Identifying Cancer Subnetwork Markers Using Game Theory Method

Farahmand, Saman; Goliaei, Sama; Kashani, Zahra Razaghi Moghadam; Farahmand, Sina

doi:10.1007/978-981-10-4505-9_17

Cited by 5 publications

(3 citation statements)

References 11 publications

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“…A maximum value of 10 was selected for k to guarantee that at least 99% of the RMS values lie below the RMS-threshold according to Chebyshev's inequality [27]. To find the optimum RMS-threshold for HFO detection, the ROC curve was used to plot the sensitivity as a function of FDR, when varying RMS-threshold [28]. Fig.…”

Section: A Performance Resultsmentioning

confidence: 99%

Using Automated Detection and Classification of Interictal HFOs to Improve the Identification of Epileptogenic Zones in Preparation for Epilepsy Surgery

Farahmand

Sobayo

Mogul

2019

Preprint

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Objective: For more than 25 million drug-resistant epilepsy patients, surgical intervention aiming at resecting brain regions where seizures arise is often the only alternative therapy. However, the identification of this epileptogenic zone (EZ) is often imprecise which may affect post-surgical outcomes (PSOs). Interictal high-frequency oscillations (HFOs) have been revealed to be reliable biomarkers in delineating EZ. In this paper, an analytical methodology aiming at automated detection and classification of interictal HFOs is proposed to improve the identification of EZ. Furthermore, the detected high-rate HFO areas were compared with the seizure onset zones (SOZs) and resected areas to investigate their clinical relevance in predicting PSOs. Methods: FIR band-pass filtering as well as a combination of time-series local energy, peak, and duration analysis were utilized to identify high-rate HFO areas in interictal, multichannel intracranial electroencephalographic (iEEG) recordings. The detected HFOs were then classified into fast-ripple (FR), ripple (R), and fast-ripple concurrent with ripple (FRandR) events. Results: The proposed method resulted in sensitivity of 91.08% and false discovery rate of 7.32%. Moreover, it was found that the detected HFO-FRandR areas in concordance with the SOZs would have better delineated the EZ for each patient, while limiting the area of the brain required to be resected. Conclusion: Testing on a dataset of 20 patients has supported the feasibility of using this method to provide an automated algorithm to better delineate the EZ. Significance: The proposed methodology may significantly improve the precision by which pathological brain tissue can be identified.Index Terms-Epilepsy, high-frequency oscillations (HFOs), false-discovery rate (FDR), epileptogenic zone, epilepsy surgery, sensitivity.

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Section: A Performance Resultsmentioning

confidence: 99%

Using Automated Detection and Classification of Interictal HFOs to Improve the Identification of Epileptogenic Zones in Preparation for Epilepsy Surgery

Farahmand

Sobayo

Mogul

2019

Preprint

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“…The second category are sequence-based predictors. These predictors utilize machine learning and probabilistic models such as Bayesian methods, Hidden Markov Models (HMMs), and conditional Random Fields [23,19,2,6], trained on features extracted from the proteins' sequences to predict the binding sites. For example, sequential features such as hydrophobicity, which plays an important role in stabilizing protein-protein interactions, and amino acid propensity, are typically utilized to infer binding site properties.…”

Section: Introductionmentioning

confidence: 99%

Patch-DCA: improved protein interface prediction by utilizing structural information and clustering DCA scores

2019

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Motivation Over the past decade, there have been impressive advances in determining the 3D structures of protein complexes. However, there are still many complexes with unknown structures, even when the structures of the individual proteins are known. The advent of protein sequence information provides an opportunity to leverage evolutionary information to enhance the accuracy of protein–protein interface prediction. To this end, several statistical and machine learning methods have been proposed. In particular, direct coupling analysis has recently emerged as a promising approach for identification of protein contact maps from sequential information. However, the ability of these methods to detect protein–protein inter-residue contacts remains relatively limited. Results In this work, we propose a method to integrate sequential and co-evolution information with structural and functional information to increase the performance of protein–protein interface prediction. Further, we present a post-processing clustering method that improves the average relative F1 score by 70% and 24% and the average relative precision by 80% and 36% in comparison with two state-of-the-art methods, PSICOV and GREMLIN. Availability and implementation https://github.com/BioMLBoston/PatchDCA Supplementary information Supplementary data are available at Bioinformatics online.

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“…There are also other sources of transcriptional regulatory network including JASPAR [10], the Open Regulatory Annotation database (ORegAnno) [11], SwissRegulon [12], the Transcriptional Regulatory Element Database (TRED) [13], the Transcription Regulatory Regions Database (TRRD) [14], TFactS [15], TRRUST [16]. These databases have been assembled with a variety of approaches, including reverse engineering approaches based on high-throughput gene expression experiments [17,18], text mining approaches [19], and manual curation [20].…”

Section: Introductionmentioning

confidence: 99%

ModEx: A text mining system for extracting mode of regulation of Transcription Factor-gene regulatory interaction

Farahmand

Riley

Zarringhalam

2019

Preprint

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A B S T R A C T Background: Transcription factors (TFs) are proteins that are fundamental to transcription and regulation of gene expression. Each TF may regulate multiple genes and each gene may be regulated by multiple TFs. TFs can act as either activator or repressor of gene expression. This complex network of interactions between TFs and genes underlies many developmental and biological processes and is implicated in several human diseases such as cancer. Hence deciphering the network of TFgene interactions with information on mode of regulation (activation vs. repression) is an important step toward understanding the regulatory pathways that underlie complex traits. There are many experimental, computational, and manually curated databases of TF-gene interactions. In particular, high-throughput ChIP-Seq datasets provide a large-scale map or transcriptional regulatory interactions. However, these interactions are not annotated with information on context and mode of regulation. Such information is crucial to gain a global picture of gene regulatory mechanisms and can aid in developing machine learning models for applications such as biomarker discovery, prediction of response to therapy, and precision medicine. Methods: In this work, we introduce a text-mining system to annotate ChIP-Seq derived interaction with such meta data through mining PubMed articles. We evaluate the performance of our system using gold standard small scale manually curated databases. Results: Our results show that the method is able to accurately extract mode of regulation with F-score 0.77 on TRRUST curated interaction and F-score 0.96 on intersection of TRUSST and ChIP-network. We provide a HTTP REST API for our code to facilitate usage. Availibility: Source code and datasets are available for download on GitHub: https:

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Identifying Cancer Subnetwork Markers Using Game Theory Method

Cited by 5 publications

References 11 publications

Using Automated Detection and Classification of Interictal HFOs to Improve the Identification of Epileptogenic Zones in Preparation for Epilepsy Surgery

Using Automated Detection and Classification of Interictal HFOs to Improve the Identification of Epileptogenic Zones in Preparation for Epilepsy Surgery

Patch-DCA: improved protein interface prediction by utilizing structural information and clustering DCA scores

ModEx: A text mining system for extracting mode of regulation of Transcription Factor-gene regulatory interaction

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