dataesr/scientific-paragraphs-categorization huggingface dataset:
Construction of gene co-expression networks To build gene co-expression networks in each tissue, we employed five complementary methods with default parameters, including WGCNA (v1. 69)(135), ICA (v1. 0. 2)(136), PEER (v1. 3)(137), MEGENA (v1. 3. 7)(138), and CEMiTool (v1. 8. 3)(139). The input gene expression values were adjusted for hidden confounding factors by regressing out 10 PEER factors and 5 genotypic PCs (see "Preparation for molQTL mapping" section). Functional enrichment analysis of gene coexpression modules was conducted by using clusterProfiler (v4. 0)(103), and the following visualization was done using Gephi (v0. 9. 2)(140).
SNP calling from RNA-Seq samples To call SNPs from RNA-Seq samples, we marked PCR duplicates in STAR alignment files and split reads that contained Ns in their cigar string using MarkDuplicates and SplitNCigarReads modules of the Genome Analysis Toolkit (GATK, v 4. 1. 9. 0)  (45) , respectively. Using the Ensembl dbSNP database (v102), we recalibrated base quality scores using GATK BaseRecalibrator and ApplyBQSR modules. By following the best practice of germline variant calling from RNA-Seq data, we detected small variants from the recalibrated alignments files, which generated individual Genomic Variant Call Format (GVCF) files using the HaplotypeCaller function of the GATK tool  (45). Then, we carried out joint-calling of all GVCF samples using the GenotypeGVCFs module from the GATK tool  (45).
Construction of the multi-breed genotype imputation panel and genotype imputation We retrieved 1,693 public WGS data sets from SRA (n=1,213) and GSA (n = 480) databases along with 1,176 additional newly generated WGS samples, resulting in a total set of 2,869 WGS samples (Table  S6 ). All raw sequence reads passed a uniform computational pipeline, including adaptor removal, read alignment, and SNP calling. Briefly, we trimmed read adaptors and lowquality reads using the Trimmomatic v0. 39 software(141). The obtained clean reads were further aligned against the Ensembl GRCg6a chicken reference genome (v102) using the MEM algorithm of the Burrows-Wheeler Aligner (BWA, v0. 7. 17)(142). The alignment files in Binary Alignment Map (BAM) format were sorted using SAMtools (v1. 9)(143), and were further passed for the removal of PCR duplicates using GATK (v4.
1. 9. 0)  (45). The obtained BAM files were then used for variant discovery to generate individual GVCF files using the HaplotypeCaller function of the GATK tool  (45). The joint-calling of all 2,869 GVCF samples was further done using the GenotypeGVCFs module from the GATK tool  (45). For selecting high-quality SNPs, we carried out a hard-filtering with criteria of "QD < 2. 0, MQ < 40. 0, FS > 60. 0, SOR > 3. 0, MQRankSum < -12. 5, and ReadPosRankSum < -8. 0", resulting in a total set of 117,900,812 clean SNPs. To create the genotype imputation reference panel, we first filtered out multi-allelic and sex chromosomal SNPs, as well as those with MAF < 0. 01 and missing rate > 0. 9 using BCFtools v1. 10. 2(144), and then imputed missing genotypes using the Beagle 5. 1 program(145). This yielded the final reference panel consisting of 2,869 samples and 10,520,420 SNP genotypes.
Principal component analysis. Within each of those 28 tissues, we first LD-pruned imputed genotypes with the option of "--indep-pairwise 200 100 0. 1" using PLINK v1. 9(146). Principal component analysis (PCA) of samples was then carried out, based on the LD-pruned genotypes using the smartpca tool of the EIGENSOFT v8. 0. 0 package(147). The top 5 principal components (PCs) were selected as covariates for heritability estimation and molQTL mapping. Estimating PEER confounder factors. To correct for confounders and other unwanted technical or biological variations in RNA-Seq samples, we estimated the Probabilistic Estimation of Expression Residuals (PEER) in each of the tissues using the PEER software package(137). The top 10 PEER factors showing highly relative contributions (i. e. , factor weight variance) to gene expression variation were selected for subsequent heritability estimation and molQTL mapping.
Estimating cis-heritability of gene expression We leveraged the GCTA program v1. 93. 2(149)  to estimate cis-heritability (cis-h 2 ) of molecular phenotypes by fitting a mixed linear model: 𝑦 = 𝑋𝛽 + 𝑔 + 𝜀 where y is a vector of phenotypic values (i. e. , gene expressions) of all samples, β is a vector of corresponding coefficients of quantitative covariates X of all samples, which included 5 genotypic PCs and 10 PEER factors, g is a vector of the genetic values of SNPs around ±1Mb of the transcription start sites (TSS) of a gene, and ɛ is a vector of residuals. The genetic value g followed a normal distribution with mean of 0 and variance of 𝑨𝜎 𝑔 2 , where A was the genetic relationship matrix (GRM) between individuals(150). Thus, we can estimate 𝜎 𝑔 2 , i. e. , the variance explained by SNP genotypes (i.
Allele specific expression (ASE) We conducted a haplotype-based ASE analysis through the phASER (v1. 1. 1) software(160). To exclude genomic regions with high mapping error rates, we first computed the genome mappability using GenMap (v1. 3. 0)(161) with parameters: -K 75 -E 2. The generated blacklist was fitted to the phASER (v1. 1. 1) tool(160) to phase variants from the STAR alignment BAM and VCF files with options of "--paired_end 1 --mapq 255 --baseq 10". Using the script "phaser_expr_matrix. py", we measured gene-level haplotypic expression for all samples with default parameters. The generated haplotypic counts files for individual samples were further aggregated by tissue by using "phaser_expr_matrix. py". Finally, we used the "phaser_cis_var. py" script to estimate the effect size of eQTLs based on aggregated haplotypic counts.
Replication of molQTL discovery To assess the replication rate of molQTL discovery, we employed the π1 statistic embedded in the qvalue package  (51, 157). Briefly, we randomly split RNA-Seq samples into two groups -QTL discovery and validation population, when the tissue sample size was greater than 100. We ran QTL mapping in each group separately using tensorQTL (v1. 0. 4) (152) as described above. Based on replicated eQTL P-values, we calculated π0 value that measured the overall proportion of true null hypotheses using the pi0est function within the qvalue package(157). The π1 was thus obtained by 1-π0.
DeepSEA model training and variant effect prediction DeepSEA is a deep learning model initially trained for predicting variant effects in human  (50) , while in this study, it was retrained by utilizing 310 epigenomic peaks generated by the chicken FAANG consortium  (30)  and by Zhu et al. (162) (Table  S7 ). According to sequencing type and histone marks, we categorized all 310 epigenomic peaks into groups, including ATAC, CTCF, DNaseSeq, H3K27ac, H3K23me3, H3K4me1 and H3K4me3, which were used as input for the model training using the Selene, a PyTorch-based package (163). Briefly, we grouped the genome into 200-bp bins and then labeled the bins according to input features. A genomic bin will be labeled 1 if half of the bin overlaps with an epigenomic peak, otherwise labeled as 0. The model was then trained based on a sequence region of 1,000 bp (i.
e. , input feature), where the 200-bp bin was placed at the center. We created validation and testing datasets by grouping chromosomes, specifically grouping chromosomes 8 and 9 to the training set and chromosomes 6 and 7 to the validation set. We computed the area under the receiver operating characteristic (AUROC) to evaluate the performance of the DeepSEA model. After that, we computed variant effect/score of two alleles for a given molQTL, i. e. 2 × 310 predicted chromatin variant scores, by inputting a 1000-bp sequence with the center being the Ref or Alt allele. The score is defined as the relative log fold changes of odds between predicted scores of the Ref and Alt. For each feature, SNPs with a score greater than 0. 7 were identified as variants affecting the feature.
Functional enrichment of molQTL To understand the enrichment of molQTL in sequence ontology (i. e. , SNP functional types annotated by SnpEff v5. 0e) and regulatory elements (i. e. , 15 chromatin states annotated in(30)), we employed the formula: 𝐸 = (𝐶 / 𝐴) (𝐵 / 𝐷) where A and D are the length of feature annotations and the total genome length, respectively. C is the length of molQTL overlapped with feature annotations, and B is the length of molQTL overlapped with the total genome length. To further uncover the regulatory mechanism of molQTL, we retrieved predicted pairs of regulatory elements-target genes from  (30). We then overlapped them with our molQTL-regulated genes but at the same time required the molQTL to be located within regulatory elements. Moreover, we also performed the enrichment analysis of molQTL-regulated genes and HiC TAD with data retrieved from a previous study (52) using the SnakeHiC pipeline (https://github.
Comparative analysis of gene expression To comparatively analyze gene expression across species, we collected gene expression and regulation data from multiple sources. Specifically, we obtained data for 15,044 samples from the Human GTEx web portal (v8) available at https://gtexportal. org. Additionally, we gathered gene expression data for 7,095 pig samples and 8,742 cattle samples from the FarmGTEx resource accessible at https://www. farmgtex. org/. Furthermore, as part of this study, we included gene expression and regulation data for 7,015 chicken samples. In this study, we focused on protein-coding genes based on the annotation of the Ensembl (v102), and we considered the genes with TPM > 0. 1 as expressed. Specifically, we grouped chicken genes into "1-1-1-1 orthologous gene" (1-1 orthologous across species, n = 10600), "complex orthologous genes" ("1 to many", "many to 1" and "many to many", n=3644), "no homology" (without any homologous counterpart in mammals, n=2535).
Acknowledgments: We thank all the researchers who have contributed to the publicly available data used in this research. Competitive grants nos. 2020-67015-31175 ,  2015-67015-22940 , and  2022-67015-36215  (H. Z. ) from the  USDA National Institute of Food and Agriculture , Multistate Research Project  NRSP8  and  NC1170  (H. Z. ), and the  California Agricultural Experimental Station  (H. Z. ). N. Y. acknowledges fundings from the  National Key Research and Development Program of China  ( 2021YFD1300600  and  2022YFF1000204 ). X. H. was supported by the  National Natural Science Foundation of China , " Genetic architecture, gene interaction and genomic prediction for chicken growth evaluated using large advanced intercross populations " ( 31961133003 ). Y. W. was supported by the  National Natural Science Foundation of China , " Deciphering the genetic architecture of polygenic clustered QTL for chicken body weight by integrative omics ", ( 32272862 ).
In the past decade, Android has become a standard smartphone operating system. The mobile devices running on the Android operating system are particularly interesting to malware developers, as the users often keep personal information on their mobile devices. This paper proposes a deep learning model for mobile malware detection and classification. It is based on SAE for reducing the data dimensionality. Then, a CNN is utilized to detect and classify malware apps in Android devices through binary visualization. Tests were carried out with an original Android application (Drebin-215) dataset consisting of 15,036 applications. The conducted experiments prove that the classification performance achieves high accuracy of about 98. 50%. Other performance measures used in the study are precision, recall, and F1-score.
For  (Alazab et al. 2020) , there are three methods of extracting features from mobile apps that are frequently used by security vendors and researchers: static, dynamic, and hybrid (a combination of static and dynamic). These techniques used by malware researchers encourage the need for new research on detection techniques, in which deep learning based methods are included and used to be more useful with high accuracy. In a general sense, deep learning techniques automate the analysis of mobile malware detection by recognizing the malware pattern  (Gohari et al. 2021; Li et al. 2018). These techniques are a specific field of artificial intelligence that predicts future decisions and outputs based on datasets  (Zhang et al. 2021). They refer to the process of characterizing malware behavior and applying classifiers to evaluate the dataset.
More recently, classifiers of deep learning have been involved in the development of intelligent systems. Deep learning methods develop a neural network that simulates the brain of humans for analytical learning. According to  (Alzaylaee et al. 2019; Hou et al. 2017; Nix & Zhang, 2017) , deep learning classifiers have inspired a great number of effective approaches in image segmentation, protein structure, machine translation, speech recognition, and natural language processing. The deep learning techniques acquire a labeled dataset and generate an output model which is able to process new data. Classifiers learn the input and labeled output from an abundance to construct a model. To solve the previous problems, the proposal of automated classifiers to assist Android application detection can greatly reduce time and cost, which is of critical importance to increase detection accuracy and to improve mobile security.
Overall, there are three major contributions made by this research: (1) A comprehensive review of ideas and research efforts in the background is conducted and organized through a three-step process catered for Deep Learning (SAE & CNNs) based Android malware detection and classification; (2) Using the Android application "Drebin-215" dataset containing vectors of 215 characteristics from 15,036 apps samples, we compared our performance model to state-of-the-art and recently presented models in the literature. As well, we described a scenario for analyzing the Drebin-215 dataset: malware binary that distinguishes benign from malicious applications; (3) an overview and summary of current state-of-the-art solutions are elaborated with in-depth analysis and future work in order to assist other researchers in broadening their perspective on DL-based Android malware.
Android Malware Detection with Static Analysis In most literature, many researchers have been developed several solutions using static analysis by using features such as permissions, API calls, Dalvik byte code, and intents. In the right context, many researchers have verified the effectiveness of the tools or suggested new tools that may be more effective in the process of accurately detecting malicious applications. Furthermore, conventional static detection methods are limited by signature databases, complex reflective calls, and high reliance upon users and resources. For example,  Hou et al. (2016)  proposed a deep belief networkbased Android malware detection system DroidDelver using a real sample collection from Comodo Cloud Security Center. They perform feature engineering on API calls by categorizing them into blocks.
(Fereidooni et al. 2016)  proposed a system to detect Android malware, named ANASTASIA, using features such as intents, permissions, system commands, and API calls. The system uses many classifiers including the deep neural network. In other work,  (Mchaughlin et al. 2017)  proposed a novel Android malware detection system that uses a deep convolutional neural network (CNN). In the system, malware classification is performed based on static analysis of the raw opcode sequence from a disassembled program. In the work done by  (Zegzhda et al. 2018) , is to explores the use of deep learning for malware identification in the Android operating system. For this, a self-designed approach is proposed for representing an Android application for a convolutional neural network, which consists in constructing an RGB image, the pixels of which are formed from a sequence of pairs of API calls and protection levels.
Yet,  (Feng et al. 2020)  proposed an effective Android malware detection system named MobiTive leveraging customized deep neural networks to provide a real-time and responsive detection environment on mobile devices. Moreover, deep learning-based approach can be maintained on server side efficiently for malware detection. Moreover,  (Iadarola et al. 2021 ) proposed a method relying on application representation in terms on images used to input an explainable deep learning model designed by authors for Android malware detection and family identification. Similarly,  (Amato et al. 2021)  proposed a method based on deep learning (Neural Networks and MultiLayer Perceptrons) aiming at discovering attacks towards the CAN-bus by analyzing a real-world dataset with the injection of messages from different types of attacks: denial of service, fuzzy pattern attacks, and attacks against specific components.
(Hoang Khoa et al. 2021) proposed a system named uitMultiClandroid to detect Android malware by using deep learning. For this, the authors applied image classification models to detect Android malware based on the converted images from APK files. Furthermore,  (Gao et al. 2021)  proposed a novel approach for Android malware detection and familial classification based on the Graph Convolutional Network (GCN). The general idea is to map apps and Android APIs into a large heterogeneous graph, converting the original problem into a node classification task. The heterogeneous graph is then fed into the GCN model, iteratively generating node embeddings that incorporate topological structure and node features. (Xing et al. 2022)  proposed a novel malware detection model which combines a grey-scale image representation of malware with an autoencoder network in a deep learning model, analyses the feasibility of the grey-scale image approach of malware based on the reconstruction error of the autoencoder, and uses the dimensionality reduction features of the autoencoder to achieve the classification of malware from benign software.
In the right context,  (Dhabal & Gupta, 2023)  proposed a novel Android malware detection framework using a hybrid of bidirectional long short-term memory (BiLSTM) and merged sparse auto-encoder (MSAE) with softmax deep learning mode. In the study done by  (Alomari et al. 2023) , introduced deep learning and feature selection methodologies to arrive at a high-performance malware detection system. Two different malware datasets are used to detect malware and differentiate it from benign activities. In conclusion, static analysis approaches can efficiently identify malware but they may become invalid when malicious applications use reflection or dynamic loading to perform malicious behaviors.
2017)  proposed a new genetic algorithm designed to evolve the parameters, and the architecture, of a DNN with the goal of maximizing the malware classification accuracy, and minimizing the complexity of the model. The model described is tested on a specific problem within the security field, which consists of determining the malware family of different malicious samples. A study was conducted by  (Kim et al. 2018 ) in order to present a malware detection framework based on multiple neural networks. Every network has a single feature input and output score. The final detection result is a combination of all the models. (Xu et al. 2018)  proposed an effective approach named CDGDroid for Android malware detection based on deep learning. For this, the authors used the semantics graph representations, that is, control flow graph, data flow graph, and their possible combinations, as the features to characterize Android application in order to train the classification model via Convolutional Neural Networks (CNNs).
Additionally,  (Jin et al. 2020 ) proposed a malware detection method based on deep learning, which uses malware images and a set of autoencoders to detect malware. The method is to design an autoencoder to learn the functional characteristics of malware, and then to observe the reconstruction error of the autoencoder to realize the classification and detection of malware and benign software. A study in dynamic analysis domain was conducted by  (Ali-Gombe et al. 2019 ) in order to develop the DroidScraper system to recover important runtime data structures of application software by enumerating and reconstructing the objects in memory for mobile device forensics and postmortem analysis. In an earlier work,  Lu et al. (2020)  proposed an Android malware detection algorithm based on a hybrid deep learning model which combines deep belief network (DBN) and gate recurrent unit (GRU).
First of all, analyze the Android malware; in addition to extracting static features, dynamic behavioral features with strong antiobfuscation ability are also extracted. Then, build a hybrid deep learning model for Android malware detection. In the study done by  (Mbaziira, 2020) , to use a deep neural network (DNN) implementation of deep learning called DeepLearning4J (DL4J) to generate our models for mobile malware detection. The models detect mobile malware with accuracy rates ranging between 97% and 99% when applied to two types of malicious datasets. In other words,  (Arslan et al. 2021 ) proposed a novel method encompassing feature selection and feature weighting for malware detection in mobile applications using deep learning (multi-layer structure). To this end, firstly, the manifest file was read from the Android application package.
Different features such as activities, services, permissions were extracted from the file, and for classification, a selection was made among these features. Besides that,  (Sihag et al. 2021)  proposed De-LADY (Deep Learning based Android malware detection using DYnamic features) an obfuscation resilient approach. It utilizes behavioral characteristics from dynamic analysis of an application executed in emulated environment. In the study of  (Alzubaidi, 2021)  which has implemented a detection scheme using effective deep learning algorithms (LSTM and MLP). Also, the author has tested their ability to detect malware by employing private and public datasets, with an accuracy of over 99%. Besides that,  (Ahmad et al. 2021 ) have implemented a detection system using efficient Deep Learning algorithms (i.
e. , LSTM and BLSTM) by employing the latest publicly available "CICAndMal2017" dataset in order to protect the android systems against numerous attacks. Further, it uses standard evaluation metrics for the measurement of the system's performance. (Lukas & Kołaczek, 2021)  described the basic concepts of the Android and deep learning algorithms in order to test several features of the application and checking several deep learning algorithms. In addition, the authors have proposed a solution based on the use of binary file representation and self-organizing maps. Another research by  (Sumit & Adamuthe, 2022) , it is to propose a deep learning model capable of building a feature set from the EMBER dataset and classifying the malicious codes' static PE files efficiently. In earlier work,  Atacak et al.
APPRoACH PRoPoSED As per our objective and motivations, this study is associated with some background ideas and research efforts as shown in Figure. 1. Brieñy, especially using Autoencoder Neural Network and CNNs models for dimensionality reduction and classification (mobile malware). In general, widely followed automatic dimensionality reduction and binary classification approaches performed with Deep Learning models such as SAE and CNNs have been directed to the malware apps in Android (See Figure  1 ). In the context mentioned above, this study followed an easy-to-design Dimensionality Reduction using SAE and deep CNN approach for automatic detection and classify Android malware, by considering the Android application as an input dataset (Drebin-215). In this respect, Figure. 2 represents the stages within the ñow of the introduced deep learning approach.