User-Specific Bicluster-based Collaborative Filtering

Tese de mestrado, Ciência de Dados, Universidade de Lisboa, Faculdade de Ciências, 2020Collaborative Filtering is one of the most popular and successful approaches for Recommender Systems. However, some challenges limit the effectiveness of Collaborative Filtering approaches when dealing with recommendation data, mainly due to the vast amounts of data and their sparse nature. In order to improve the scalability and performance of Collaborative Filtering approaches, several authors proposed successful approaches combining Collaborative Filtering with clustering techniques. In this work, we study the effectiveness of biclustering, an advanced clustering technique that groups rows and columns simultaneously, in Collaborative Filtering. When applied to the classic U-I interaction matrices, biclustering considers the duality relations between users and items, creating clusters of users who are similar under a particular group of items. We propose USBCF, a novel biclustering-based Collaborative Filtering approach that creates user specific models to improve the scalability of traditional CF approaches. Using a realworld dataset, we conduct a set of experiments to objectively evaluate the performance of the proposed approach, comparing it against baseline and state-of-the-art Collaborative Filtering methods. Our results show that the proposed approach can successfully suppress the main limitation of the previously proposed state-of-the-art biclustering-based Collaborative Filtering (BBCF) since BBCF can only output predictions for a small subset of the system users and item (lack of coverage). Moreover, USBCF produces rating predictions with quality comparable to the state-of-the-art approaches

Artificial immune system based security algorithm for mobile ad hoc networks

Securing Mobile Ad hoc Networks (MANET) that are a collection of mobile, decentralized, and self-organized nodes is a challenging task. The most fundamental aspect of a MANET is its lack of infrastructure, and most design issues and challenges stem from this characteristic. The lack of a centralized control mechanism brings added difficulty in fault detection and correction. The dynamically changing nature of mobile nodes causes the formation of an unpredictable topology. This varying topology causes frequent traffic routing changes, network partitioning and packet losses. The various attacks that can be carried out on MANETs challenge the security capabilities of the mobile wireless network in which nodes can join, leave and move dynamically. The Human Immune System (HIS) provides a foundation upon which Artificial Immune algorithms are based. The algorithms can be used to secure both host-based and network-based systems. However, it is not only important to utilize the HIS during the development of Artificial Immune System (AIS) based algorithms as much as it is important to introduce an algorithm with high performance. Therefore, creating a balance between utilizing HIS and AIS-based intrusion detection algorithms is a crucial issue that is important to investigate. The immune system is a key to the defence of a host against foreign objects or pathogens. Proper functioning of the immune system is necessary to maintain host homeostasis. The cells that play a fundamental role in this defence process are known as Dendritic Cells (DC). The AIS based Dendritic Cell Algorithm is widely known for its large number of applications and well established in the literature. The dynamic, distributed topology of a MANET provides many challenges, including decentralized infrastructure wherein each node can act as a host, router and relay for traffic. MANETs are a suitable solution for distributed regional, military and emergency networks. MANETs do not utilize fixed infrastructure except where a connection to a carrier network is required, and MANET nodes provide the transmission capability to receive, transmit and route traffic from a sender node to the destination node. In the HIS, cells can distinguish between a range of issues including foreign body attacks as well as cellular senescence. The primary purpose of this research is to improve the security of MANET using the AIS framework. This research presents a new defence approach using AIS which mimics the strategy of the HIS combined with Danger Theory. The proposed framework is known as the Artificial Immune System based Security Algorithm (AISBA). This research also modelled participating nodes as a DC and proposed various signals to indicate the MANET communications state. Two trust models were introduced based on AIS signals and effective communication. The trust models proposed in this research helped to distinguish between a “good node” as well as a “selfish node”. A new MANET security attack was identified titled the Packet Storage Time attack wherein the attacker node modifies its queue time to make the packets stay longer than necessary and then circulates stale packets in the network. This attack is detected using the proposed AISBA. This research, performed extensive simulations with results to support the effectiveness of the proposed framework, and statistical analysis was done which showed the false positive and false negative probability falls below 5%. Finally, two variations of the AISBA were proposed and investigated, including the Grudger based Artificial Immune System Algorithm - to stimulate selfish nodes to cooperate for the benefit of the MANET and Pain reduction based Artificial Immune System Algorithm - to model Pain analogous to HIS

A Multi-objective Multipopulation Approach For Biclustering

Biclustering is a technique developed to allow simultaneous clustering of rows and columns of a dataset. This might be useful to extract more accurate information from sparse datasets and to avoid some of the drawbacks presented by standard clustering techniques, such as their impossibility of finding correlating data under a subset of features. Given that biclustering requires the optimization of two conflicting objectives (residue and volume) and that multiple independent solutions are desirable as the outcome, a multi-objective artificial immune system capable of performing a multipopulation search, named MOM-aiNet, will be proposed in this paper. To illustrate the capabilities of this novel algorithm, MOM-aiNet was applied to the extraction of biclusters from two datasets, one taken from a well-known gene expression problem and the other from a collaborative filtering application. A comparative analysis has also been accomplished, with the obtained results being confronted with the ones produced by two popular biclustering algorithms from the literature (FLOC and CC) and also by another immune-inspired approach for biclustering (BIC-aiNet). © 2008 Springer-Verlag Berlin Heidelberg.5132 LNCS7182de França, F.O., Bezerra, G., Von Zuben, F.J., New Perspectives for the Biclustering Problem (2006) Proceedings of the IEEE Congress on Evolutionary Computation (CEC), pp. 753-760. , Vancouver, Canada, ppCheng, Y., Church, G.M., Biclustering of expression data (2000) Proc. of the 8th Int. Conf. on Intelligent Systems for Molecular Biology, pp. 93-103de Castro, L.N., Von Zuben, F.J.: aiNet: An Artificial Immune Network for Data Analysis. In: Abbass, H.A., Sarker, R.A., Newton, C.S. (eds.) Data Mining: A Heuristic Approach, pp. 231-259. Idea Group Publishing (2001)Cho, R., Campbell, M., Winzeler, E., Steinmetz, L., Conway, A., Wodicka, L., Wolfsberg, T., Davis, R., A genome-wide transcriptional analysis of the mitotic cell cycle (1998) Molecular Cell, 2, pp. 65-73GroupLens, MovieLens dataset, , http://www.grouplens.org/node/73, Department of Computer Science and Engineering, University of MinnesotaJiong, Y., Haixun, W., Wei, W., Yu, P.S., Enhanced biclustering on expression data (2003) Proc. of the Third IEEE Symposium on Bioinformatics and Bioengineering, pp. 321-327de Castro, P.A.D., de França, F.O., Ferreira, H.M., Von Zuben, F.J.: Applying Biclustering to Text Mining: An Immune-Inspired Approach. In: de Castro, L.N., Von Zuben, F.J., Knidel, H. (eds.) ICARIS 2007. LNCS, 4628, pp. 83-94. Springer, Heidelberg (2007)de Castro, P.A.D., de França, F.O., Ferreira, H.M., Von Zuben, F.J., Applying Biclustering to Perform Collaborative Filtering (2007) Proc. of the 7th International Conference on Intelligent Systems Design and Applications, pp. 421-426. , Rio de Janeiro, Brazil, ppde Castro, P.A.D., de França, F.O., Ferreira, H.M., Von Zuben, F.J., Evaluating the Performance of a Biclustering Algorithm Applied to Collaborative Filtering: A Comparative Analysis (2007) Proc. of the 7th International Conference on Hybrid Intelligent Systems, pp. 65-70. , Kaiserslautern, Germany, ppAgrawal, R., Gehrke, J., Gunopulus, D., Raghavan, P., Automatic subspace clustering of high dimensional data for data mining applications (1998) Proc. of the ACM/SIGMOD Int. Conference on Management of Data, pp. 94-105Dhillon, I.S., Co-clustering documents and words using bipartite spectral graph partitioning (2001) Proc. of the 7th Int. Con. on Knowledge Discovery and Data Mining, pp. 269-274Feldman, R., Sanger, J., (2006) The Text Mining Handbook, , Cambridge University Press, CambridgeHartigan, J.A., Direct clustering of a data matrix (1972) Journal of the American Statistical Association (JASA), 67 (337), pp. 123-129Symeonidis, P., Nanopoulos, A., Papadopoulos, A., Manolopoulos, Y., Nearestbiclusters collaborative filtering with constant values (2007) LNCS, 4811, pp. 36-55. , Advances in Web Mining and Web Usage Analysis, Philadelphia, USA, Springer, HeidelbergTang, C., Zhang, L., Zhang, I., Ramanathan, M., Interrelated two-way clustering: An unsupervised approach for gene expression data analysis (2001) Proc. of the 2nd IEEE Int. Symposium on Bioinformatics and Bioengineering, pp. 41-48Madeira, S.C., Oliveira, A.L., Biclustering algorithms for biological data analysis: A survey (2004) IEEE Transactions on Computational Biology and Bioinformatics, 1 (1), pp. 24-45Burnet, F.M., Clonal selection and after (1978) Theoretical Immunology, pp. 63-85. , Bell, G.I, Perelson, A.S, Pimgley Jr, G.H, eds, Marcel Dekker Inc, New YorkJerne, N.K.: Towards a network theory of the immune system. Ann. Immunol., Inst. Pasteur 125C, 373-389 (1974)de Castro, L.N., Von Zuben, F.J., Learning and Optimization Using the Clonal Selection Principle (2002) IEEE Transactions on Evolutionary Computation, 6 (3), pp. 239-251Deb, K., (2001) Multi-Objective Optimization Using Evolutionary Algorithms, , Wiley, ChichesterCoelho, G.P., Von Zuben, F. J.: omni-aiNet: An immune-inspired approach for omni optimization. In: Bersini, H., Carneiro, J. (eds.) ICARIS 2006. LNCS, 4163, pp. 294-308. Springer, Heidelberg (2006)Deb, K., Pratap, A., Agarwal, S., Meyarivan, T., A fast and elitist multiobjective genetic algorithm: NSGA-II (2002) IEEE Transactions on Evolutionary Computation, 6 (2), pp. 182-19

Improving A Multi-objective Multipopulation Artificial Immune Network For Biclustering

The biclustering technique was developed to avoid some of the drawbacks presented by standard clustering techniques. Given that biclustering requires the optimization of at least two conflicting objectives and that multiple independent solutions are desirable as the outcome, a few multi-objective evolutionary algorithms for biclustering were proposed in the literature. However, apart from the individual characteristics of the biclusters that should be optimized during their construction, several other global aspects should also be considered, such as the coverage of the dataset and the overlap among biclusters. These requirements will be addressed in this work with the MOM-aiNet+ algorithm, which is an improvement of the original multi-objective multipopulation artificial immune network denoted MOM-aiNet. Here, the MOM-aiNet+ algorithm will be described in detail, its main differences from the original MOM-aiNet will be highlighted, and both algorithms will be compared, together with three other proposals from the literature. © 2009 IEEE.27482755De França, F.O., Bezerra, G., Von Zuben, F.J., New Perspectives for the Biclustering Problem (2006) Proceedings of the IEEE Congress on Evolutionary Computation (CEC), pp. 753-760. , Vancouver, Canada(2003) The Analysis of Gene Expression Data, , G. Parmigiani, E. S. Garett, R. A. Irizarry, and S. L. Zeger, Eds., SpringerHerlocker, J., Konstan, J., Borchers, A., Riedl, J., An algorithmic framework for performing collaborative filtering (1999) Proceedings of the 1999 Conference on Research and Development in Information Retrieval, pp. 230-237Hartigan, J.A., Direct clustering of a data matrix (1972) Journal of the American Statistical Association (JASA), 67 (337), pp. 123-129Mirkin, B., (1996) Mathematical Classification and Clustering, , ser. Nonconvex Optimization and Its Applications. SpringerCheng, Y., Church, G.M., Biclustering of expression data (2000) Proc. of the 8th Int. Conf. on Intelligent Systems for Molecular Biology, pp. 93-103Mitra, S., Banka, H., Multi-objective evolutionary biclustering of gene expression data (2006) Pattern Recognition, 39 (12), pp. 2464-2477. , DOI 10.1016/j.patcog.2006.03.003, PII S0031320306000872, BioinformaticsDivina, F., Aguilar-Ruiz, J.S., A multi-objective approach to discover biclusters in microarray data (2007) Proceedings of the Genetic and Evolutionary Computation Conference (GECCO'07), pp. 385-392. , London, UKMaulik, U., Mukhopadhyay, A., Bandyopadhyay, S., Zhang, M.Q., Zhang, X., Multiobjective fuzzy biclustering in microarray data: Method and a new performance measure (2008) Proceedings of the 2008 IEEE Congress on Evolutionary Computation (CEC 2008), pp. 1536-1543. , Hong Kong, ChinaCoelho, G.P., De França, F.O., Von Zuben, F.J., A multi- objective multipopulation approach for biclustering (2008) Artificial Immune Systems, Proc. of the 7th International Conference on Artificial Immune Systems (ICARIS), 5132, pp. 71-82. , ser. Lecture Notes in Computer Science, P. J. Bentley, D. Lee, and S. Jung, Eds., Phuket, ThailandDe Castro, P.A.D., De França, F.O., Ferreira, H.M., Von Zuben, F.J., Applying biclustering to text mining: An immune-inspired approach (2007) Artificial Immune Systems, Proc. of the 6th International Conference on Artificial Immune Systems (ICARIS), 4628, pp. 83-94. , ser. Lecture Notes in Computer Science, L. N. de Castro, F. J. Von Zuben, and H. Knidel, Eds., Santos, BrazilAgrawal, R., Gehrke, J., Gunopulus, D., Raghavan, P., Automatic subspace clustering of high dimensional data for data mining applications (1998) Proc. of the ACM/SIGMOD Int. Conference on Management of Data, pp. 94-105Feldman, R., Sanger, J., (2006) Text Mining Handbook, , Cambridge University PressDe Castro, P.A.D., De França, F.O., Ferreira, H.M., Von Zuben, F.J., Evaluating the performance of a biclustering algorithm applied to collaborative filtering: A comparative analysis (2007) Proc. of the 7th International Conference on Hybrid Intelligent Systems, pp. 65-70. , Kaiserslautern, GermanySymeonidis, P., Nanopoulos, A., Papadopoulos, A., Manolopoulos, Y., Nearest-biclusters collaborative filtering with constant values (2007) Advances in Web Mining and Web Usage Analysis, 4811, pp. 36-55. , Philadelphia, USA: Springer-Verlag, ser. Lecture Notes in Computer ScienceMadeira, S.C., Oliveira, A.L., Biclustering algorithms for biological data analysis: A survey (2004) IEEE Transactions on Computational Biology and Bioinformatics, 1 (1), pp. 24-45Deb, K., (2001) Multi-Objective Optimization using Evolutionary Algorithms, , Chichester, UK: John-Wiley & SonsDe Castro, L.N., Timmis, J., (2002) Artificial Immune Systems: A New Computational Intelligence Approach, , SpringerBurnet, F.M., Clonal selection and after (1978) Theoretical Immunology, pp. 63-85. , G. I. Bell, A. S. Perelson, and G. H. Pimgley Jr, Eds. Marcel Dekker IncJerne, N.K., Towards a network theory of the immune system (1974) Ann. Immunol., Inst. Pasteur, 125 C, pp. 373-389De Castro, L.N., Von Zuben, F.J., Learning and optimization using the clonal selection principle (2002) IEEE Transactions on Evolutionary Computation, 6 (3), pp. 239-251AiNet: An artificial immune network for data analysis (2001) Data Mining: A Heuristic Approach, pp. 231-259. , H. A. Abbass, R. A. Sarker, and C. S. Newton, Eds. Idea Group PublishingDeb, K., Pratap, A., Agarwal, S., Meyarivan, T., A fast and elitist multiobjective genetic algorithm: NSGA-II (2002) IEEE Transactions on Evolutionary Computation, 6 (2), pp. 182-197Cho, R., Campbell, M., Winzeler, E., Steinmetz, L., Conway, A., Wodicka, L., Wolfsberg, T., Davis, R., A genome-wide transcriptional analysis of the mitotic cell cycle (1998) Molecular Cell, 2, pp. 65-73Alizadeh, A.A., Eisen, M.B., Davis, R.E., Ma, C., Lossos, I.S., Rosenwald, A., Boldrick, J.C., Staudt, L.M., Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling (2000) Nature, 403, pp. 503-51

Multi-objective Biclustering: When Non-dominated Solutions Are Not Enough

The biclustering technique was developed to avoid some of the drawbacks presented by standard clustering techniques, such as their impossibility of finding correlating data under a subset of features, and, consequently, to allow the extraction of more accurate information from datasets. Given that biclustering requires the optimization of at least two conflicting objectives (residue and volume) and that multiple independent solutions are desirable as the outcome, a few multi-objective evolutionary algorithms for biclustering were proposed in the literature. However, these algorithms only focus their search in the generation of a global set of non-dominated biclusters, which may be insufficient for most of the problems as the coverage of the dataset can be compromised. In order to overcome such problem, a multi-objective artificial immune system capable of performing a multipopulation search, named MOM-aiNet, was proposed. In this work, the MOM-aiNet algorithm will be described in detail, and an extensive set of experimental comparisons will be performed, with the obtained results of MOM-aiNet being confronted with those produced by the popular CC algorithm, by another immune-inspired approach for biclustering (BIC-aiNet), and by the multi-objective approach for biclustering proposed by Mitra & Banka. © 2009 Springer Science+Business Media B.V.82175202Han, J., Kamber, M., (2006) Data Mining: Concepts and Techniques, , Morgan Kaufmann San FranciscoDe França, F.O., Bezerra, G., Von Zuben, F.J., New perspectives for the biclustering problem (2006) Proceedings of the IEEE Congress on Evolutionary Computation (CEC), pp. 753-760. , IEEE, VancouverParmigiani, G., Garett, E.S., Irizarry, R.A., Zeger, S.L., (2003) The Analysis of Gene Expression Data, , Springer New YorkHerlocker, J., Konstan, J., Borchers, A., Riedl, J., An algorithmic framework for performing collaborative filtering (1999) Proceedings of the 1999 Conference on Research and Development in Information Retrieval, pp. 230-237. , Berkeley, 15-19 AugustFeldman, R., Sanger, J., (2006) The Text Mining Handbook, , Cambridge University Press CambridgeHartigan, J.A., Direct clustering of a data matrix (1972) J. Am. Stat. Assoc. (JASA), 67 (337), pp. 123-129Mirkin, B., (1996) Mathematical Classification and Clustering. Nonconvex Optimization and Its Applications., , Springer New YorkCheng, Y., Church, G.M., Biclustering of expression data (2000) Proc. of the 8th Int. Conf. on Intelligent Systems for Molecular Biology, pp. 93-103. , La Jolla, 19-23 AugustDeb, K., (2001) Multi-Objective Optimization Using Evolutionary Algorithms, , Wiley ChichesterMitra, S., Banka, H., Multi-objective evolutionary biclustering of gene expression data (2006) Pattern Recognition, 39 (12), pp. 2464-2477. , DOI 10.1016/j.patcog.2006.03.003, PII S0031320306000872, BioinformaticsDeb, K., Pratap, A., Agarwal, S., Meyarivan, T., A fast and elitist multiobjective genetic algorithm: NSGA-II (2002) IEEE Transactions on Evolutionary Computation, 6 (2), pp. 182-197. , DOI 10.1109/4235.996017, PII S1089778X02041012Mitra, S., Banka, H., Pal, S.K., A MOE framework for biclustering of microarray data (2006) Proceedings of the 18th International Conference on Pattern Recognition (ICPR'06), pp. 1154-1157. , Hong Kong, 20-24 AugustDivina, F., Aguilar-Ruiz, J.S., A multi-objective approach to discover biclusters in microarray data (2007) Proceedings of the Genetic and Evolutionary Computation Conference (GECCO'07), pp. 385-392. , London, 7-11 JulyGiráldez, R., Evolutionary search of biclusters by minimal intrafluctuation (2007) Proceedings of the IEEE International Fuzzy Systems Conference (FUZZ-IEEE 2007), pp. 1-6. , IEEE, LondonMaulik, U., Mukhopadhyay, A., Bandyopadhyay, S., Zhang, M.Q., Zhang, X., Multiobjective fuzzy biclustering in microarray data: Method and a new performance measure (2008) Proceedings of the 2008 IEEE Congress on Evolutionary Computation (CEC 2008), pp. 1536-1543. , IEEE, Hong KongKrishnapuram, R., Joshi, A., Yi, L., A fuzzy relative of the k-medoids algorithm with application to document and snippet clustering (1999) Proceedings of the IEEE International Conference on Fuzzy Systems (FUZZ-IEEE'99), pp. 1281-1286. , IEEE, SeoulDe França, F.O., Von Zuben, F.J., Coelho, G.P., A multi-objective multipopulation approach for biclustering (2008) Lecture Notes in Computer Science, 5132, pp. 71-82. , Bentley, P.J., Lee, D., Jung, S. (eds.) Artificial Immune Systems, Proc. of the 7th International Conference on Artificial Immune Systems (ICARIS) Phuket, 10-13 AugustDe Castro, L.N., Timmis, J., (2002) Artificial Immune Systems: A New Computational Intelligence Approach, , Springer New YorkDe França, F.O., Ferreira, H.M., Von Zuben, F.J., Castro, P.A.D., Applying biclustering to text mining: An immune-inspired approach (2007) Lecture Notes in Computer Science, 4628, pp. 83-94. , de Castro, L.N., Von Zuben, F.J., Knidel, H. (eds.) Artificial Immune Systems, Proc. of the 6th International Conference on Artificial Immune Systems (ICARIS) Santos, 26-29 AugustAgrawal, R., Gehrke, J., Gunopulus, D., Raghavan, P., Automatic subspace clustering of high dimensional data for data mining applications (1998) Proc. of the ACM/SIGMOD Int. Conference on Management of Data, pp. 94-105. , Seattle, 2-4 JuneDhillon, I.S., Co-clustering documents and words using bipartite spectral graph partitioning (2001) Proc. of the 7th Int. Con. on Knowledge Discovery and Data Mining, pp. 269-274. , San Francisco, 26-29 AugustDe França, F.O., Ferreira, H.M., Von Zuben, F.J., Castro, P.A.D., Applying biclustering to perform collaborative filtering (2007) Proc. of the 7th International Conference on Intelligent Systems Design and Applications, pp. 421-426. , Rio de Janeiro, 22-24 OctoberDe França, F.O., Ferreira, H.M., Von Zuben, F.J., Castro, P.A.D., Evaluating the performance of a biclustering algorithm applied to collaborative filtering: A comparative analysis (2007) Proc. of the 7th International Conference on Hybrid Intelligent Systems, pp. 65-70. , Kaiserslautern, 17-19 SeptemberSymeonidis, P., Nanopoulos, A., Papadopoulos, A., Manolopoulos, Y., Nearest-biclusters collaborative filtering with constant values (2007) Lecture Notes in Computer Science, 4811, pp. 36-55. , Advances in Web Mining and Web Usage Analysis Springer PhiladelphiaTang, C., Zhang, L., Zhang, I., Ramanathan, M., Interrelated two-way clustering: An unsupervised approach for gene expression data analysis (2001) Proc. of the 2nd IEEE Int. Symposium on Bioinformatics and Bioengineering, pp. 41-48. , IEEE PiscatawayMadeira, S.C., Oliveira, A.L., Biclustering algorithms for biological data analysis: A survey (2004) IEEE Trans. Comput. Biol. Bioinformatics, 1 (1), pp. 24-45Edgeworth, F.Y., (1881) Mathematical Physics, , P. Keagan LondonPareto, V., (1896) Cours d'Economie Politique, , F. Rouge LausanneBurnet, F.M., Bell, G.I., Perelson, A.S., Pimgley Jr., G.H., Clonal selection and after (1978) Theoretical Immunology, pp. 63-85. , Marcel Dekker New YorkJerne, N.K., Towards a network theory of the immune system (1974) Ann. Immunol. Inst. Pasteur, 125 C, pp. 373-389De Castro, L.N., Von Zuben, F.J., Learning and optimization using the Clonal selection principle (2002) IEEE Trans. Evol. Comput., 6 (3), pp. 239-251De Castro, L.N., Von Zuben, F.J., AiNet: An artificial immune network for data analysis (2001) Data Mining: A Heuristic Approach, pp. 231-259. , Abbass, H.A., Sarker, R.A., Newton, C.S. (eds.) Idea Group, HarrisburgCoelho, G.P., Von Zuben, F.J., Omni-aiNet: An immune-inspired approach for omni optimization (2006) Lecture Notes in Computer Science, 4163, pp. 294-308. , Bersini, H., Carneiro, J. (eds.) Artificial Immune Systems, Proc. of the 5th International Conference on Artificial Immune Systems (ICARIS) Oeiras, PortugalCho, R.J., Campbell, M.J., Winzeler, E.A., Steinmetz, L., Conway, A., Wodicka, L., Wolfsberg, T.G., Davis, R.W., A genome-wide transcriptional analysis of the mitotic cell cycle (1998) Molecular Cell, 2 (1), pp. 65-73Alizadeh, A.A., Elsen, M.B., Davis, R.E., Ma, C.L., Lossos, I.S., Rosenwald, A., Boldrick, J.C., Staudt, L.M., Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling (2000) Nature, 403 (6769), pp. 503-511. , DOI 10.1038/35000501Snedecor, G.S., Cochran, W.G., (1989) Statistical Methods, , Iowa University Press Iow