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BioSeq-Diabolo

biological sequence similarity anal 8000 ysing using Diabolo.

Software Requirements:

BioSeq-Diabolo has been tested on Windows, Ubuntu 16.04, and 18.04 operating systems.

Installation

virtualenv

virtualenv -p python3.7 venv

source ./venv/bin/activate

# You can use 'requirements' file:
pip install -r requirements.txt
# or directly install the corresponding package:
pip install matchzoo-py
pip install scikit-learn
pip install lightgbm
pip install seaborn

Anaconda

conda create -n venv python=3.7

conda activate venv

# You can use 'requirements' file:
pip install -r requirements.txt
# or directly install the corresponding package:
pip install matchzoo-py
pip install scikit-learn
pip install lightgbm
pip install seaborn

Command

Demo of web server

python sesica_clf.py -base_dir ../data/web_hetero -data_type hetero -bmk_vec_a ../data/web_hetero/bmk_vec_a.txt -bmk_vec_b ../data/web_hetero/bmk_vec_b.txt -bmk_label ../data/web_hetero/bmk_pos_label.txt ../data/web_hetero/bmk_neg_label.txt -ind score -ind_vec_a ../data/web_hetero/ind_vec_a.txt -clf svm rf ert knn mnb gbdt goss dart mlp -metric aupr

python sesica_rank.py -base_dir ../data/web_hetero -rank ltr -clf svm rf ert knn mnb gbdt goss dart mlp -rank ltr -metric aupr

python sesica_plot.py -base_dir ../data/web_hetero -data_type hetero -clf svm rf ert knn mnb gbdt goss dart mlp -plot pie net roc prc box dist dr hp

Identification of circRNA-disease associations

python sesica_clf.py -base_dir ../data/iCircDA -data_type hetero -bmk_vec_a ../data/iCircDA/bmk_circRNA.txt -bmk_vec_b ../data/iCircDA/bmk_disease.txt -bmk_label ../data/iCircDA/benchmark_pos.txt ../data/iCircDA/benchmark_neg.txt -clf svm rf ert knn mnb gbdt goss mlp -metric auc -gs_mode 2

python sesica_rank.py -base_dir ../data/iCircDA -rank ltr -clf svm rf knn mnb goss -metric auc -gs_mode 2

python sesica_plot.py -base_dir ../data/iCircDA -data_type hetero -clf svm rf knn mnb goss -rank ltr -plot roc polar hp dr pie -plot_set test

Protein remote homology detection

python sesica_clf.py -base_dir ../data/ProtRe -data_type homo -bmk_vec ../data/ProtRe/bmk_vec.txt -bmk_label ../data/ProtRe/pos_label.txt ../data/ProtRe/neg_label.txt -clf svm rf ert knn gbdt goss dart mlp -metric roc@1 -gs_mode 2

python sesica_rank.py -base_dir ../data/ProtRe -rank ltr -clf svm rf ert knn mlp -metric roc@1 -gs_mode 2

python sesica_plot.py -base_dir ../data/ProtRe -data_type homo -clf svm rf ert knn mlp -rank ltr -plot roc prc box polar hp dr dist pie bar -plot_set test

Protein function annotation

python sesica_clf.py -base_dir ../data/go -data_type hetero -bmk_vec_a ../data/go/cc_bmk_vec_a.txt -bmk_vec_b ../data/go/cc_bmk_vec_b.txt -bmk_label ../data/go/pos_label.txt ../data/go/neg_label.txt -clf svm rf ert knn mnb gbdt goss dart mlp -metric aupr -gs_mode 2

python sesica_rank.py -base_dir ../data/go -rank ltr -clf svm rf ert knn mlp -metric aupr -gs_mode 2

python sesica_plot.py -base_dir ../data/go -data_type homo -clf svm rf ert knn mlp -rank ltr -plot polar dr dist pie bar -plot_set test

Deep-Learning Model Reference

Model Reference
ARC-I Convolutional Neural Network Architectures for Matching Natural Language Sentences
ARC-II Learning Deep Structured Semantic Models for Web Search using Clickthrough Data
CDSSM Learning Semantic Representations Using Convolutional Neural Networks for Web Search
DRMM A Deep Relevance Matching Model for Ad-hoc Retrieval
DRMMTKS A Deep Top-K Relevance Matching Model for Ad-hoc Retrieval.
MatchLSTM Machine Comprehension Using Match-LSTM and Answer Pointer
DUET Learning to Match Using Local and Distributed Representations of Text for Web Search
KNRM End-to-End Neural Ad-hoc Ranking with Kernel Pooling
ConvKNRM Convolutional neural networks for soft-matching n-grams in ad-hoc search
ESIM Enhanced LSTM for Natural Language Inference
BiMPM Bilateral Multi-Perspective Matching for Natural Language Sentences
MatchPyramid Text Matching as Image Recognition
Match-SRNN Match-SRNN: Modeling the Recursive Matching Structure with Spatial RNN
aNMM aNMM: Ranking Short Answer Texts with Attention-Based Neural Matching Model
MV-LSTM A Deep Architecture for Semantic Matching with Multiple Positional Sentence Representations
DIIN Natural Lanuguage Inference Over Interaction Space
HBMP Sentence Embeddings in NLI with Iterative Refinement Encoders

Code reference

deep-learning semantic similarity calculation reference

LTR part code reference

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