NetInfer @ LMMD

NetInfer is a command-line toolkit written in C++ programming language, which can be used for predicting potential targets for approved drugs, drug candidates failed in clinical trials, and new chemical entities. It is light-weight and does not need the support of any third-party math libraries such as linear algebra libraries. To accelerate the calculation and decrease the cost of memory space, different data structures were designed for sparse and dense matrices. NetInfer provides various functions, such as link prediction, 10-fold cross validation, leave-one-cross validation, and external validation. In a uniform platform, researchers can input their in-house data and then obtain predictive lists or evaluation indicators. Several our previous methods were implemented in this toolkit, including:
  • Network-Based Inference (NBI)
  • Substructure-Drug-Target Network-Based Inference (SDTNBI)
  • Balanced Substructure-Drug-Target Network-Based Inference (bSDTNBI)

The latest versions are available upon request at now:

Updated date Platform Comment
2017-2-1964-bit LinuxCompiled by GCC 5.3.0 on CentOS 5.8
Input file format:
Tab-delimited text files can be used as input files for our toolkit. Each line in a input file represents one linkage between two nodes A and B, containing 5 parts:
  1. the type of node A (e.g. DRUG or COMPOUND)
  2. the identifier of node A (e.g. DB00945)
  3. the type of node B (e.g. TARGET)
  4. the identifier of node B (e.g. P23219)
  5. the weighted value of the edge between A and B (e.g. 1.0)
References (#Co-first authors, *Corresponding authors):
  1. Wu ZR, Cheng FX*, Li J, Li WH, Liu GX, Tang Y*. SDTNBI: an integrated network and chemoinformatics tool for systematic prediction of drug-target interactions and drug repositioning. Brief Bioinform, 2017, 18(2): 333-347. DOI: 10.1093/bib/bbw012.
  2. Wu ZR#, Lu WQ#, Wu D, Luo AQ, Bian HP, Li J, Li WH, Liu GX, Huang J*, Cheng FX*, Tang Y*. In silico prediction of chemical mechanism of action via an improved network-based inference method. Br J Pharmacol, 2016, 173(23): 3372-3385. DOI: 10.1111/bph.13629.
  3. Fang JS#, Wu ZR#, Cai CP, Wang Q, Tang Y*, Cheng FX*. Quantitative and systems pharmacology. 1. In silico prediction of drug–target interactions of natural products enables new targeted cancer therapy. J Chem Inf Model, 2017, DOI: 10.1021/acs.jcim.7b00216.
  4. Wu ZR#, Lu WQ#, Yu WW, Wang TDY, Li WH, Liu GX, Zhang HK, Pang XF, Huang J, Liu MY*, Cheng FX*, Tang Y*. Quantitative and systems pharmacology 2. In silico polypharmacology of G protein-coupled receptor ligands via network-based approaches. Accepted.


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Page last updated at 2017-11-08 09:11:36 (Asia/Shanghai)

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