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bioimage-based-prediction-of-protein-subcellular-location-in-human-tissue-with-bp_neral_network

To prove the efficacy of SAE-RF method proposed, we need to compare the result with an equivalent neural network. So, we designed this bp_neral_network to verify our proposed metod.

bp_neral_network_for_bioimage_based_prediction

To prove the efficacy of SAE-RF method proposed, we need to compare the result with an equivalent neural network. So, we designed this bp_neral_network to verify our proposed metod.

caffe_cnn_for_bioimage_based_prediction

we designed this end-to-end CNN code in CAFFE to compare our proposed metod.

dc-rf-rus-pf-ppi-code

Prediction of Protein-Protein Interaction Sites with Machine Learning

ibd_uc_week-4-renission

lstm-cox-code

The source code is an implementation of our method described in the paper "Survival Analysis of Breast Cancer Utilizing Integrated Features with Ordinal Cox Model and Auxiliary Loss".

ml_ordcox

The source code is an implementation of our method described in the paper "Isabelle Bichindaritz, Guanghui Liu, and Christopher Bartlett. Integrative Survival Analysis of Breast Cancer with from Gene Expression and DNA Methylation Data".

ml_ordcox_1

mt-adscca

protect-data

protect-data_ibd

react1

sae-rf-code

The source code is an implementation of our method described in the paper "Bioimage-based Prediction of Protein Subcellular Location in Human Tissue with Ensemble Features and Deep Networks".

sae-rf-code-data

the collection of 2,413 IHC bioimages, containing 21 proteins related to 46 normal human tissues, generated from the HPA served as our benchmark dataset.