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Found 10 papers, 2 papers with code
Transition Matrix Representation of Trees with Transposed Convolutions
How can we effectively find the best structures in tree models?
Generalizing Tree Models for Improving Prediction Accuracy
In this work, we propose Decision Transformer Network (DTN), our highly accurate and interpretable tree model based on our generalized framework of tree models, decision transformers.
Fast and Accurate Pseudoinverse with Sparse Matrix Reordering and Incremental Approach
How can we compute the pseudoinverse of a sparse feature matrix efficiently and accurately for solving optimization problems?
SNeCT: Scalable network constrained Tucker decomposition for integrative multi-platform data analysis
SNeCT decomposition is applied to tensor constructed from large scale multi-platform multi-cohort cancer data, PanCan12, constrained on a network built from PathwayCommons database.
Feature versus Raw Sequence: Deep Learning Comparative Study on Predicting Pre-miRNA
Tested on a variant of six-layer convolution neural net and three-layer deep belief network, we find the raw sequence input based convolution neural network model performs similar or slightly better than feature based deep belief networks with best accuracy values of 0. 995 and 0. 990, respectively.
Prior Knowledge based mutation prioritization towards causal variant finding in rare disease
The performance of the structure-based features trained on 80\% of HumDiv and tested on 20\% of HumDiv and on ClinVar datasets showed high levels of discernibility in the mutation's pathogenic or benign effects: F score of 0. 71 and 0. 68 respectively using multi-layer perceptron.
CTD: Fast, Accurate, and Interpretable Method for Static and Dynamic Tensor Decompositions
Also, CTD-S is made 5 ~ 86x faster, and 7 ~ 12x more memory-efficient than the state-of-the-art method by removing redundancy.
Next Generation Business Intelligence and Analytics: A Survey
Business Intelligence and Analytics (BI&A) is the process of extracting and predicting business-critical insights from data.
Deep Neural Network Based Precursor microRNA Prediction on Eleven Species
We propose a deep learning based classification model, called DP-miRNA, for predicting precursor miRNA sequence that contains the miRNA sequence.
Multi-Kernel LS-SVM Based Bio-Clinical Data Integration: Applications to Ovarian Cancer
We were also able to predict clinical status with higher accuracy as compared to using individual data types.
