Developed
Software/Research Tools
·
[Apr/06/2024] Application of Deep Learning
for Segmenting Seepages in Levee Systems
o Click
here for the data.
o
·
IterLUNet: [Sep/15/2022]
o
IterLUNet: Deep Learning Architecture for Pixel-Wise
Crack Detection in Levee Systems, see Technical Paper: Tech-report
(TR-2022/3)
·
TAFPred: Backbone [Mar/27/2022]
o TAFPred: Backbone Torsion Angle Fluctuations Prediction
from Protein Sequences. See Technical
Paper: Tech-report/Preprint
o Click here for the tool (code & data)
·
Dispredict3.0 [Mar/12/2022]:
o
Dispredict3.0: Prediction of Intrinsically
Disordered Proteins with Protein Language Model, see Technical
Paper: Tech-report
(TR-2022/1)
o Click here for the tool (code & data)
·
Accurate Gene Regulatory Network (AGRN) [Dec/31/2021]:
o
AGRN: Accurate Gene Regulatory Network Inference
using Collective Machine Learning Methods
o
Click here for the tool (code
& data) [Compressed size: 29.3 MB]
·
Rogue Wave Prediction [Aug/30/2020]:
o
An Ensemble-based Prediction of Rogue Waves in
Oceanic Waters.
o Click
here for the tool
[Compressed size: about 8 GB]
·
PCa-Clf [May/01/2020]:
o
A Classifier of Prostate Cancer Patients into
Indolent and Aggressive Using Machine Learning.
o Click
here for the tool
[Compressed size: about 46 MB]
·
Liz_Tool [Mar/01/2020]
o A Machine-Learning tool to Predict the
Multivariate Performance Phenotype Elements.
o Click
here for the tool
·
ClassifyTE [July/02/2019]
o Machine Learning based Prediction of
Hierarchical Classification of Transposable Elements [KDD paper]
o Click
here for code
and data
·
diSBPred [July/19/2019]
o A Machine Learning-based Approach for Disulfide
Bond Prediction
o Click
here for code
and data
·
AIBH [April/27/2019]
o Accurate Identification of Brain Hemorrhage
using Genetic Algorithm based Feature Selection and Stacking
o Click
here for code
and data
·
AIRBP [Mar/03/2019]
o Accurate Identification of RNA-binding
Proteins Using Machine Learning Techniques from sequences
o Click
here for code
and data
o Online
server <Coming Soon>
o Technical
Paper #1, “AIRBP: Accurate
Identification of RNA-binding Proteins Using Machine Learning Techniques from
sequences,” Tech-report
(TR-2019/1).
·
StackCBPred [Nov/14/2018]
o A Stacking based Prediction of
Protein-Carbohydrate Binding Sites from Sequence (Readme)
o Click
here for code and data
o Online
server here
o Technical
Paper #1, “StackCBPred: A
Stacking based Prediction of Protein-Carbohydrate Binding Sites from Sequence,”
Tech-report
(TR-2018/3).
·
StackSSSPred [July/01/2018]:
o StackSSSPred: A Stacking based Prediction of
Supersecondary Structure from Sequence
o Benchmark
data for supersecondary structure prediction only
from sequence, is available here.
·
StackDPPred [Mar/15/2018]
o A Stacking based Prediction of DNA-binding
Proteins from Sequences (Readme)
o Click
here for code
and data
o Online
server here
o Technical
Paper #1, “StackDPPred: A Stacking based Prediction of DNA-binding Protein from
Sequence,” Tech-report
(TR-2018/2).
·
PBRpredict-Suite
o A
Peptide-Binding Residue Predictor-Suite (Readme)
o Click
here for code
and data
·
PBRpredict
o A
Peptide-Binding Residue Predictor
o Click
here for code
and data
·
BIRpredict
o Predicts Binding Inducing Regions of Receptor
Proteins in a Complex
o Click here for data.
·
PCaAnalyser:
o A
2D-Image Analysis Based Module for Effective Determination of Prostate Cancer
Progression in 3D Culture (publication)
o Click
here for code and
data.
· DisPredict:
o Disorder
Protein Predictor. Click here for code
and data.
o Tech-report
(TR-2014/1), published in PLOS One in 2015.
o CASP11’s
abstract.
· DisPredict2:
o Disorder
Protein Predictor version #2, includes PSEE: Position
Specific Estimated Energy.
o Corresponding
article, “Estimation of Free Energy Contribution of Protein Residues as Feature
for Structure Prediction from Sequence”, is coming soon.
·
3DIGARS:
o Energy
Function. Click here for code and data.
o Tech-report
(TR-2014/2), Accepted in Current Bioinformatics, Bentham Journal in
2015, for publication.
o Update
version 2: 3DIGARS-2.0. Click here for code, see:
publication.
o Update
version 3.0: 3DIGARS-3.0. Click here for code.
§ sDFIRE:
·
Sequence-specific
statistical energy function for protein structure prediction by decoy
selections (published)
·
For
the software, DFIRE, dDFIRE and SPINE-X are needed (can be found here: http://sparks-lab.org/index.php/Main/Downloads).
·
REGAd3p:
o A
real value predictor of Accessible Surface Area (ASA) by Regularized Exact
regression, with
optimization by Genetic Algorithm and using polynomial
kernel of degree 3. (Layman’s
summary)
o Click
here for code
and data.
o Tech-report
(TR-2015/1), see: publication.
·
MetaSSPred:
o
A Balanced Secondary Structure Predictor (Layman’s
summary)
o Click
here for code
and data.
o Tech-report
(TR-2015/2), see publication.
·
GAPlus:
o “An Enhanced Genetic Algorithm for Ab initio Protein Structure Prediction”, DOI: 10.1109/TEVC.2015.2505317.
o Click
here for the code.
·
MH_GA:
o “Guided
macro-mutation in a graded energy based genetic algorithm for protein structure
prediction”, in Computational Biology and Chemistry, Elsevier (Accepted).
o Click for the code.
·
Sampling:
o Article #1: Genetic
Algorithm based Improved Sampling for Protein Structure Prediction, Accepted in International Journal of
Bio-Inspired Computation, Tech-report
(TR-2015/4). Click for the code.
o Article
#2: hGRGA: A Scalable Genetic Algorithm using Homologous Gene Schema Replacement,
Tech-report
(TR-2016/1). Click for the code.
o Article
#3: AMLGA: A Genetic Algorithm with Adaptive and Memory-Assisted Local
Operators, Tech-report
(TR-2016/2). Click for the code.
·
Ab initio Protein Structure
Predictor
o Version
1: Click for the code.
o Version
2: Click for the code.
o Technical-Paper
#1: 3DIGARS-PSP: A Novel Statistical Energy Function and Effective
Conformational Search Strategy based ab initio Protein Structure Prediction, Tech-report
(TR-2018/1).
·
Behavior Predictor, DVB
o A Machine Learning Approach to Determine
Oyster Vessel Behavior, given tracking data. Click for code and data.
·
NASA’s Patent
Classifier Software,
Data is available here
·
NASA’s Software
Category: BaseLine Data
·
APAS: APAS_tool
·
Catalog SW web with
Tag inserted: here
·
NASA’s
Software+Patent Classifier: here,
Demo Video is available here,
·
NASA Risk Prediction:
Final Product, here
·
Other to NMIS here
·
DataSets
o Angle-fluctuation
datasets here.
·
Detection of rip
current from images
o Code
o Dataset
o Technical Report: “Machine Learning Applications in Detecting Rip Currents from Images,”
Tech-report
(TR-2018/3).
·
Detection
of Levee Crack from Images
·
Detection
of Sand-Boil
·
Rogue
Wave Detection
o Code