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Machine learning

PRmePRed: A protein arginine methylation prediction tool

Pawan Kumar, Joseph Joy, Ashutosh Pandey, Dinesh Gupta

Peculiar Genes Selection: A new features selection method to improve classification performances in imbalanced data sets

Federica Martina, Marco Beccuti, Gianfranco Balbo, Francesca Cordero

"What is relevant in a text document?": An interpretable machine learning approach

Leila Arras, Franziska Horn, [ ... ], Wojciech Samek

An analysis of the influence of deep neural network (DNN) topology in bottleneck feature based language recognition

Alicia Lozano-Diez, Ruben Zazo, Doroteo T. Toledano, Joaquin Gonzalez-Rodriguez

Prediction of N-linked glycosylation sites using position relative features and statistical moments

Muhammad Aizaz Akmal, Nouman Rasool, Yaser Daanial Khan

Analyzing the effectiveness of vocal features in early telediagnosis of Parkinson's disease

Betul Erdogdu Sakar, Gorkem Serbes, C. Okan Sakar

Multi-scale encoding of amino acid sequences for predicting protein interactions using gradient boosting decision tree

Chang Zhou, Hua Yu, [ ... ], Xiu-Jun Gong

A machine learning approach to estimation of downward solar radiation from satellite-derived data products: An application over a semi-arid ecosystem in the U.S.

Qingtao Zhou, Alejandro Flores, [ ... ], Bangshuai Han

Detecting representative data and generating synthetic samples to improve learning accuracy with imbalanced data sets

Der-Chiang Li, Susan C. Hu, Liang-Sian Lin, Chun-Wu Yeh

CRISPRpred: A flexible and efficient tool for sgRNAs on-target activity prediction in CRISPR/Cas9 systems

Md. Khaledur Rahman, M. Sohel Rahman

Adaptive Swarm Balancing Algorithms for rare-event prediction in imbalanced healthcare data

Jinyan Li, Lian-sheng Liu, [ ... ], Kelvin K. L. Wong

Preliminary research on the identification system for anthracnose and powdery mildew of sandalwood leaf based on image processing

Chunyan Wu, Xuefeng Wang

Predicting diabetes mellitus using SMOTE and ensemble machine learning approach: The Henry Ford ExercIse Testing (FIT) project

Manal Alghamdi, Mouaz Al-Mallah, [ ... ], Sherif Sakr

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