TECHNOLOGY FOCUS - BIOTECHNOLOGY - Preferred Method: Selecting microorganisms to generate clean energy from biowastes is an in silico screening platform, or a knowledge pattern learning and search system. The microorganisms consist of bacteria, fungi, archaea or protists. Microorganisms can be a single species or a mixture of consortia. Microorganisms can be natural or bioengineered and genetic-altered organisms. The clean energy is clean, renewable and alternative energy which has the maximum efficiency and minimal impact on the environment, where the clean energy comprises hydrogen, electricity, ethanol, methane, biogas or solar energy. The biowastes comprise food and drink processing wastewaters, domestic wastewaters, animal or meat-packing wastes, metal wastes, nuclear wastes, or by-products of industrial processes such as DDGS (distillers dry grain and solubles) from ethanol production. The metabolic and energy-generating biological pathways comprise metabolism, genetic information processing, environmental information processing, and cellular processes pathways. Metabolism pathways consist of carbohydrate metabolism, energy metabolism, lipid metabolism, nucleotide metabolism, amino acid metabolism, metabolism of other amino acids, glycan biosynthesis and metabolism, biosynthesis of polyketides and non-ribosomal peptides, metabolism of cofactors and vitamins, biosynthesis of secondary metabolites, xenobiotics biodegradation and metabolism, and metabolism of enzyme families. Genetic information processing pathways consist of transcription, translation, aminoacyl-trna biosynthesis, folding, sorting and degradation, replication and repair pathways. Environmental information processing pathways consist of membrane transport, signaling molecules and interaction pathways, and phosphatidylinositol signaling system. Cellular processes pathways consist of cell motility, cell growth and death, and cell communication pathways. Step (1) within the massive genetic and chemical fingerprints of the organisms in the metabolic and energy-generating biological pathways comprise gene similarity scores along the metabolic pathways among organisms, gene usages in the metabolic pathways, percentage of gene usages in general function categories, codon usages, and distribution of gene usages in metabolic function categories. Step (1) within the organisms' biological behaviors include abilities to utilize or digest certain substrates, to undergo aerobic or anaerobic fermentation, to produce hydrogen (hydrogenic), electricity (electrogenic), methane (methanogenic), or other desired products. Step (1) within the organisms' profiles is the data or text describing the organisms with respects to its biological system (as the massive genetic and chemical fingerprints) that link to the organism's biological behaviors. Data or text describing the organisms with respects to its biological system as a whole consist of genomic information, codon usages, gene distributions along biological (metabolic and regulatory) pathways and networks, gene distributions in function categories, gene similarity among organisms, genes' generic functions, genes' metabolic functions, biological pathways involving energy-generation, and pathway substrates/products involving energy-generation. Organisms' profiles are collected from various databases such as KEGG (Kyoto Encyclopedia of Genes and Genomes), or the organisms metabolically reconstructed directly from genomic sequences, or the organisms in literature with other types of genetic information. Organisms' profiles can be data or text that best describes the organism with respect to the input substrates, output products and its biological system while the linked biological behaviors are the ability to digest the input substrates and to produce the output products given the contents of a biowaste input and a desired clean energy output are provided. The organisms are selected based on the content of the biowaste input and the desired clean energy output by first linking massive genetic and chemical fingerprints in the metabolic and energy-generating biological pathways, then applying a machine learning and pattern recognition system to assess an organism's metabolic capability to digest a required organic matter and generate clean energy. Step (2) within the machine learning and pattern recognition system uses machine learning, data mining, text mining and pattern recognition method to group the organism population into characteristic groups based on their profiles. Machine learning and pattern recognition system include supervised machine learning, unsupervised machine learning methods and pattern recognition methods for profiling, grouping and clustering organisms including neural networks, decision trees, radial basis functions, logistic regression, K-means clustering, Kohonen maps or hierarchical clustering. Step (3) within the metabolic efficiency measures are defined depending on applications, for example, average or unique number of genes in metabolic pathways and gene function categories, average codon usage frequencies with respect to a pathway product and gene similarity along the metabolic pathways with respect to a reference organism. Metabolic efficiency measure can be number of substrates consumed and products produced in the reactions involved in a fermentation process which uses a biowaste input as the feeding substrates, and the desired products will be hydrogen and fermentation end products, such as acetic acid (acetate), butyric acids (butyrate), propionic acid, ethanol, lactate, which then serve as the input substrates for the subsequent step. The metabolic efficiency measure can be number of substrates consumed and products produced in reactions, which use the fermentation end products as the feeding substrates, involving in generating the desired clean energy output. Step (3) within the profile match scores are calculated for each organism from the defined metabolic efficiency measure to see how well an organism's profile match the various the metabolic efficiency measures. Profile match scores are set higher if the organism's profile matches better with the corresponding metabolic efficiency measure, and vice versa. Step (3) within the one or a group of microorganisms is selected while the profile match scores are above certain thresholds. Step (3) within the desired capability of organisms in real life is part of the organisms' biological behaviors in step (a), for example, the abilities to utilize or digest certain substrates, to produce desired products such as hydrogen, electricity or methane. The selected microorganisms can be used to produce desired and useful recombinant proteins. Recombinant proteins are antibodies, peptides such as spider silk and human acetylcholinesterase isoforms to produce a desired pathway product, for example, high-value compounds important in pharmaceuticals and fine chemicals such as isoprenoids, succinate to explore novel properties such as glycosylation that is necessary for producing antibody to perform specific biological experiments based on research interest and need. The knowledge pattern learning and search system can be used in different applications, such as human pathogen screening based on pathogens' specific properties contributing to their pathogenic nature to human, small molecules selection for drug efficacy and toxicity, e.g. anti-cancer, anti-HIV, gene and biomarker screening for detecting biothreat agent infection, e.g. human tularemia disease, DNA aptamer selection to counter biological agents, such as detection and identification of biological threat agents. The knowledge pattern learning and search system can be used in broader applications in non-scientific fields, such as election a population of people for marketing a product, election a population of companies for investing, or selection information for business opportunities. The method is a method for selecting a microorganism or consortia to generate clean hydrogen, and clean electricity from waste-waters from food process industries, domestic, animal or meat-packing wastes. The method comprises: (a) compiling organisms' profiles by linking the massive genetic and chemical fingerprints in the metabolic and energy-generating biological pathways to the organisms' ability to undergo anaerobic fermentation by utilizing or digesting an initial the waste-water content and produce hydrogen from the process, to utilize or digest the end products from the fermentation process such as acetic acid (acetate), butyric acids (butyrate), propionic acid, ethanol, lactate in a Microbial Fuel Cell (MFC) system to generate clean electricity; (b) grouping the organism population into characteristic groups using a machine learning and pattern recognition system based on the profiles compiled in part (a); (c) selecting one or a group of microorganisms based on profile match scores calculated from the defined metabolic efficiency measures, where the metabolic efficiency measures are number of substrates consumed and products produced in the reactions involved in the fermentation process which uses the biowaste input as the feeding substrates, and the desired products are hydrogen and fermentation end products which then serve as the input substrates for the subsequent step; and (d) selecting one or a group of microorganisms based on profile match scores calculated from the defined metabolic efficiency measures, where the metabolic efficiency measures are number of substrates consumed and products produced in reactions, which use the fermentation end products as the feeding substrates, involving in generating the desired clean electricity output in a Microbial Fuel Cell (MFC) system. Microbial Fuel Cell (MFC) system comprises devices that generate current by using bacteria as the catalysts to oxidize organic or inorganic substances. EXAMPLE - No suitable example given.