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Purine: A bi-graph based deep learning framework [article]

Min Lin, Shuo Li, Xuan Luo, Shuicheng Yan
2015 arXiv   pre-print
In this paper, we introduce a novel deep learning framework, termed Purine.  ...  In Purine, a deep network is expressed as a bipartite graph (bi-graph), which is composed of interconnected operators and data tensors.  ...  To facilitate the implementation of various parallelization schemes, we built a bigraph-based deep learning framework called "Purine".  ... 
arXiv:1412.6249v5 fatcat:r7u2jdnnkzeabcsqahilie7gqi

Deep Neural Network-Assisted Drug Recommendation Systems for Identifying Potential Drug–Target Interactions

Yogesh Kalakoti, Shashank Yadav, Durai Sundar
2022 ACS Omega  
Here, we present a machine learning-based multiclass classification workflow that segregates interactions between active, inactive, and intermediate drug-target pairs.  ...  External validation results showed that models based on att-biLSTM and gCNN could help predict novel DTIs.  ...  to feed into a compatible ML framework.  ... 
doi:10.1021/acsomega.2c00424 pmid:35449922 pmcid:PMC9016825 fatcat:4s46dftmnndrrm4inydlaghgh4

m6AGE: A Predictor for N6-Methyladenosine Sites Identification Utilizing Sequence Characteristics and Graph Embedding-Based Geometrical Information

Yan Wang, Rui Guo, Lan Huang, Sen Yang, Xuemei Hu, Kai He
2021 Frontiers in Genetics  
In this study, we propose a predictor called m6AGE which utilizes sequence-derived and graph embedding features.  ...  To the best of our knowledge, our predictor is the first to combine sequence-derived features and graph embeddings for m6A site prediction.  ...  DeepM6ASeq develops a deep learning framework and uses one-hot encoding for the identification of m 6 A sites.  ... 
doi:10.3389/fgene.2021.670852 pmid:34122525 pmcid:PMC8191635 fatcat:6oeqvv5aujdrzdgsrrpd7u35ay

Towards Structured Prediction in Bioinformatics with Deep Learning [article]

Yu Li
2020 arXiv   pre-print
Using machine learning, especially deep learning, to facilitate biological research is a fascinating research direction.  ...  Firstly, we can combine deep learning with other classic algorithms, such as probabilistic graphical models, which model the problem structure explicitly.  ...  deduced in a pure structure-based computational framework.  ... 
arXiv:2008.11546v1 fatcat:5in2a642b5cj3lweuynl7sniaa

Advances in Flux Balance Analysis by Integrating Machine Learning and Mechanism-based Models

Ankur Sahu, Mary-Ann Blätke, Jędrzej Jakub Szymański, Nadine Töpfer
2021 Computational and Structural Biotechnology Journal  
This review article provides an overview of integrative studies that combine flux balance analysis with machine learning approaches, kinetic models, such as physiology-based pharmacokinetic models, and  ...  Due to its linear nature, this optimization framework is readily scalable to multi-tissue or -organ and even multi-organism models.  ...  Integrating Deep Learning with Flux Balance Analysis Deep Learning is a branch of ML which is based on Artificial Neural Networks.  ... 
doi:10.1016/j.csbj.2021.08.004 pmid:34471504 pmcid:PMC8382995 fatcat:q5zaa3lczfesznnegt3owrm4ya

The History Began from AlexNet: A Comprehensive Survey on Deep Learning Approaches [article]

Md Zahangir Alom, Tarek M. Taha, Christopher Yakopcic, Stefan Westberg, Paheding Sidike, Mst Shamima Nasrin, Brian C Van Esesn, Abdul A S. Awwal, Vijayan K. Asari
2018 arXiv   pre-print
There are some surveys have published on Deep Learning in Neural Networks [1, 38] and a survey on RL [234].  ...  We have also comprised recently developed frameworks, SDKs, and benchmark datasets that are used for implementing and evaluating deep learning approaches.  ...  Doctoral research scientist on deep Learning, computer vision for remote sensing and hyper spectral imaging (e-mail: pehedings@slu.edu). Brian C Van Esesn 3 and Abdul A S.  ... 
arXiv:1803.01164v2 fatcat:eo353y77tvckbdjcfexpaadeh4

Machine Learning and Rule Mining Techniques in the Study of Gene Inactivation and RNA Interference [chapter]

Saurav Mallik, Ujjwal Maulik, Namrata Tomar, Tapas Bhadra, Anirban Mukhopadhyay, Ayan Mukherji
2019 Modulating Gene Expression - Abridging the RNAi and CRISPR-Cas9 Technologies [Working Title]  
In this book chapter, we provided a comprehensive review of various machine learning and association rule mining algorithms developed to handle different biological problems such as detection of gene signature  ...  We also provided a comparative study of different well-known classifiers along with other used methods.  ...  [56] proposed a deep learning based methodology to integrate multi-omics data and robustly perform survival study on hepatocellular carcinoma.  ... 
doi:10.5772/intechopen.83470 fatcat:dqrgeewornfjlhy7wwmzteh2ry

Deep semi-supervised learning ensemble framework for classifying co-mentions of human proteins and phenotypes

Morteza Pourreza Shahri, Indika Kahanda
2021 BMC Bioinformatics  
Conclusions This article presents a novel approach for human protein-phenotype co-mention classification based on deep, semi-supervised, and ensemble learning.  ...  Results In this study, we propose a novel deep semi-supervised ensemble framework that combines deep neural networks, semi-supervised, and ensemble learning for classifying human protein-phenotype co-mentions  ...  Methods Approach Our proposed framework is a combination of semi-supervised learning, deep learning, and ensemble learning. Figure 5 depicts the proposed framework.  ... 
doi:10.1186/s12859-021-04421-z pmid:34656098 fatcat:yr2nylu6uvf43ff4mms535arjq

Identifying large scale interaction atlases using probabilistic graphs and external knowledge

Sree K. Chanumolu, Hasan H. Otu
2022 Journal of Clinical and Translational Science  
Reconstruction of gene interaction networks from experimental data provides a deep understanding of the underlying biological mechanisms.  ...  We propose a divide-and-conquer approach using probabilistic graph representations and external knowledge.  ...  In this paper, we propose a method that uses a diverse set of knowledge bases to infer interaction between two genes based on a stochastic, automated framework.  ... 
doi:10.1017/cts.2022.18 pmid:35321220 pmcid:PMC8922291 fatcat:yoyjbq235veflm2dvwdscx3tfu

A high throughput molecular screening for organic electronics via machine learning: present status and perspective

Akinori SAEKI, Kranthiraja Kakaraparthi
2019 Japanese Journal of Applied Physics  
Machine learning (ML), based on the rapidly growing field of artificial intelligence technology, offers high throughput material exploration that is more efficient than high-cost quantum chemical calculations  ...  This review describes the present status and perspective of ML-based development (materials informatics) of organic electronics.  ...  (DNN) regression models based on graph-and geometry-based descriptors [ Fig. 4(a) ].  ... 
doi:10.7567/1347-4065/ab4f39 fatcat:lkmmjv67dfhhhib7zglwqihapy

Graph ranking for exploratory gene data analysis

Cuilan Gao, Xin Dang, Yixin Chen, Dawn Wilkins
2009 BMC Bioinformatics  
We propose a general framework for gene ranking. We construct a bipartite graph from the Gene Ontology (GO) and gene expression data.  ...  The relevance of genes is described in the graph (through a common function). The proposed method provides an exploratory framework for gene data analysis.  ...  single framework.  ... 
doi:10.1186/1471-2105-10-s11-s19 pmid:19811684 pmcid:PMC3226190 fatcat:jdmeuxzjkbafxo3gdc2hiy4sou

The Comparison of Active Cooperative and Traditional Teaching Methods in Nanochemistry Students' Satisfaction and Learning of Clinical Nanochemistry

Alireza Heidari1, 2*, Ricardo Gobato
2020 Zenodo  
Its deep learning and understanding could be an important foundation for Nanochemistry students' expertise.  ...  The Nanochemistry students' satisfactory score of cooperative method was calculated by a questionnaire.  ...  methods such as Nanochemistry student-centered methods such as problem-based learning, case-based learning and inquiry-based education can play a significant role in improving factors such as deep and  ... 
doi:10.5281/zenodo.3901197 fatcat:csmhivekgvc77nuf2oslrnqo4u

Building trees of algae: some advances in phylogenetic and evolutionary analysis

Heroen Verbruggen, Edward C. Theriot
2008 European journal of phycology  
The focus of the paper is on model-based techniques.  ...  For each of these topics, we provide a brief circumscription, refer to the more specialized literature, and list a selection of software to carry out the analyses.  ...  The base frequencies graph shows marked differences in base composition among codon positions (cp1, cp2, cp3), with a strong AT bias at third codon positions.  ... 
doi:10.1080/09670260802207530 fatcat:3msu5po4hrgbpffvsumm7qtgla

Common Features in lncRNA Annotation and Classification: A Survey

Christopher Klapproth, Rituparno Sen, Peter F. Stadler, Sven Findeiß, Jörg Fallmann
2021 Non-Coding RNA  
We conclude that the distinction of lncRNAs from intronic sequences and untranslated regions of coding mRNAs remains a pressing research gap.  ...  Still, only a few representatives of this diverse class of RNAs are well studied, while the vast majority is poorly described beyond the existence of their transcripts.  ...  DeepLNC [42] is a tool based on deep neural networks (DNN).  ... 
doi:10.3390/ncrna7040077 pmid:34940758 pmcid:PMC8708962 fatcat:vcctqwr4dngzjgdmtwqu7cjewu

Synaptic plasticity in early aging

Gary Lynch, Christopher S. Rex, Christine M. Gall
2006 Ageing Research Reviews  
The view of LTP as having redundant and modulated substrates also suggests a number of approaches for reversing age-related losses.  ...  Tests for causes of the localized failure of LTP during early aging suggest that the problem lies in excessive activity of a negative modulator.  ...  Beyond this, the potentiation effect has a deep relationship with rhythmic patterns of brain activity associated with learning and has been shown to accompany the formation of specific memories (Roman  ... 
doi:10.1016/j.arr.2006.03.008 pmid:16935034 fatcat:hvhx6ner5zgipj4h4iei7abbpy
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