.
Juan Carlos Cortés   +2 more
wiley   +1 more source

Network Latency Estimation for Telesurgery Using Deep Reinforcement Learning

open access: yesSmartBot, EarlyView.
Overview of the proposed two‐stage deep reinforcement learning framework for network latency prediction in telesurgery. The pipeline includes data collection from simulated catheter navigation sessions (Philippines–Botswana), feature engineering, DQN‐based direction prediction (85.8% accuracy), direction‐to‐value transformation, and value forecasting ...
Bakang Kgopolo   +2 more
wiley   +1 more source

Performance and Reliability Analysis of Prioritized Safety Messages Broadcasting in DSRC With Hidden Terminals

open access: yesIEEE Access, 2020
In this paper, we design a mathematical model for performance and reliability evaluation of the IEEE 802.11p Enhanced Distributed Channel Access (EDCA) broadcast scheme in Dedicated Short-Range Communication (DSRC) with the presence of hidden terminals ...
Lin Hu, Zhijian Dai
doaj   +1 more source

DQN‐Guided Subset‐Induced OCSVM Kernel Approximation for Imbalanced Anomaly Detection

open access: yesIEEJ Transactions on Electrical and Electronic Engineering, EarlyView.
Anomaly detection under limited normal data remains a fundamental challenge due to severe class imbalance and scarcity of anomalies. We propose a novel framework that reformulates support vector selection in One‐Class SVM as a sequential decision‐making problem.
Wenqian Yu, Jiaying Wu, Jinglu Hu
wiley   +1 more source

Learning Evolutionary Stages with Hidden Semi-Markov Model for Predicting Social Unrest Events

open access: yesDiscrete Dynamics in Nature and Society, 2020
Social unrest events are common happenings in modern society which need to be proactively handled. An effective method is to continuously assess the risk of upcoming social unrest events and predict the likelihood of these events. Our previous work built
Fengcai Qiao, Xin Zhang, Jinsheng Deng
doaj   +1 more source

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