DeepPred-SubMito: A Novel Submitochondrial Localization Predictor Based on Multi-Channel Convolutional Neural Network and Dataset Balancing Treatment

SCLpred-EMS: subcellular localization prediction of endomembrane system and secretory pathway proteins by Deep N-to-1 Convolutional Neural Networks

(2020) Manaz Kaleel et al.   BIOINFORMATICS

A Novel Protein Subcellular Localization Method With CNN-XGBoost Model for Alzheimer's Disease

(2019) Long Pang et al.   Frontiers in Genetics

DeepMito: accurate prediction of protein sub-mitochondrial localization using convolutional neural networks

(2019) Castrense Savojardo et al.   BIOINFORMATICS

SubMito-XGBoost: predicting protein submitochondrial localization by fusing multiple feature information and eXtreme gradient boosting

(2019) Bin Yu et al.   BIOINFORMATICS

The lncLocator: a subcellular localization predictor for long non-coding RNAs based on a stacked ensemble classifier

(2018) Zhen Cao et al.   BIOINFORMATICS

Predicting RNA–protein binding sites and motifs through combining local and global deep convolutional neural networks

(2018) Xiaoyong Pan et al.   BIOINFORMATICS

Prediction of RNA-protein sequence and structure binding preferences using deep convolutional and recurrent neural networks

(2018) Xiaoyong Pan et al.   BMC GENOMICS

Prediction of protein subcellular localization with oversampling approach and Chou's general PseAAC

(2018) Shengli Zhang et al.   JOURNAL OF THEORETICAL BIOLOGY

Predicting protein submitochondrial locations by incorporating the pseudo-position specific scoring matrix into the general Chou's pseudo-amino acid composition

(2018) Wenying Qiu et al.   JOURNAL OF THEORETICAL BIOLOGY

A systematic study of the class imbalance problem in convolutional neural networks

(2018) Mateusz Buda et al.   NEURAL NETWORKS

Learning from class-imbalanced data: Review of methods and applications

(2017) Guo Haixiang et al.   EXPERT SYSTEMS WITH APPLICATIONS

Proteome-wide prediction and annotation of mitochondrial and sub-mitochondrial proteins by incorporating domain information

(2017) Ravindra Kumar et al.   MITOCHONDRION

Convolutional neural network architectures for predicting DNA–protein binding

(2016) Haoyang Zeng et al.   BIOINFORMATICS

Deep learning in bioinformatics

(2016) Seonwoo Min et al.   BRIEFINGS IN BIOINFORMATICS

Deep learning for computational biology

(2016) Christof Angermueller et al.   Molecular Systems Biology

iOri-Human: identify human origin of replication by incorporating dinucleotide physicochemical properties into pseudo nucleotide composition

(2016) Chang-Jian Zhang et al.   Oncotarget

iRNA-AI: identifying the adenosine to inosine editing sites in RNA sequences

(2016) Wei Chen et al.   Oncotarget

Geometric mean based boosting algorithm with over-sampling to resolve data imbalance problem for bankruptcy prediction

(2015) Myoung-Jong Kim et al.   EXPERT SYSTEMS WITH APPLICATIONS

Predicting the sequence specificities of DNA- and RNA-binding proteins by deep learning

(2015) Babak Alipanahi et al.   NATURE BIOTECHNOLOGY

Using Over-Represented Tetrapeptides to Predict Protein Submitochondria Locations

(2013) Hao Lin et al.   ACTA BIOTHEORETICA

SubMito-PSPCP: Predicting Protein Submitochondrial Locations by Hybridizing Positional Specific Physicochemical Properties with Pseudoamino Acid Compositions

(2013) Pufeng Du et al.   Biomed Research International

Predicting plant protein subcellular multi-localization by Chou's PseAAC formulation based multi-label homolog knowledge transfer learning

(2012) Suyu Mei   JOURNAL OF THEORETICAL BIOLOGY

Identify submitochondria and subchloroplast locations with pseudo amino acid composition: Approach from the strategy of discrete wavelet transform feature extraction

(2011) Shao-Ping Shi et al.   BIOCHIMICA ET BIOPHYSICA ACTA-MOLECULAR CELL RESEARCH

An intimate liaison: spatial organization of the endoplasmic reticulum–mitochondria relationship

(2010) Olga Martins de Brito et al.   EMBO JOURNAL

Targeting mitochondria for cancer therapy

(2010) Simone Fulda et al.   NATURE REVIEWS DRUG DISCOVERY